Configuration for Execution Points


HTCondor Execution Points, or EP’s, are the machines where jobs run. Every Execution Point has an implied human owner who can control the policy of these machines in very fine detail. The configuration of an EP is responsible for:

  1. Dividing a single machine one or more slots, each of which can run at most one job at a time. These slots can protect the machine and other slots by limiting the amount of resources used by the job in the slot. Different slots can support different policies.

  2. Deciding when jobs run, and when they should not run. More specifically, the EP can decide which jobs run when, (and when they stop running). This is covered in section condor_startd Policy Configuration below.

  3. Partially in service of the above, the EP detects and advertises aspects about the machine. These are placed in attributes in a machine or slot Classad, and sent to the condor_collector for global querying. (See Machine ClassAd Attributes) Some of these attributes may be about the hardware, such as how much memory the machine has, or what kind of GPU it has. Other attributes may be about the software, such as what Operating System it is running. Some are predefined and automatically advertised by the system, others may be custom attributes created and defined by the administrator. These custom attributes may be statically defined (e.g. this machine is on the 3rd floor, and belongs to the Astronomy department), or they may be dynamically discovered by scripts (e.g. The temperature of the CPU is currently 40 degrees C) This is covered in section Custom and system slot attributes below.

  4. Providing an environment, and services for running jobs. These services may include the ability to run in a container or VM environment, such as Docker, Apptainer or Xen; providing the capability for the job to read or update information on the AP; and setting environment variables for the job to read.

The execution point is mainly managed by the condor_startd daemon, which itself is managed by a condor_master daemon. Each running job in a slot is then managed by an instance of the condor_starter daemon, which was spawned from the condor_startd when the job was started.

Slots: where jobs run

Each EP runs one condor_startd daemon. The HTCondor “slot” describes a set of resources (e.g. Memory, Cpus, Disk) where a job may run. Each slot is represented by its own machine ClassAd, distinguished by the machine ClassAd attribute Name, which is of the form slot<N>@hostname, or slot<M_N>@hostname. The value for <N> will also be defined with machine ClassAd attribute SlotID. Every condor_startd contains one or more slots, depending on configuration, and the hardware it runs on. There are three types of slots: Partitionable, Dynamic, and Static.

Partitionable Slots

By default, each EP starts out with one partitionable slot, which represents all the detected resources on the machine. Attributes like Memory, Disk and Cpus describe how much is available. However, no jobs run directly in Partionable slots. Rather, partitionable slots serve as a parent for Dynamic slots. Partionable slots have the attribute SlotType set to Partitionable, and PartionableSlot set to True, and are sometimes called p-slots for convenience. p-slots are named slotN@startd_name, where N is usually 1. Although possible, it is rare to have multiple p-slots on one machine.

Dynamic Slots

Dynamic slots actually run jobs. They are created dynamically, from the resources of their parent Partitionable Slot. For example, assume a partitionable slot on a machine has 3 cpu cores, 10 Gb of Memory, and 100 Gb of disk. Then, when a job which is allocated 1 cpu core, 2 Gb of Memory and 20 Gb of disk is started under that partitionable slot, the partionable slot is left with 2 cores, 8 Gb of memory and 80 Gb of disk. A new dynamic slot is created with the allocated resources. When the job exits, if the AP has another job that fits in the dynamic slot (or d-slot), the AP can reuse the d-slot for another job. At such time as it cannot reused the slot the d-slot is destroyed, and the resources allocated to it are returned to the parent p-slot. Depending on the configuration, the privilege level of HTCondor, and the OS, these slot may or may not enforce the resources limits they have allocated. dslots are named slotN_M@startd_name, where N is the number of the parent partitionable slot (often “1”). Dynamic slots have the attribute DynamicSlot set to True, and the attribute SlotType set to Dynamic.

Static Slots

Jobs run in static slots, in much they same way they do for dynamic slots. However, the number of static slots in a condor_startd, and their size is fixed by configuration at boot time of the condor_startd, and cannot be changed without restarting the condor_startd. By default, no static slots are created. Static slots are named slotN@startd_name, where N starts at 1 and continues to the number of static slots. The configuration setting use FEATURE : StaticSlots will configure a startd to advertise zero partitionable slots, and one static slot per detected core, with 1 cpu core in each slot, and each slot evenly dividing the detected memory and execution disk space. However, an administrator can configure the various static slots to have any amount of resources. See following section for details.

Dividing System Resources in Multi-core Machines

Within a machine the shared system resources of cores, RAM, swap space and disk space will be divided for use by the slots. There are two main ways to go about dividing the resources of a multi-core machine:

Evenly divide all resources.

Prior to HTCondor 23.0 the condor_startd will automatically divide the machine into multiple slots by default, placing one core in each slot, and evenly dividing all shared resources among the slots. Beginning with HTCondor 23.0 the condor_startd will create a single partitionable slot by default.

In HTCondor 23.0 you can use the configuration template use FEATURE : StaticSlots to configure a number of static slots. If used without arguments this configuration template will define a number of single core static slots equal to the number of detected cpu cores.

To simply configure static slots in any version, configure NUM_SLOTS to the integer number of slots desired. NUM_SLOTS may not be used to make HTCondor advertise more slots than there are cores on the machine. The number of cores is defined by NUM_CPUS.

Define slot types.

Instead of the default slot configuration, the machine may have definitions of slot types, where each type is provided with a fraction of shared system resources. Given the slot type definition, control how many of each type are reported at any given time with further configuration.

Configuration variables define the slot types, as well as variables that list how much of each system resource goes to each slot type.

Configuration variable SLOT_TYPE_<N>, where <N> is an integer (for example, SLOT_TYPE_1) defines the slot type. Note that there may be multiple slots of each type. The number of slots created of a given type is configured with NUM_SLOTS_TYPE_<N>.

The resources configured for the slot type can be defined by:

  • A simple fraction, such as 1/4

  • A simple percentage, such as 25%

  • A comma-separated list of attributes, with a percentage, fraction, numerical value, or auto for each one.

  • A comma-separated list that includes a blanket value that serves as a default for any resources not explicitly specified in the list.

A simple fraction or percentage describes the allocation of the total system resources, including the number of CPUS or cores. A comma separated list allows a fine tuning of the amounts for specific resources.

The number of CPUs and the total amount of RAM in the machine do not change over time. For these attributes, specify either absolute values or percentages of the total available amount (or auto). For example, in a machine with 128 Mbytes of RAM, all the following definitions result in the same allocation amount.

SLOT_TYPE_1 = mem=64

SLOT_TYPE_1 = mem=1/2

SLOT_TYPE_1 = mem=50%

SLOT_TYPE_1 = mem=auto

Amounts of disk space and swap space are dynamic, as they change over time. For these, specify a percentage or fraction of the total value that is allocated to each slot, instead of specifying absolute values. As the total values of these resources change on the machine, each slot will take its fraction of the total and report that as its available amount.

The disk space allocated to each slot is taken from the disk partition containing the slot’s EXECUTE or SLOT<N>_EXECUTE directory. If every slot is in a different partition, then each one may be defined with up to 100% for its disk share. If some slots are in the same partition, then their total is not allowed to exceed 100%.

The four predefined attribute names are case insensitive when defining slot types. The first letter of the attribute name distinguishes between these attributes. The four attributes, with several examples of acceptable names for each:

  • Cpus, C, c, cpu

  • ram, RAM, MEMORY, memory, Mem, R, r, M, m

  • disk, Disk, D, d

  • swap, SWAP, S, s, VirtualMemory, V, v

As an example, consider a machine with 4 cores and 256 Mbytes of RAM. Here are valid example slot type definitions. Types 1-3 are all equivalent to each other, as are types 4-6. Note that in a real configuration, all of these slot types would not be used together, because they add up to more than 100% of the various system resources. This configuration example also omits definitions of NUM_SLOTS_TYPE_<N>, to define the number of each slot type.

SLOT_TYPE_1 = cpus=2, ram=128, swap=25%, disk=1/2

SLOT_TYPE_2 = cpus=1/2, memory=128, virt=25%, disk=50%

SLOT_TYPE_3 = c=1/2, m=50%, v=1/4, disk=1/2

SLOT_TYPE_4 = c=25%, m=64, v=1/4, d=25%

SLOT_TYPE_5 = 25%

SLOT_TYPE_6 = 1/4

The default value for each resource share is auto. The share may also be explicitly set to auto. All slots with the value auto for a given type of resource will evenly divide whatever remains, after subtracting out explicitly allocated resources given in other slot definitions. For example, if one slot is defined to use 10% of the memory and the rest define it as auto (or leave it undefined), then the rest of the slots will evenly divide 90% of the memory between themselves.

In both of the following examples, the disk share is set to auto, number of cores is 1, and everything else is 50%:

SLOT_TYPE_1 = cpus=1, ram=1/2, swap=50%

SLOT_TYPE_1 = cpus=1, disk=auto, 50%

Note that it is possible to set the configuration variables such that they specify an impossible configuration. If this occurs, the condor_startd daemon fails after writing a message to its log attempting to indicate the configuration requirements that it could not implement.

In addition to the standard resources of CPUs, memory, disk, and swap, the administrator may also define custom resources on a localized per-machine basis. In addition to GPUs (see Configuring GPUs.) the administrator can define other types of custom resources.

The resource names and quantities of available resources are defined using configuration variables of the form MACHINE_RESOURCE_<name>, as shown in this example:


If the configuration uses the optional configuration variable MACHINE_RESOURCE_NAMES to enable and disable local machine resources, also add the resource names to this variable. For example:


Local machine resource names defined in this way may now be used in conjunction with SLOT_TYPE_<N>, using all the same syntax described earlier in this section. The following example demonstrates the definition of static and partitionable slot types with local machine resources:

# declare one partitionable slot with half of the Cogs, 6 actuators, and
# 50% of all other resources:
SLOT_TYPE_1 = cogs=50%,actuator=6,50%

# declare two static slots, each with 25% of the Cogs, 1 actuator, and
# 25% of all other resources:
SLOT_TYPE_2 = cogs=25%,actuator=1,25%

A job may request these local machine resources using the syntax request_<name> as described in condor_startd Policy Configuration. This example shows a portion of a submit description file that requests cogs and an actuator:

universe = vanilla

# request two cogs and one actuator:
request_cogs = 2
request_actuator = 1


The slot ClassAd will represent each local machine resource with the following attributes:

Total<name>: the total quantity of the resource identified by <name> Detected<name>: the quantity detected of the resource identified by <name>; this attribute is currently equivalent to Total<name> TotalSlot<name>: the quantity of the resource identified by <name> allocated to this slot <name>: the amount of the resource identified by <name> available to be used on this slot

From the example given, the Cogs resource would be represented by the ClassAd attributes TotalCogs, DetectedCogs, TotalSlotCogs, and Cogs. In the job ClassAd, the amount of the requested machine resource appears in a job ClassAd attribute named Request<name>. For this example, the two attributes will be RequestCogs and RequestActuator.

The number of each type and the definitions for the types themselves cannot be changed with reconfiguration. To change any slot type definitions, use condor_restart

$ condor_restart -startd

for that change to take effect.

Configuration Specific to Multi-core Machines

Each slot within a multi-core machine is treated as an independent machine, each with its own view of its state as represented by the machine ClassAd attribute State. The policy expressions for the multi-core machine as a whole are propagated from the condor_startd to the slot’s machine ClassAd. This policy may consider a slot state(s) in its expressions.

Specifically, the evaluation of a slot policy expression works in the following way.

  1. The configuration file specifies policy expressions that are shared by all of the slots on the machine.

  2. Each slot reads the configuration file and sets up its own machine ClassAd.

  3. Each slot is now separate from the others. It has a different ClassAd attribute State, a different machine ClassAd, and if there is a job running, a separate job ClassAd. Each slot periodically evaluates the policy expressions, changing its own state as necessary. This occurs independently of the other slots on the machine. So, if the condor_startd daemon is evaluating a policy expression on a specific slot, and the policy expression refers to ProcId, Owner, or any attribute from a job ClassAd, it always refers to the ClassAd of the job running on the specific slot.

To set a different policy for the slots within a machine, incorporate the slot-specific machine ClassAd attribute SlotID. A SUSPEND policy that is different for each of the two slots will be of the form

SUSPEND = ( (SlotID == 1) && (PolicyForSlot1) ) || \
          ( (SlotID == 2) && (PolicyForSlot2) )

where (PolicyForSlot1) and (PolicyForSlot2) are the desired expressions for each slot.

Dynamic Provisioning: Partitionable and Dynamic Slots

Dynamic provisioning, also referred to as partitionable or dynamic slots, allows HTCondor to use the resources of a slot in a dynamic way; these slots may be partitioned. This means that more than one job can occupy a single slot at any one time. Slots have a fixed set of resources which include the cores, memory and disk space. By partitioning the slot, the use of these resources becomes more flexible.

Here is an example that demonstrates how resources are divided as more than one job is or can be matched to a single slot. In this example, Slot1 is identified as a partitionable slot and has the following resources:

cpu = 10
memory = 10240
disk = BIG

Assume that JobA is allocated to this slot. JobA includes the following resource requests:

request_cpu = 3
request_memory = 1024
request_disk = 10240

The portion of the slot that is carved out is now known as a dynamic slot. This dynamic slot has its own machine ClassAd, and its Name attribute distinguishes itself as a dynamic slot with incorporating the substring Slot1_1. Note that the startd may round up the resource requests, so that subsequent jobs may also match this slot, but it will never run a job in a slot that won’t fit the job.

After allocation, the partitionable Slot1 advertises that it has the following resources still available, which might look like:

cpu = 7
memory = 9216
disk = BIG-10240

As each new job is allocated to Slot1, it breaks into Slot1_1, Slot1_2, Slot1_3 etc., until the entire set of Slot1’s available resources have been consumed by jobs.

Dynamic provisioning is enabled by default, as of HTCondor version 23.0 In older versions, to enable dynamic provisioning, define a slot type, and declare at least one slot of that type. Then, identify that slot type as partitionable by setting configuration variable SLOT_TYPE_<N>_PARTITIONABLE to True. The value of <N> within the configuration variable name is the same value as in slot type definition configuration variable SLOT_TYPE_<N>. For the most common cases the machine should be configured for one slot, managing all the resources on the machine. To do so, set the following configuration variables:

SLOT_TYPE_1 = 100%

In a pool using dynamic provisioning, jobs must express resources they need in the submit description file:

request_disk (in kilobytes)

This example shows a portion of the job submit description file for use when submitting a job to a pool with dynamic provisioning.

universe = vanilla

request_cpus = 3
request_memory = 1024
request_disk = 10240


A partitionable slot will always appear as though it is not running a job. If matched jobs consume all its resources, the partitionable slot will eventually show as having no available resources; this will prevent further matching of new jobs. The dynamic slots will show as running jobs. The dynamic slots can be preempted in the same way as all other slots.

Dynamic provisioning provides powerful configuration possibilities, and so should be used with care. Specifically, while preemption occurs for each individual dynamic slot, it cannot occur directly for the partitionable slot, or for groups of dynamic slots. For example, for a large number of jobs requiring 1GB of memory, a pool might be split up into 1GB dynamic slots. In this instance a job requiring 2GB of memory will be starved and unable to run. A partial solution to this problem is provided by defragmentation accomplished by the condor_defrag daemon, as discussed in condor_startd Policy Configuration.

Preemption of Partionable Slots


Partionable slot preemption is an experimental feature, and may not work as expected with all other HTCondor features.

Another partial solution is a new matchmaking algorithm in the negotiator, referred to as partitionable slot preemption, or pslot preemption. Without pslot preemption, when the negotiator searches for a match for a job, it looks at each slot ClassAd individually. With pslot preemption, the negotiator looks at a partitionable slot and all of its dynamic slots as a group. If the partitionable slot does not have sufficient resources (memory, cpu, and disk) to be matched with the candidate job, then the negotiator looks at all of the related dynamic slots that the candidate job might preempt (following the normal preemption rules described elsewhere). The resources of each dynamic slot are added to those of the partitionable slot, one dynamic slot at a time. Once this partial sum of resources is sufficient to enable a match, the negotiator sends the match information to the condor_schedd. When the condor_schedd claims the partitionable slot, the dynamic slots are preempted, such that their resources are returned to the partitionable slot for use by the new job.

To enable pslot preemption, the following configuration variable must be set for the condor_negotiator:


When the negotiator examines the resources of dynamic slots, it sorts the slots by their CurrentRank attribute, such that slots with lower values are considered first. The negotiator only examines the cpu, memory and disk resources of the dynamic slots; custom resources are ignored.

Dynamic slots that have retirement time remaining are not considered eligible for preemption, regardless of how configuration variable NEGOTIATOR_CONSIDER_EARLY_PREEMPTION is set.

When pslot preemption is enabled, the negotiator will not preempt dynamic slots directly. It will preempt them only as part of a match to a partitionable slot.

When multiple partitionable slots match a candidate job and the various job rank expressions are evaluated to sort the matching slots, the ClassAd of the partitionable slot is used for evaluation. This may cause unexpected results for some expressions, as attributes such as RemoteOwner will not be present in a partitionable slot that matches with preemption of some of its dynamic slots.

Defaults for Partitionable Slot Sizes

If a job does not specify the required number of CPUs, amount of memory, or disk space, there are ways for the administrator to set default values for all of these parameters.

First, if any of these attributes are not set in the submit description file, there are three variables in the configuration file that condor_submit will use to fill in default values. These are

The value of these variables can be ClassAd expressions. The default values for these variables, should they not be set are

    ifThenElse(MemoryUsage =!= UNDEFINED, MemoryUsage, 1)

Note that these default values are chosen such that jobs matched to partitionable slots function similar to static slots. These variables do not apply to batch grid universe jobs.

Once the job has been matched, and has made it to the execute machine, the condor_startd has the ability to modify these resource requests before using them to size the actual dynamic slots carved out of the partitionable slot. Clearly, for the job to work, the condor_startd daemon must create slots with at least as many resources as the job needs. However, it may be valuable to create dynamic slots somewhat bigger than the job’s request, as subsequent jobs may be more likely to reuse the newly created slot when the initial job is done using it.

The condor_startd configuration variables which control this and their defaults are

Slot Isolation and Protection

When multiple jobs, one in each slots, are running on the same machine, one job might negatively impact another. This might happen by using too much cpu, or disk, or even sending a signal to a process in another job. HTCondor provides several mechanism to protect jobs in slots from each other.

Per Job PID Namespaces

Per job PID namespaces provide enhanced isolation of one process tree from another through kernel level process ID namespaces. HTCondor may enable the use of per job PID namespaces for Linux RHEL 6, Debian 6, and more recent kernels.

Read about per job PID namespaces

The needed isolation of jobs from the same user that execute on the same machine as each other is already provided by the implementation of slot users as described in User Accounts in HTCondor on Unix Platforms. This is the recommended way to implement the prevention of interference between more than one job submitted by a single user. However, the use of a shared file system by slot users presents issues in the ownership of files written by the jobs.

The per job PID namespace provides a way to handle the ownership of files produced by jobs within a shared file system. It also isolates the processes of a job within its PID namespace. As a side effect and benefit, the clean up of processes for a job within a PID namespace is enhanced. When the process with PID = 1 is killed, the operating system takes care of killing all child processes.

To enable the use of per job PID namespaces, set the configuration to include


This configuration variable defaults to False, thus the use of per job PID namespaces is disabled by default.

Group ID-Based Process Tracking

One function that HTCondor often must perform is keeping track of all processes created by a job. This is done so that HTCondor can provide resource usage statistics about jobs, and also so that HTCondor can properly clean up any processes that jobs leave behind when they exit.


Group ID based process tracking has generally been replaced by cgroup based tracking, which is more powerful and more general, and requires less setup. Group ID based process tracking may be removed from HTCondor in the future.

In general, tracking process families is difficult to do reliably. By default HTCondor uses a combination of process parent-child relationships, process groups, and information that HTCondor places in a job’s environment to track process families on a best-effort basis. This usually works well, but it can falter for certain applications or for jobs that try to evade detection.

Jobs that run with a user account dedicated for HTCondor’s use can be reliably tracked, since all HTCondor needs to do is look for all processes running using the given account. Administrators must specify in HTCondor’s configuration what accounts can be considered dedicated via the DEDICATED_EXECUTE_ACCOUNT_REGEXP setting. See User Accounts in HTCondor on Unix Platforms for further details.

Ideally, jobs can be reliably tracked regardless of the user account they execute under. This can be accomplished with group ID-based tracking. This method of tracking requires that a range of dedicated group IDs (GID) be set aside for HTCondor’s use. The number of GIDs that must be set aside for an execute machine is equal to its number of execution slots. GID-based tracking is only available on Linux, and it requires that HTCondor daemons run as root.

GID-based tracking works by placing a dedicated GID in the supplementary group list of a job’s initial process. Since modifying the supplementary group ID list requires root privilege, the job will not be able to create processes that go unnoticed by HTCondor.

Once a suitable GID range has been set aside for process tracking, GID-based tracking can be enabled via the USE_GID_PROCESS_TRACKING parameter. The minimum and maximum GIDs included in the range are specified with the MIN_TRACKING_GID and MAX_TRACKING_GID settings. For example, the following would enable GID-based tracking for an execute machine with 8 slots.


If the defined range is too small, such that there is not a GID available when starting a job, then the condor_starter will fail as it tries to start the job. An error message will be logged stating that there are no more tracking GIDs.

GID-based process tracking requires use of the condor_procd. If USE_GID_PROCESS_TRACKING is true, the condor_procd will be used regardless of the USE_PROCD setting. Changes to MIN_TRACKING_GID and MAX_TRACKING_GID require a full restart of HTCondor.

Cgroup-Based Process Tracking

A new feature in Linux version 2.6.24 allows HTCondor to more accurately and safely manage jobs composed of sets of processes. This Linux feature is called Control Groups, or cgroups for short, and it is available starting with RHEL 6, Debian 6, and related distributions. Documentation about Linux kernel support for cgroups can be found in the Documentation directory in the kernel source code distribution. Another good reference is

The interface between the kernel cgroup functionality is via a (virtual) file system, usually mounted at /sys/fs/cgroup.

If your Linux distribution uses systemd, it will mount the cgroup file system, and the only remaining item is to set configuration variable BASE_CGROUP, as described below.

When cgroups are correctly configured and running, the virtual file system mounted on /sys/fs/cgroup should have several subdirectories under it, and there should an htcondor subdirectory under the directory /sys/fs/cgroup/cpu, /sys/fs/cgroup/memory and some others.

The condor_starter daemon uses cgroups by default on Linux systems to accurately track all the processes started by a job, even when quickly-exiting parent processes spawn many child processes. As with the GID-based tracking, this is only implemented when a condor_procd daemon is running.

Kernel cgroups are named in a virtual file system hierarchy. HTCondor will put each running job on the execute node in a distinct cgroup. The name of this cgroup is the name of the execute directory for that condor_starter, with slashes replaced by underscores, followed by the name and number of the slot. So, for the memory controller, a job running on slot1 would have its cgroup located at /sys/fs/cgroup/memory/htcondor/condor_var_lib_condor_execute_slot1/. The tasks file in this directory will contain a list of all the processes in this cgroup, and many other files in this directory have useful information about resource usage of this cgroup. See the kernel documentation for full details.

Once cgroup-based tracking is configured, usage should be invisible to the user and administrator. The condor_procd log, as defined by configuration variable PROCD_LOG, will mention that it is using this method, but no user visible changes should occur, other than the impossibility of a quickly-forking process escaping from the control of the condor_starter, and the more accurate reporting of memory usage.

A cgroup-enabled HTCondor will install and handle a per-job (not per-process) Linux Out of Memory killer (OOM-Killer). When a job exceeds the memory provisioned by the condor_startd, the Linux kernel will send an OOM message to the condor_starter, and HTCondor will evict the job, and put it on hold. Sometimes, even when the job’s memory usage is below the provisioned amount, if other, non-HTCondor processes, on the system are using too much memory, the linux kernel may choose to OOM-kill the job. In this case, HTCondor will log a message and evict the job, mark it as idle, so it can start again somewhere else.

Limiting Resource Usage Using Cgroups

While the method described to limit a job’s resource usage is portable, and it should run on any Linux or BSD or Unix system, it suffers from one large flaw. The flaw is that resource limits imposed are per process, not per job. An HTCondor job is often composed of many Unix processes. If the method of limiting resource usage with a user job wrapper is used to impose a 2 Gigabyte memory limit, that limit applies to each process in the job individually. If a job created 100 processes, each using just under 2 Gigabytes, the job would continue without the resource limits kicking in. Clearly, this is not what the machine owner intends. Moreover, the memory limit only applies to the virtual memory size, not the physical memory size, or the resident set size. This can be a problem for jobs that use the mmap system call to map in a large chunk of virtual memory, but only need a small amount of memory at one time. Typically, the resource the administrator would like to control is physical memory, because when that is in short supply, the machine starts paging, and can become unresponsive very quickly.

The condor_starter can, using the Linux cgroup capability, apply resource limits collectively to sets of jobs, and apply limits to the physical memory used by a set of processes. The main downside of this technique is that it is only available on relatively new Unix distributions such as RHEL 6 and Debian 6. This technique also may require editing of system configuration files.

To enable cgroup-based limits, first ensure that cgroup-based tracking is enabled, as it is by default on supported systems, as described in section 3.14.13. Once set, the condor_starter will create a cgroup for each job, and set attributes in that cgroup to control memory and cpu usage. These attributes are the cpu.shares attribute in the cpu controller, and two attributes in the memory controller, both memory.limit_in_bytes, and memory.soft_limit_in_bytes. The configuration variable CGROUP_MEMORY_LIMIT_POLICY controls this. If CGROUP_MEMORY_LIMIT_POLICY is set to the string hard, the hard limit will be set to the slot size, and the soft limit to 90% of the slot size. If set to soft, the soft limit will be set to the slot size and the hard limit will be set to the memory size of the whole startd. By default, this whole size is the detected memory the size, minus RESERVED_MEMORY. Or, if MEMORY is defined, that value is used..

No limits will be set if the value is none. The default is none. If the hard limit is in force, then the total amount of physical memory used by the sum of all processes in this job will not be allowed to exceed the limit. If the process goes above the hard limit, the job will be put on hold.

The memory size used in both cases is the machine ClassAd attribute Memory. Note that Memory is a static amount when using static slots, but it is dynamic when partitionable slots are used. That is, the limit is whatever the “Mem” column of condor_status reports for that slot.

If CGROUP_MEMORY_LIMIT_POLICY is set, HTCondor will also use cgroups to limit the amount of swap space used by each job. By default, the maximum amount of swap space used by each slot is the total amount of Virtual Memory in the slot, minus the amount of physical memory. Note that HTCondor measures virtual memory in kbytes, and physical memory in megabytes. To prevent jobs with high memory usage from thrashing and excessive paging, and force HTCondor to put them on hold instead, you can tell condor that a job should never use swap, by setting DISABLE_SWAP_FOR_JOB to true (the default is false).

In addition to memory, the condor_starter can also control the total amount of CPU used by all processes within a job. To do this, it writes a value to the cpu.shares attribute of the cgroup cpu controller. The value it writes is copied from the Cpus attribute of the machine slot ClassAd multiplied by 100. Again, like the Memory attribute, this value is fixed for static slots, but dynamic under partitionable slots. This tells the operating system to assign cpu usage proportionally to the number of cpus in the slot. Unlike memory, there is no concept of soft or hard, so this limit only applies when there is contention for the cpu. That is, on an eight core machine, with only a single, one-core slot running, and otherwise idle, the job running in the one slot could consume all eight cpus concurrently with this limit in play, if it is the only thing running. If, however, all eight slots where running jobs, with each configured for one cpu, the cpu usage would be assigned equally to each job, regardless of the number of processes or threads in each job.

Per Job Ephemeral Scratch Filesystems

On Linux systems, when HTCondor is started as root, it optionally has the ability to create a custom ephemeral filesystem for the job’s scratch directory. HTCondor manages this per scratch directory filesystem usage with the LVM disk management system. This feature has the following benefits:

  • Disk usage is more accurately monitored and enforced preventing the job from using more scratch space than provisioned.

  • HTCondor can get the current disk usage much quicker.

  • Creates more isolation for the jobs workspace.

  • HTCondor can cleanup the jobs workspace much quicker..

This feature will enable better handling of jobs that utilize more than the disk space than provisioned by HTCondor. With the feature enabled, when a job fills up the filesystem created for it, the starter will put the job on hold with the out of resources hold code (34). Otherwise, in a full filesystem, writes will fail with ENOSPC, and leave it up to the job to handle these errors internally at all places writes occur. Even in included third party libraries.


The ephemeral filesystem created for the job is private to that job so the contents of the filesystem are not visible outside the process hierarchy. The nsenter command can be used to enter this namespace in order inspect the job’s sandbox.


As this filesystem will never live through a system reboot, it is mounted with mount options that optimize for performance, not reliability, and may improve performance for I/O heavy jobs.

To enable per job scratch directory filesystems, simply set STARTD_ENFORCE_DISK_LIMITS to True. However, it is recommended to also setup a functioning Linux LVM environment for HTCondor to utilize by providing the LVM volume group and possibly thin-pool type logical volume via the configuration options LVM_VOLUME_GROUP_NAME and LVM_THINPOOL_NAME. If a pre-setup Linux LVM environment isn’t provided then the condor_startd will automatically setup a Linux LVM environment using a backing loopback file specified by LVM_BACKING_FILE.

flowchart TD Disk1(Disk 1) --> PV1(Physical Volume 1) Disk2(Disk 2) --> PV2(Physical Volume 2) PV1 --> VG(Volume Group\n<b style="color:red">condor_vg</b>) PV2 --> VG(Volume Group\n<b style="color:red">condor_vg</b>) VG -- Thin --> Thinpool(Thinpool Logical Volume\n<b style="color:red">htcondor</b>) VG -- Thick --> Done(HTCondor Ready) Thinpool --> Done(HTCondor Ready)

Linux LVM Environment Setup


The condor_startd must be restarted rather than just reconfigured in order for any LVM Startd disk enforcement configuration to take effect.


When setup to use thin provisioning, if the backing thin pool logical volume fills up completely then all writes to subsequent thin logical volumes carved from the thin pool with pause for 60 seconds. If desired this behavior can be disabled by using --errorwhenfull y option when creating the backing thin-pool type logical volume.

condor_startd Policy Configuration

This section describes the configuration of machines, via the condor_startd daemon, and policies to start, suspend, resume, vacate or to kill jobs. These polices are at the heart of HTCondor’s balancing act between the needs and wishes of resource owners (machine owners) and resource users (people submitting their jobs to HTCondor). Understanding the configuration requires an understanding of ClassAd expressions, which are detailed in the HTCondor’s ClassAd Mechanism section.

The START Expression

The most important expression to the condor_startd is the START expression. This expression describes the conditions that must be met for a machine or slot to run a job. This expression can reference attributes in the machine’s ClassAd (such as KeyboardIdle and LoadAvg) and attributes in a job ClassAd (such as Owner, ImageSize, and Cmd, the name of the executable the job will run). The value of the START expression plays a crucial role in determining the state and activity of a machine.


Configuration templates make it easier to implement certain policies; see information on policy templates here: Available Configuration Templates.

The Requirements expression is used for matching machines with jobs.

In situations where a machine wants to make itself unavailable for further matches, the Requirements expression is set to False. When the START expression locally evaluates to True, the machine advertises the Requirements expression as True and does not publish the START expression.

Normally, the expressions in the machine ClassAd are evaluated against certain request ClassAds in the condor_negotiator to see if there is a match, or against whatever request ClassAd currently has claimed the machine. However, by locally evaluating an expression, the machine only evaluates the expression against its own ClassAd. If an expression cannot be locally evaluated (because it references other expressions that are only found in a request ClassAd, such as Owner or ImageSize), the expression is (usually) undefined. See the HTCondor’s ClassAd Mechanism section for specifics on how undefined terms are handled in ClassAd expression evaluation.

A note of caution is in order when modifying the START expression to reference job ClassAd attributes. When using the POLICY : Desktop configuration template, the IS_OWNER expression is a function of the START expression:


See a detailed discussion of the IS_OWNER expression in condor_startd Policy Configuration. However, the machine locally evaluates the IS_OWNER expression to determine if it is capable of running jobs for HTCondor. Any job ClassAd attributes appearing in the START expression, and hence in the IS_OWNER expression, are undefined in this context, and may lead to unexpected behavior. Whenever the START expression is modified to reference job ClassAd attributes, the IS_OWNER expression should also be modified to reference only machine ClassAd attributes.


If you have machines with lots of real memory and swap space such that the only scarce resource is CPU time, consider defining JOB_RENICE_INCREMENT so that HTCondor starts jobs on the machine with low priority. Then, further configure to set up the machines with:

START = True
KILL = False

In this way, HTCondor jobs always run and can never be kicked off from activity on the machine. However, because they would run with the low priority, interactive response on the machines will not suffer. A machine user probably would not notice that HTCondor was running the jobs, assuming you had enough free memory for the HTCondor jobs such that there was little swapping.

The RANK Expression

A machine may be configured to prefer certain jobs over others using the RANK expression. It is an expression, like any other in a machine ClassAd. It can reference any attribute found in either the machine ClassAd or a job ClassAd. The most common use of this expression is likely to configure a machine to prefer to run jobs from the owner of that machine, or by extension, a group of machines to prefer jobs from the owners of those machines.

For example, imagine there is a small research group with 4 machines called tenorsax, piano, bass, and drums. These machines are owned by the 4 users coltrane, tyner, garrison, and jones, respectively.

Assume that there is a large HTCondor pool in the department, and this small research group has spent a lot of money on really fast machines for the group. As part of the larger pool, but to implement a policy that gives priority on the fast machines to anyone in the small research group, set the RANK expression on the machines to reference the Owner attribute and prefer requests where that attribute matches one of the people in the group as in

RANK = Owner == "coltrane" || Owner == "tyner" \
    || Owner == "garrison" || Owner == "jones"

The RANK expression is evaluated as a floating point number. However, like in C, boolean expressions evaluate to either 1 or 0 depending on if they are True or False. So, if this expression evaluated to 1, because the remote job was owned by one of the preferred users, it would be a larger value than any other user for whom the expression would evaluate to 0.

A more complex RANK expression has the same basic set up, where anyone from the group has priority on their fast machines. Its difference is that the machine owner has better priority on their own machine. To set this up for Garrison’s machine (bass), place the following entry in the local configuration file of machine bass:

RANK = (Owner == "coltrane") + (Owner == "tyner") \
    + ((Owner == "garrison") * 10) + (Owner == "jones")

Note that the parentheses in this expression are important, because the + operator has higher default precedence than ==.

The use of + instead of || allows us to distinguish which terms matched and which ones did not. If anyone not in the research group quartet was running a job on the machine called bass, the RANK would evaluate numerically to 0, since none of the boolean terms evaluates to 1, and 0+0+0+0 still equals 0.

Suppose Elvin Jones submits a job. His job would match the bass machine, assuming START evaluated to True for him at that time. The RANK would numerically evaluate to 1. Therefore, the Elvin Jones job could preempt the HTCondor job currently running. Further assume that later Jimmy Garrison submits a job. The RANK evaluates to 10 on machine bass, since the boolean that matches gets multiplied by 10. Due to this, Jimmy Garrison’s job could preempt Elvin Jones’ job on the bass machine where Jimmy Garrison’s jobs are preferred.

The RANK expression is not required to reference the Owner of the jobs. Perhaps there is one machine with an enormous amount of memory, and others with not much at all. Perhaps configure this large-memory machine to prefer to run jobs with larger memory requirements:

RANK = ImageSize

That’s all there is to it. The bigger the job, the more this machine wants to run it. It is an altruistic preference, always servicing the largest of jobs, no matter who submitted them. A little less altruistic is the RANK on Coltrane’s machine that prefers John Coltrane’s jobs over those with the largest ImageSize:

RANK = (Owner == "coltrane" * 1000000000000) + Imagesize

This RANK does not work if a job is submitted with an image size of more 1012 Kbytes. However, with that size, this RANK expression preferring that job would not be HTCondor’s only problem!

Machine States

A machine is assigned a state by HTCondor. The state depends on whether or not the machine is available to run HTCondor jobs, and if so, what point in the negotiations has been reached. The possible states are


The machine is being used by the machine owner, and/or is not available to run HTCondor jobs. When the machine first starts up, it begins in this state.


The machine is available to run HTCondor jobs, but it is not currently doing so.


The machine is available to run jobs, and it has been matched by the negotiator with a specific schedd. That schedd just has not yet claimed this machine. In this state, the machine is unavailable for further matches.


The machine has been claimed by a schedd.


The machine was claimed by a schedd, but is now preempting that claim for one of the following reasons.

  1. the owner of the machine came back

  2. another user with higher priority has jobs waiting to run

  3. another request that this resource would rather serve was found


The machine is running a backfill computation while waiting for either the machine owner to come back or to be matched with an HTCondor job. This state is only entered if the machine is specifically configured to enable backfill jobs.


The machine is not running jobs, because it is being drained. One reason a machine may be drained is to consolidate resources that have been divided in a partitionable slot. Consolidating the resources gives large jobs a chance to run.

stateDiagram-v2 direction LR [*]--> Owner Owner --> Unclaimed: A Unclaimed --> Matched: C Unclaimed --> Owner: B Unclaimed --> Drained: P Unclaimed --> Backfill: E Unclaimed --> Claimed: D Backfill --> Owner: K Backfill --> Matched: L Backfill --> Claimed: M Matched --> Claimed: G Matched --> Owner: F Claimed --> Preempting: H Preempting --> Owner: J Preempting --> Claimed: I Owner --> Drained: N Drained --> Owner: O

Machine states and the possible transitions between the states

Each transition is labeled with a letter. The cause of each transition is described below.

  • Transitions out of the Owner state


    The machine switches from Owner to Unclaimed whenever the START expression no longer locally evaluates to FALSE. This indicates that the machine is potentially available to run an HTCondor job.


    The machine switches from the Owner to the Drained state whenever draining of the machine is initiated, for example by condor_drain or by the condor_defrag daemon.

  • Transitions out of the Unclaimed state


    The machine switches from Unclaimed back to Owner whenever the START expression locally evaluates to FALSE. This indicates that the machine is unavailable to run an HTCondor job and is in use by the resource owner.


    The transition from Unclaimed to Matched happens whenever the condor_negotiator matches this resource with an HTCondor job.


    The transition from Unclaimed directly to Claimed also happens if the condor_negotiator matches this resource with an HTCondor job. In this case the condor_schedd receives the match and initiates the claiming protocol with the machine before the condor_startd receives the match notification from the condor_negotiator.


    The transition from Unclaimed to Backfill happens if the machine is configured to run backfill computations (see the Configuring HTCondor for Running Backfill Jobs section) and the START_BACKFILL expression evaluates to TRUE.


    The transition from Unclaimed to Drained happens if draining of the machine is initiated, for example by condor_drain or by the condor_defrag daemon.

  • Transitions out of the Matched state


    The machine moves from Matched to Owner if either the START expression locally evaluates to FALSE, or if the MATCH_TIMEOUT timer expires. This timeout is used to ensure that if a machine is matched with a given condor_schedd, but that condor_schedd does not contact the condor_startd to claim it, that the machine will give up on the match and become available to be matched again. In this case, since the START expression does not locally evaluate to FALSE, as soon as transition F is complete, the machine will immediately enter the Unclaimed state again (via transition A). The machine might also go from Matched to Owner if the condor_schedd attempts to perform the claiming protocol but encounters some sort of error. Finally, the machine will move into the Owner state if the condor_startd receives a condor_vacate command while it is in the Matched state.


    The transition from Matched to Claimed occurs when the condor_schedd successfully completes the claiming protocol with the condor_startd.

  • Transitions out of the Claimed state


    From the Claimed state, the only possible destination is the Preempting state. This transition can be caused by many reasons:

    • The condor_schedd that has claimed the machine has no more work to perform and releases the claim

    • The PREEMPT expression evaluates to True (which usually means the resource owner has started using the machine again and is now using the keyboard, mouse, CPU, etc.)

    • The condor_startd receives a condor_vacate command

    • The condor_startd is told to shutdown (either via a signal or a condor_off command)

    • The resource is matched to a job with a better priority (either a better user priority, or one where the machine rank is higher)

  • Transitions out of the Preempting state


    The resource will move from Preempting back to Claimed if the resource was matched to a job with a better priority.


    The resource will move from Preempting to Owner if the PREEMPT expression had evaluated to TRUE, if condor_vacate was used, or if the START expression locally evaluates to FALSE when the condor_startd has finished evicting whatever job it was running when it entered the Preempting state.

  • Transitions out of the Backfill state


    The resource will move from Backfill to Owner for the following reasons:

    • The EVICT_BACKFILL expression evaluates to TRUE

    • The condor_startd receives a condor_vacate command

    • The condor_startd is being shutdown


    The transition from Backfill to Matched occurs whenever a resource running a backfill computation is matched with a condor_schedd that wants to run an HTCondor job.


    The transition from Backfill directly to Claimed is similar to the transition from Unclaimed directly to Claimed. It only occurs if the condor_schedd completes the claiming protocol before the condor_startd receives the match notification from the condor_negotiator.

  • Transitions out of the Drained state


    The transition from Drained to Owner state happens when draining is finalized or is canceled. When a draining request is made, the request either asks for the machine to stay in a Drained state until canceled, or it asks for draining to be automatically finalized once all slots have finished draining.

The Claimed State and Leases

When a condor_schedd claims a condor_startd, there is a claim lease. So long as the keep alive updates from the condor_schedd to the condor_startd continue to arrive, the lease is reset. If the lease duration passes with no updates, the condor_startd drops the claim and evicts any jobs the condor_schedd sent over.

The alive interval is the amount of time between, or the frequency at which the condor_schedd sends keep alive updates to all condor_schedd daemons. An alive update resets the claim lease at the condor_startd. Updates are UDP packets.

Initially, as when the condor_schedd starts up, the alive interval starts at the value set by the configuration variable ALIVE_INTERVAL. It may be modified when a job is started. The job’s ClassAd attribute JobLeaseDuration is checked. If the value of JobLeaseDuration/3 is less than the current alive interval, then the alive interval is set to either this lower value or the imposed lowest limit on the alive interval of 10 seconds. Thus, the alive interval starts at ALIVE_INTERVAL and goes down, never up.

If a claim lease expires, the condor_startd will drop the claim. The length of the claim lease is the job’s ClassAd attribute JobLeaseDuration. JobLeaseDuration defaults to 40 minutes time, except when explicitly set within the job’s submit description file. If JobLeaseDuration is explicitly set to 0, or it is not set as may be the case for a Web Services job that does not define the attribute, then JobLeaseDuration is given the Undefined value. Further, when undefined, the claim lease duration is calculated with MAX_CLAIM_ALIVES_MISSED * alive interval. The alive interval is the current value, as sent by the condor_schedd. If the condor_schedd reduces the current alive interval, it does not update the condor_startd.

Machine Activities

Within some machine states, activities of the machine are defined. The state has meaning regardless of activity. Differences between activities are significant. Therefore, a “state/activity” pair describes a machine. The following list describes all the possible state/activity pairs.

  • Owner


    This is the only activity for Owner state. As far as HTCondor is concerned the machine is Idle, since it is not doing anything for HTCondor.

  • Unclaimed


    This is the normal activity of Unclaimed machines. The machine is still Idle in that the machine owner is willing to let HTCondor jobs run, but HTCondor is not using the machine for anything.


    The machine is running benchmarks to determine the speed on this machine. This activity only occurs in the Unclaimed state. How often the activity occurs is determined by the RUNBENCHMARKS expression.

  • Matched


    When Matched, the machine is still Idle to HTCondor.

  • Claimed


    In this activity, the machine has been claimed, but the schedd that claimed it has yet to activate the claim by requesting a condor_starter to be spawned to service a job. The machine returns to this state (usually briefly) when jobs (and therefore condor_starter) finish.


    Once a condor_starter has been started and the claim is active, the machine moves to the Busy activity to signify that it is doing something as far as HTCondor is concerned.


    If the job is suspended by HTCondor, the machine goes into the Suspended activity. The match between the schedd and machine has not been broken (the claim is still valid), but the job is not making any progress and HTCondor is no longer generating a load on the machine.


    When an active claim is about to be preempted for any reason, it enters retirement, while it waits for the current job to finish. The MaxJobRetirementTime expression determines how long to wait (counting since the time the job started). Once the job finishes or the retirement time expires, the Preempting state is entered.

  • Preempting The Preempting state is used for evicting an HTCondor job from a given machine. When the machine enters the Preempting state, it checks the WANT_VACATE expression to determine its activity.


    In the Vacating activity, the job is given a chance to exit cleanly. This may include uploading intermediate files. As soon as the job finishes exiting, the machine moves into either the Owner state or the Claimed state, depending on the reason for its preemption.


    Killing means that the machine has requested the running job to exit the machine immediately.

  • Backfill


    The machine is configured to run backfill jobs and is ready to do so, but it has not yet had a chance to spawn a backfill manager (for example, the BOINC client).


    The machine is performing a backfill computation.


    The machine was running a backfill computation, but it is now killing the job to either return resources to the machine owner, or to make room for a regular HTCondor job.

  • Drained


    All slots have been drained.


    This slot has been drained. It is waiting for other slots to finish draining.

The following diagram gives the overall view of all machine states and activities and shows the possible transitions from one to another within the HTCondor system. Each transition is labeled with a number on the diagram, and transition numbers referred to in this manual will be bold.


The “Matched” state and the “Suspended” activity have been removed from this diagram in order to simplify the number of shown transitions. The Matched state is not entered by default, and Suspended is rarely used.

flowchart TD start((start)) ----> oidle isOwner -- false (1) ------> uidle subgraph OwnerState oidle[Idle\nActivity] isOwner{IS_OWNER\nexpression} -- true --> oidle oidle -- periodic\nrecheck --> isOwner end uidle -- periodic recheck (2) --> isOwner uidle -- drain command --> retiring subgraph UnclaimedState uidle[Idle] -- periodic check --> runBenchmarks runBenchmarks{RunBenchmarks\nexpression} -- true (3) --> benchmarking runBenchmarks -- false --> uidle benchmarking -- when completed (4) --> uidle uidle -- claim by schedd --> isStart isStart -- false --> uidle end didle -- all slots drained\n34 --> isOwner retiring -- draining\ncancelled\n35 --> isOwner subgraph DrainedState retiring -- one slot drained\n33 --> didle[Idle] end isStart{START\nExpression} -- true\nclaim from schedd ----> cidle subgraph ClaimedState cidle[Idle] -- Activate\nby shadow\n11 --> busy busy -- job exit\n12 --> cidle busy -- RANK preemption\nfrom negotiator\n13 --> cretiring busy -- periodic\ncheck --> isPreempt isPreempt{PREEMPT\nexpression} -- true\n13 --> cretiring isPreempt -- false --> busy cretiring[retiring] -- \n\n\nRANK preempt\ncancel\n19 --> busy end cretiring -- MaxJobRetirementTime exceeded\n18 --> wantVacate cidle -- condor_vacate\nor\nSTART == false\n10 --> wantVacate subgraph VacatingState direction TB wantVacate{WANT_VACATE\nexpression} -- true --> vacating vacating -- periodic check --> wantKill wantKill{Kill\nexpression} -- true\n21 --> killing wantKill -- false --> vacating vacating -- MachineMaxVacateTime expires --> killing wantVacate -- false --> killing end killing -- IS_OWNER = true \n22 --> oidle killing -- job exit\n23 --> cidle vacating -- OWNER true\n25 --> isOwner vacating -- better rank\nstarts\n24 --> cidle

States and Activities of the condor_startd

Various expressions are used to determine when and if many of these state and activity transitions occur. Other transitions are initiated by parts of the HTCondor protocol (such as when the condor_negotiator matches a machine with a schedd). The following section describes the conditions that lead to the various state and activity transitions.

State and Activity Transitions

This section traces through all possible state and activity transitions within a machine and describes the conditions under which each one occurs. Whenever a transition occurs, HTCondor records when the machine entered its new activity and/or new state. These times are often used to write expressions that determine when further transitions occurred. For example, enter the Killing activity if a machine has been in the Vacating activity longer than a specified amount of time.

Owner State

When the startd is first spawned, the machine it represents enters the Owner state. The machine remains in the Owner state while the expression IS_OWNER evaluates to TRUE. If the IS_OWNER expression evaluates to FALSE, then the machine transitions to the Unclaimed state. The default value of IS_OWNER is FALSE, which is intended for dedicated resources. But when the POLICY : Desktop configuration template is used, the IS_OWNER expression is optimized for a shared resource


So, the machine will remain in the Owner state as long as the START expression locally evaluates to FALSE. The condor_startd Policy Configuration section provides more detail on the START expression. If the START locally evaluates to TRUE or cannot be locally evaluated (it evaluates to UNDEFINED), transition 1 occurs and the machine enters the Unclaimed state. The IS_OWNER expression is locally evaluated by the machine, and should not reference job ClassAd attributes, which would be UNDEFINED.

The Owner state represents a resource that is in use by its interactive owner (for example, if the keyboard is being used). The Unclaimed state represents a resource that is neither in use by its interactive user, nor the HTCondor system. From HTCondor’s point of view, there is little difference between the Owner and Unclaimed states. In both cases, the resource is not currently in use by the HTCondor system. However, if a job matches the resource’s START expression, the resource is available to run a job, regardless of if it is in the Owner or Unclaimed state. The only differences between the two states are how the resource shows up in condor_status and other reporting tools, and the fact that HTCondor will not run benchmarking on a resource in the Owner state. As long as the IS_OWNER expression is TRUE, the machine is in the Owner State. When the IS_OWNER expression is FALSE, the machine goes into the Unclaimed State.

Here is an example that assumes that the POLICY : Desktop configuration template is in use. If the START expression is

START = KeyboardIdle > 15 * $(MINUTE) && Owner == "coltrane"

and if KeyboardIdle is 34 seconds, then the machine would remain in the Owner state. Owner is undefined, and anything && FALSE is FALSE.

If, however, the START expression is

START = KeyboardIdle > 15 * $(MINUTE) || Owner == "coltrane"

and KeyboardIdle is 34 seconds, then the machine leaves the Owner state and becomes Unclaimed. This is because FALSE || UNDEFINED is UNDEFINED. So, while this machine is not available to just anybody, if user coltrane has jobs submitted, the machine is willing to run them. Any other user’s jobs have to wait until KeyboardIdle exceeds 15 minutes. However, since coltrane might claim this resource, but has not yet, the machine goes to the Unclaimed state.

While in the Owner state, the startd polls the status of the machine every UPDATE_INTERVAL to see if anything has changed that would lead it to a different state. This minimizes the impact on the Owner while the Owner is using the machine. Frequently waking up, computing load averages, checking the access times on files, computing free swap space take time, and there is nothing time critical that the startd needs to be sure to notice as soon as it happens. If the START expression evaluates to TRUE and five minutes pass before the startd notices, that’s a drop in the bucket of high-throughput computing.

The machine can only transition to the Unclaimed state from the Owner state. It does so when the IS_OWNER expression no longer evaluates to TRUE. With the POLICY : Desktop configuration template, that happens when START no longer locally evaluates to FALSE.

Whenever the machine is not actively running a job, it will transition back to the Owner state if IS_OWNER evaluates to TRUE. Once a job is started, the value of IS_OWNER does not matter; the job either runs to completion or is preempted. Therefore, you must configure the preemption policy if you want to transition back to the Owner state from Claimed Busy.

If draining of the machine is initiated while in the Owner state, the slot transitions to Drained/Retiring (transition 36).

Unclaimed State

If the IS_OWNER expression becomes TRUE, then the machine returns to the Owner state. If the IS_OWNER expression becomes FALSE, then the machine remains in the Unclaimed state. The default value of IS_OWNER is FALSE (never enter Owner state). If the POLICY : Desktop configuration template is used, then the IS_OWNER expression is changed to


so that while in the Unclaimed state, if the START expression locally evaluates to FALSE, the machine returns to the Owner state by transition 2.

When in the Unclaimed state, the RUNBENCHMARKS expression is relevant. If RUNBENCHMARKS evaluates to TRUE while the machine is in the Unclaimed state, then the machine will transition from the Idle activity to the Benchmarking activity (transition 3) and perform benchmarks to determine Mips and KFlops. When the benchmarks complete, the machine returns to the Idle activity (transition 4).

The startd automatically inserts an attribute, LastBenchmark, whenever it runs benchmarks, so commonly RunBenchmarks is defined in terms of this attribute, for example:

RunBenchmarks = (time() - LastBenchmark) >= (4 * $(HOUR))

This macro calculates the time since the last benchmark, so when this time exceeds 4 hours, we run the benchmarks again. The startd keeps a weighted average of these benchmarking results to try to get the most accurate numbers possible. This is why it is desirable for the startd to run them more than once in its lifetime.


LastBenchmark is initialized to 0 before benchmarks have ever been run. To have the condor_startd run benchmarks as soon as the machine is Unclaimed (if it has not done so already), include a term using LastBenchmark as in the example above.


If RUNBENCHMARKS is defined and set to something other than FALSE, the startd will automatically run one set of benchmarks when it first starts up. To disable benchmarks, both at startup and at any time thereafter, set RUNBENCHMARKS to FALSE or comment it out of the configuration file.

From the Unclaimed state, the machine can go to four other possible states: Owner (transition 2), Backfill/Idle, Matched, or Claimed/Idle.

Once the condor_negotiator matches an Unclaimed machine with a requester at a given schedd, the negotiator sends a command to both parties, notifying them of the match. If the schedd receives that notification and initiates the claiming procedure with the machine before the negotiator’s message gets to the machine, the Match state is skipped, and the machine goes directly to the Claimed/Idle state (transition 5). However, normally the machine will enter the Matched state (transition 6), even if it is only for a brief period of time.

If the machine has been configured to perform backfill jobs (see the Configuring HTCondor for Running Backfill Jobs section), while it is in Unclaimed/Idle it will evaluate the START_BACKFILL expression. Once START_BACKFILL evaluates to TRUE, the machine will enter the Backfill/Idle state (transition 7) to begin the process of running backfill jobs.

If draining of the machine is initiated while in the Unclaimed state, the slot transitions to Drained/Retiring (transition 37).

Matched State

The Matched state is not very interesting to HTCondor. Noteworthy in this state is that the machine lies about its START expression while in this state and says that Requirements are False to prevent being matched again before it has been claimed. Also interesting is that the startd starts a timer to make sure it does not stay in the Matched state too long. The timer is set with the MATCH_TIMEOUT configuration file macro. It is specified in seconds and defaults to 120 (2 minutes). If the schedd that was matched with this machine does not claim it within this period of time, the machine gives up, and goes back into the Owner state via transition 8. It will probably leave the Owner state right away for the Unclaimed state again and wait for another match.

At any time while the machine is in the Matched state, if the START expression locally evaluates to FALSE, the machine enters the Owner state directly (transition 8).

If the schedd that was matched with the machine claims it before the MATCH_TIMEOUT expires, the machine goes into the Claimed/Idle state (transition 9).

Claimed State

The Claimed state is certainly the most complex state. It has the most possible activities and the most expressions that determine its next activities. In addition, the condor_vacate command affects the machine when it is in the Claimed state.

In general, there are two sets of expressions that might take effect, depending on the universe of the job running on the claim: vanilla, and all others. The normal expressions look like the following:

WANT_SUSPEND            = True
WANT_VACATE             = $(ActivationTimer) > 10 * $(MINUTE)
SUSPEND                 = $(KeyboardBusy) || $(CPUBusy)

The vanilla expressions have the string”_VANILLA” appended to their names. For example:

SUSPEND_VANILLA         = $(KeyboardBusy) || $(CPUBusy)

Without specific vanilla versions, the normal versions will be used for all jobs, including vanilla jobs. In this manual, the normal expressions are referenced.

While Claimed, the POLLING_INTERVAL takes effect, and the startd polls the machine much more frequently to evaluate its state.

If the machine owner starts typing on the console again, it is best to notice this as soon as possible to be able to start doing whatever the machine owner wants at that point. For multi-core machines, if any slot is in the Claimed state, the startd polls the machine frequently. If already polling one slot, it does not cost much to evaluate the state of all the slots at the same time.

There are a variety of events that may cause the startd to try to get rid of or temporarily suspend a running job. Activity on the machine’s console, load from other jobs, or shutdown of the startd via an administrative command are all possible sources of interference. Another one is the appearance of a higher priority claim to the machine by a different HTCondor user.

Depending on the configuration, the startd may respond quite differently to activity on the machine, such as keyboard activity or demand for the cpu from processes that are not managed by HTCondor. The startd can be configured to completely ignore such activity or to suspend the job or even to kill it. A standard configuration for a desktop machine might be to go through successive levels of getting the job out of the way. The first and least costly to the job is suspending it. If suspending the job for a short while does not satisfy the machine owner (the owner is still using the machine after a specific period of time), the startd moves on to vacating the job. Vanilla jobs are sent a soft kill signal so that they can gracefully shut down if necessary; the default is SIGTERM. If vacating does not satisfy the machine owner (usually because it is taking too long and the owner wants their machine back now), the final, most drastic stage is reached: killing. Killing is a quick death to the job, using a hard-kill signal that cannot be intercepted by the application. For vanilla jobs that do no special signal handling, vacating and killing are equivalent.

The WANT_SUSPEND expression determines if the machine will evaluate the SUSPEND expression to consider entering the Suspended activity. The WANT_VACATE expression determines what happens when the machine enters the Preempting state. It will go to the Vacating activity or directly to Killing. If one or both of these expressions evaluates to FALSE, the machine will skip that stage of getting rid of the job and proceed directly to the more drastic stages.

When the machine first enters the Claimed state, it goes to the Idle activity. From there, it has two options. It can enter the Preempting state via transition 10 (if a condor_vacate arrives, or if the START expression locally evaluates to FALSE), or it can enter the Busy activity (transition 11) if the schedd that has claimed the machine decides to activate the claim and start a job.

From Claimed/Busy, the machine can transition to three other state/activity pairs. The startd evaluates the WANT_SUSPEND expression to decide which other expressions to evaluate. If WANT_SUSPEND is TRUE, then the startd evaluates the SUSPEND expression. If WANT_SUSPEND is any value other than TRUE, then the startd will evaluate the PREEMPT expression and skip the Suspended activity entirely. By transition, the possible state/activity destinations from Claimed/Busy:


If the starter that is serving a given job exits (for example because the jobs completes), the machine will go to Claimed/Idle (transition 12). Claimed/Retiring If WANT_SUSPEND is FALSE and the PREEMPT expression is True, the machine enters the Retiring activity (transition 13). From there, it waits for a configurable amount of time for the job to finish before moving on to preemption.

Another reason the machine would go from Claimed/Busy to Claimed/Retiring is if the condor_negotiator matched the machine with a “better” match. This better match could either be from the machine’s perspective using the startd RANK expression, or it could be from the negotiator’s perspective due to a job with a higher user priority.

Another case resulting in a transition to Claimed/Retiring is when the startd is being shut down. The only exception is a “fast” shutdown, which bypasses retirement completely.


If both the WANT_SUSPEND and SUSPEND expressions evaluate to TRUE, the machine suspends the job (transition 14).

From the Claimed/Suspended state, the following transitions may occur:


If the CONTINUE expression evaluates to TRUE, the machine resumes the job and enters the Claimed/Busy state (transition 15) or the Claimed/Retiring state (transition 16), depending on whether the claim has been preempted.


If the PREEMPT expression is TRUE, the machine will enter the Claimed/Retiring activity (transition 16).


If the claim is in suspended retirement and the retirement time expires, the job enters the Preempting state (transition 17). This is only possible if MaxJobRetirementTime decreases during the suspension.

For the Claimed/Retiring state, the following transitions may occur:


If the job finishes or the job’s run time exceeds the value defined for the job ClassAd attribute MaxJobRetirementTime, the Preempting state is entered (transition 18). The run time is computed from the time when the job was started by the startd minus any suspension time. When retiring due to condor_startd daemon shutdown or restart, it is possible for the administrator to issue a peaceful shutdown command, which causes MaxJobRetirementTime to effectively be infinite, avoiding any killing of jobs. It is also possible for the administrator to issue a fast shutdown command, which causes MaxJobRetirementTime to be effectively 0.


If the startd was retiring because of a preempting claim only and the preempting claim goes away, the normal Claimed/Busy state is resumed (transition 19). If instead the retirement is due to owner activity (PREEMPT) or the startd is being shut down, no unretirement is possible.


In exactly the same way that suspension may happen from the Claimed/Busy state, it may also happen during the Claimed/Retiring state (transition 20). In this case, when the job continues from suspension, it moves back into Claimed/Retiring (transition 16) instead of Claimed/Busy (transition 15).

Preempting State

The Preempting state is less complex than the Claimed state. There are two activities. Depending on the value of WANT_VACATE, a machine will be in the Vacating activity (if True) or the Killing activity (if False).

While in the Preempting state (regardless of activity) the machine advertises its Requirements expression as False to signify that it is not available for further matches, either because it is about to transition to the Owner state, or because it has already been matched with one preempting match, and further preempting matches are disallowed until the machine has been claimed by the new match.

The main function of the Preempting state is to get rid of the condor_starter associated with the resource. If the condor_starter associated with a given claim exits while the machine is still in the Vacating activity, then the job successfully completed a graceful shutdown. For other jobs, this means the application was given an opportunity to do a graceful shutdown, by intercepting the soft kill signal.

If the machine is in the Vacating activity, it keeps evaluating the KILL expression. As soon as this expression evaluates to TRUE, the machine enters the Killing activity (transition 21). If the Vacating activity lasts for as long as the maximum vacating time, then the machine also enters the Killing activity. The maximum vacating time is determined by the configuration variable MachineMaxVacateTime. This may be adjusted by the setting of the job ClassAd attribute JobMaxVacateTime.

When the starter exits, or if there was no starter running when the machine enters the Preempting state (transition 10), the other purpose of the Preempting state is completed: notifying the schedd that had claimed this machine that the claim is broken.

At this point, the machine enters either the Owner state by transition 22 (if the job was preempted because the machine owner came back) or the Claimed/Idle state by transition 23 (if the job was preempted because a better match was found).

If the machine enters the Killing activity, (because either WANT_VACATE was False or the KILL expression evaluated to True), it attempts to force the condor_starter to immediately kill the underlying HTCondor job. Once the machine has begun to hard kill the HTCondor job, the condor_startd starts a timer, the length of which is defined by the KILLING_TIMEOUT macro (condor_startd Configuration File Macros). This macro is defined in seconds and defaults to 30. If this timer expires and the machine is still in the Killing activity, something has gone seriously wrong with the condor_starter and the startd tries to vacate the job immediately by sending SIGKILL to all of the condor_starter ‘s children, and then to the condor_starter itself.

Once the condor_starter has killed off all the processes associated with the job and exited, and once the schedd that had claimed the machine is notified that the claim is broken, the machine will leave the Preempting/Killing state. If the job was preempted because a better match was found, the machine will enter Claimed/Idle (transition 24). If the preemption was caused by the machine owner (the PREEMPT expression evaluated to TRUE, condor_vacate was used, etc), the machine will enter the Owner state (transition 25).

Backfill State

The Backfill state is used whenever the machine is performing low priority background tasks to keep itself busy. For more information about backfill support in HTCondor, see the Configuring HTCondor for Running Backfill Jobs section. This state is only used if the machine has been configured to enable backfill computation, if a specific backfill manager has been installed and configured, and if the machine is otherwise idle (not being used interactively or for regular HTCondor computations). If the machine meets all these requirements, and the START_BACKFILL expression evaluates to TRUE, the machine will move from the Unclaimed/Idle state to Backfill/Idle (transition 7).

Once a machine is in Backfill/Idle, it will immediately attempt to spawn whatever backfill manager it has been configured to use (currently, only the BOINC client is supported as a backfill manager in HTCondor). Once the BOINC client is running, the machine will enter Backfill/Busy (transition 26) to indicate that it is now performing a backfill computation.


On multi-core machines, the condor_startd will only spawn a single instance of the BOINC client, even if multiple slots are available to run backfill jobs. Therefore, only the first machine to enter Backfill/Idle will cause a copy of the BOINC client to start running. If a given slot on a multi-core enters the Backfill state and a BOINC client is already running under this condor_startd, the slot will immediately enter Backfill/Busy without waiting to spawn another copy of the BOINC client.

If the BOINC client ever exits on its own (which normally wouldn’t happen), the machine will go back to Backfill/Idle (transition 27) where it will immediately attempt to respawn the BOINC client (and return to Backfill/Busy via transition 26).

As the BOINC client is running a backfill computation, a number of events can occur that will drive the machine out of the Backfill state. The machine can get matched or claimed for an HTCondor job, interactive users can start using the machine again, the machine might be evicted with condor_vacate, or the condor_startd might be shutdown. All of these events cause the condor_startd to kill the BOINC client and all its descendants, and enter the Backfill/Killing state (transition 28).

Once the BOINC client and all its children have exited the system, the machine will enter the Backfill/Idle state to indicate that the BOINC client is now gone (transition 29). As soon as it enters Backfill/Idle after the BOINC client exits, the machine will go into another state, depending on what caused the BOINC client to be killed in the first place.

If the EVICT_BACKFILL expression evaluates to TRUE while a machine is in Backfill/Busy, after the BOINC client is gone, the machine will go back into the Owner/Idle state (transition 30). The machine will also return to the Owner/Idle state after the BOINC client exits if condor_vacate was used, or if the condor_startd is being shutdown.

When a machine running backfill jobs is matched with a requester that wants to run an HTCondor job, the machine will either enter the Matched state, or go directly into Claimed/Idle. As with the case of a machine in Unclaimed/Idle (described above), the condor_negotiator informs both the condor_startd and the condor_schedd of the match, and the exact state transitions at the machine depend on what order the various entities initiate communication with each other. If the condor_schedd is notified of the match and sends a request to claim the condor_startd before the condor_negotiator has a chance to notify the condor_startd, once the BOINC client exits, the machine will immediately enter Claimed/Idle (transition 31). Normally, the notification from the condor_negotiator will reach the condor_startd before the condor_schedd attempts to claim it. In this case, once the BOINC client exits, the machine will enter Matched/Idle (transition 32).

Drained State

The Drained state is used when the machine is being drained, for example by condor_drain or by the condor_defrag daemon, and the slot has finished running jobs and is no longer willing to run new jobs.

Slots initially enter the Drained/Retiring state. Once all slots have been drained, the slots transition to the Idle activity (transition 33).

If draining is finalized or canceled, the slot transitions to Owner/Idle (transitions 34 and 35).

State/Activity Transition Expression Summary

This section is a summary of the information from the previous sections. It serves as a quick reference.


When TRUE, the machine is willing to spawn a remote HTCondor job.


While in the Unclaimed state, the machine will run benchmarks whenever TRUE.


If the machine has been in the Matched state longer than this value, it will transition to the Owner state.


If True, the machine evaluates the SUSPEND expression to see if it should transition to the Suspended activity. If any value other than True, the machine will look at the PREEMPT expression.


If WANT_SUSPEND is True, and the machine is in the Claimed/Busy state, it enters the Suspended activity if SUSPEND is True.


If the machine is in the Claimed/Suspended state, it enter the Busy activity if CONTINUE is True.


If the machine is either in the Claimed/Suspended activity, or is in the Claimed/Busy activity and WANT_SUSPEND is FALSE, the machine enters the Claimed/Retiring state whenever PREEMPT is TRUE.


This expression specifies the number of seconds after which a claim will stop accepting additional jobs. This configuration macro is fully documented here: condor_startd Configuration File Macros.


When the machine enters the Preempting/Vacating state, this expression specifies the maximum time in seconds that the condor_startd will wait for the job to finish. The job may adjust the wait time by setting JobMaxVacateTime. If the job’s setting is less than the machine’s, the job’s is used. If the job’s setting is larger than the machine’s, the result depends on whether the job has any excess retirement time. If the job has more retirement time left than the machine’s maximum vacate time setting, then retirement time will be converted into vacating time, up to the amount of JobMaxVacateTime. Once the vacating time expires, the job is hard-killed. The KILL expression may be used to abort the graceful shutdown of the job at any time.


If the machine is in the Claimed/Retiring state, jobs which have run for less than the number of seconds specified by this expression will not be hard-killed. The condor_startd will wait for the job to finish or to exceed this amount of time, whichever comes sooner. Time spent in suspension does not count against the job. If the job vacating policy grants the job X seconds of vacating time, a preempted job will be soft-killed X seconds before the end of its retirement time, so that hard-killing of the job will not happen until the end of the retirement time if the job does not finish shutting down before then. The job may provide its own expression for MaxJobRetirementTime, but this can only be used to take less than the time granted by the condor_startd, never more. For convenience, nice_user jobs are submitted with a default retirement time of 0, so they will never wait in retirement unless the user overrides the default.

The machine enters the Preempting state with the goal of finishing shutting down the job by the end of the retirement time. If the job vacating policy grants the job X seconds of vacating time, the transition to the Preempting state will happen X seconds before the end of the retirement time, so that the hard-killing of the job will not happen until the end of the retirement time, if the job does not finish shutting down before then.

This expression is evaluated in the context of the job ClassAd, so it may refer to attributes of the current job as well as machine attributes.

By default the condor_negotiator will not match jobs to a slot with retirement time remaining. This behavior is controlled by NEGOTIATOR_CONSIDER_EARLY_PREEMPTION.


This is checked only when the PREEMPT expression is True and the machine enters the Preempting state. If WANT_VACATE is True, the machine enters the Vacating activity. If it is False, the machine will proceed directly to the Killing activity.


If the machine is in the Preempting/Vacating state, it enters Preempting/Killing whenever KILL is True.


If the machine is in the Preempting/Killing state for longer than KILLING_TIMEOUT seconds, the condor_startd sends a SIGKILL to the condor_starter and all its children to try to kill the job as quickly as possible.


If this expression evaluates to a higher number for a pending resource request than it does for the current request, the machine may preempt the current request (enters the Preempting/Vacating state). When the preemption is complete, the machine enters the Claimed/Idle state with the new resource request claiming it.


When TRUE, if the machine is otherwise idle, it will enter the Backfill state and spawn a backfill computation (using BOINC).


When TRUE, if the machine is currently running a backfill computation, it will kill the BOINC client and return to the Owner/Idle state.

Configuring HTCondor for Running Backfill Jobs

HTCondor can be configured to run backfill jobs whenever the condor_startd has no other work to perform. These jobs are considered the lowest possible priority, but when machines would otherwise be idle, the resources can be put to good use.

Currently, HTCondor only supports using the Berkeley Open Infrastructure for Network Computing (BOINC) to provide the backfill jobs. More information about BOINC is available at

The rest of this section provides an overview of how backfill jobs work in HTCondor, details for configuring the policy for when backfill jobs are started or killed, and details on how to configure HTCondor to spawn the BOINC client to perform the work.

Overview of Backfill jobs in HTCondor

Whenever a resource controlled by HTCondor is in the Unclaimed/Idle state, it is totally idle; neither the interactive user nor an HTCondor job is performing any work. Machines in this state can be configured to enter the Backfill state, which allows the resource to attempt a background computation to keep itself busy until other work arrives (either a user returning to use the machine interactively, or a normal HTCondor job). Once a resource enters the Backfill state, the condor_startd will attempt to spawn another program, called a backfill client, to launch and manage the backfill computation. When other work arrives, the condor_startd will kill the backfill client and clean up any processes it has spawned, freeing the machine resources for the new, higher priority task. More details about the different states an HTCondor resource can enter and all of the possible transitions between them are described in Machine States, above.

At this point, the only backfill system supported by HTCondor is BOINC. The condor_startd has the ability to start and stop the BOINC client program at the appropriate times, but otherwise provides no additional services to configure the BOINC computations themselves. Future versions of HTCondor might provide additional functionality to make it easier to manage BOINC computations from within HTCondor. For now, the BOINC client must be manually installed and configured outside of HTCondor on each backfill-enabled machine.

Defining the Backfill Policy

There are a small set of policy expressions that determine if a condor_startd will attempt to spawn a backfill client at all, and if so, to control the transitions in to and out of the Backfill state. This section briefly lists these expressions. More detail can be found in condor_startd Configuration File Macros.


A boolean value to determine if any backfill functionality should be used. The default value is False.


A string that defines what backfill system to use for spawning and managing backfill computations. Currently, the only supported string is "BOINC".


A boolean expression to control if an HTCondor resource should start a backfill client. This expression is only evaluated when the machine is in the Unclaimed/Idle state and the ENABLE_BACKFILL expression is True.


A boolean expression that is evaluated whenever an HTCondor resource is in the Backfill state. A value of True indicates the machine should immediately kill the currently running backfill client and any other spawned processes, and return to the Owner state.

The following example shows a possible configuration to enable backfill:

# Turn on backfill functionality, and use BOINC

# Spawn a backfill job if we've been Unclaimed for more than 5
# minutes
START_BACKFILL = $(StateTimer) > (5 * $(MINUTE))

# Evict a backfill job if the machine is busy (based on keyboard
# activity or cpu load)
EVICT_BACKFILL = $(MachineBusy)

Overview of the BOINC system

The BOINC system is a distributed computing environment for solving large scale scientific problems. A detailed explanation of this system is beyond the scope of this manual. Thorough documentation about BOINC is available at their website: However, a brief overview is provided here for sites interested in using BOINC with HTCondor to manage backfill jobs.

BOINC grew out of the relatively famous SETI@home computation, where volunteers installed special client software, in the form of a screen saver, that contacted a centralized server to download work units. Each work unit contained a set of radio telescope data and the computation tried to find patterns in the data, a sign of intelligent life elsewhere in the universe, hence the name: “Search for Extra Terrestrial Intelligence at home”. BOINC is developed by the Space Sciences Lab at the University of California, Berkeley, by the same people who created SETI@home. However, instead of being tied to the specific radio telescope application, BOINC is a generic infrastructure by which many different kinds of scientific computations can be solved. The current generation of SETI@home now runs on top of BOINC, along with various physics, biology, climatology, and other applications.

The basic computational model for BOINC and the original SETI@home is the same: volunteers install BOINC client software, called the boinc_client, which runs whenever the machine would otherwise be idle. However, the BOINC installation on any given machine must be configured so that it knows what computations to work for instead of always working on a hard coded computation. The BOINC terminology for a computation is a project. A given BOINC client can be configured to donate all of its cycles to a single project, or to split the cycles between projects so that, on average, the desired percentage of the computational power is allocated to each project. Once the boinc_client starts running, it attempts to contact a centralized server for each project it has been configured to work for. The BOINC software downloads the appropriate platform-specific application binary and some work units from the central server for each project. Whenever the client software completes a given work unit, it once again attempts to connect to that project’s central server to upload the results and download more work.

BOINC participants must register at the centralized server for each project they wish to donate cycles to. The process produces a unique identifier so that the work performed by a given client can be credited to a specific user. BOINC keeps track of the work units completed by each user, so that users providing the most cycles get the highest rankings, and therefore, bragging rights.

Because BOINC already handles the problems of distributing the application binaries for each scientific computation, the work units, and compiling the results, it is a perfect system for managing backfill computations in HTCondor. Many of the applications that run on top of BOINC produce their own application-specific checkpoints, so even if the boinc_client is killed, for example, when an HTCondor job arrives at a machine, or if the interactive user returns, an entire work unit will not necessarily be lost.

Installing the BOINC client software

In HTCondor Version 23.7.2, the boinc_client must be manually downloaded, installed and configured outside of HTCondor. Download the boinc_client executables at

Once the BOINC client software has been downloaded, the boinc_client binary should be placed in a location where the HTCondor daemons can use it. The path will be specified with the HTCondor configuration variable BOINC_Executable.

Additionally, a local directory on each machine should be created where the BOINC system can write files it needs. This directory must not be shared by multiple instances of the BOINC software. This is the same restriction as placed on the spool or execute directories used by HTCondor. The location of this directory is defined by BOINC_InitialDir. The directory must be writable by whatever user the boinc_client will run as. This user is either the same as the user the HTCondor daemons are running as, if HTCondor is not running as root, or a user defined via the BOINC_Owner configuration variable.

Finally, HTCondor administrators wishing to use BOINC for backfill jobs must create accounts at the various BOINC projects they want to donate cycles to. The details of this process vary from project to project. Beware that this step must be done manually, as the boinc_client can not automatically register a user at a given project, unlike the more fancy GUI version of the BOINC client software which many users run as a screen saver. For example, to configure machines to perform work for the Einstein@home project (a physics experiment run by the University of Wisconsin at Milwaukee), HTCondor administrators should go to, fill in the web form, and generate a new Einstein@home identity. This identity takes the form of a project URL (such as followed by an account key, which is a long string of letters and numbers that is used as a unique identifier. This URL and account key will be needed when configuring HTCondor to use BOINC for backfill computations.

Configuring the BOINC client under HTCondor

After the boinc_client has been installed on a given machine, the BOINC projects to join have been selected, and a unique project account key has been created for each project, the HTCondor configuration needs to be modified.

Whenever the condor_startd decides to spawn the boinc_client to perform backfill computations, it will spawn a condor_starter to directly launch and monitor the boinc_client program. This condor_starter is just like the one used to invoke any other HTCondor jobs.

This condor_starter reads values out of the HTCondor configuration files to define the job it should run, as opposed to getting these values from a job ClassAd in the case of a normal HTCondor job. All of the configuration variables names for variables to control things such as the path to the boinc_client binary to use, the command-line arguments, and the initial working directory, are prefixed with the string "BOINC_". Each of these variables is described as either a required or an optional configuration variable.

Required configuration variables:


The full path and executable name of the boinc_client binary to use.


The full path to the local directory where BOINC should run.


The HTCondor universe used for running the boinc_client program. This must be set to vanilla for BOINC to work under HTCondor.


What user the boinc_client program should be run as. This variable is only used if the HTCondor daemons are running as root. In this case, the condor_starter must be told what user identity to switch to before invoking the boinc_client. This can be any valid user on the local system, but it must have write permission in whatever directory is specified by BOINC_InitialDir.

Optional configuration variables:


Command-line arguments that should be passed to the boinc_client program. For example, one way to specify the BOINC project to join is to use the -attach_project argument to specify a project URL and account key. For example:

BOINC_Arguments = --attach_project [account_key]

Environment variables that should be set for the boinc_client.


Full path to the file where stdout from the boinc_client should be written. If this variable is not defined, stdout will be discarded.


Full path to the file where stderr from the boinc_client should be written. If this macro is not defined, stderr will be discarded.

The following example shows one possible usage of these settings:

# Define a shared macro that can be used to define other settings.
# This directory must be manually created before attempting to run
# any backfill jobs.

# Path to the boinc_client to use, and required universe setting
BOINC_Executable = /usr/local/bin/boinc_client
BOINC_Universe = vanilla

# What initial working directory should BOINC use?
BOINC_InitialDir = $(BOINC_HOME)

# Where to place stdout and stderr
BOINC_Output = $(BOINC_HOME)/boinc.out
BOINC_Error = $(BOINC_HOME)/boinc.err

If the HTCondor daemons reading this configuration are running as root, an additional variable must be defined:

# Specify the user that the boinc_client should run as:
BOINC_Owner = nobody

In this case, HTCondor would spawn the boinc_client as nobody, so the directory specified in $(BOINC_HOME) would have to be writable by the nobody user.

A better choice would probably be to create a separate user account just for running BOINC jobs, so that the local BOINC installation is not writable by other processes running as nobody. Alternatively, the BOINC_Owner could be set to daemon.

Attaching to a specific BOINC project

There are a few ways to attach an HTCondor/BOINC installation to a given BOINC project:

  • Use the -attach_project argument to the boinc_client program, defined via the BOINC_Arguments variable. The boinc_client will only accept a single -attach_project argument, so this method can only be used to attach to one project.

  • The boinc_cmd command-line tool can perform various BOINC administrative tasks, including attaching to a BOINC project. Using boinc_cmd, the appropriate argument to use is called -project_attach. Unfortunately, the boinc_client must be running for boinc_cmd to work, so this method can only be used once the HTCondor resource has entered the Backfill state and has spawned the boinc_client.

  • Manually create account files in the local BOINC directory. Upon start up, the boinc_client will scan its local directory (the directory specified with BOINC_InitialDir) for files of the form account_[URL].xml, for example, Any files with a name that matches this convention will be read and processed. The contents of the file define the project URL and the authentication key. The format is:


    For example:


    Of course, the <authenticator> tag would use the real authentication key returned when the account was created at a given project.

    These account files can be copied to the local BOINC directory on all machines in an HTCondor pool, so administrators can either distribute them manually, or use symbolic links to point to a shared file system.

In the two cases of using command-line arguments for boinc_client or running the boinc_cmd tool, BOINC will write out the resulting account file to the local BOINC directory on the machine, and then future invocations of the boinc_client will already be attached to the appropriate project(s).

BOINC on Windows

The Windows version of BOINC has multiple installation methods. The preferred method of installation for use with HTCondor is the Shared Installation method. Using this method gives all users access to the executables. During the installation process

  1. Deselect the option which makes BOINC the default screen saver

  2. Deselect the option which runs BOINC on start up.

  3. Do not launch BOINC at the conclusion of the installation.

There are three major differences from the Unix version to keep in mind when dealing with the Windows installation:

  1. The Windows executables have different names from the Unix versions. The Windows client is called boinc.exe. Therefore, the configuration variable BOINC_Executable is written:

    BOINC_Executable = C:\PROGRA~1\BOINC\boinc.exe

    The Unix administrative tool boinc_cmd is called boinccmd.exe on Windows.

  2. When using BOINC on Windows, the configuration variable BOINC_InitialDir will not be respected fully. To work around this difficulty, pass the BOINC home directory directly to the BOINC application via the BOINC_Arguments configuration variable. For Windows, rewrite the argument line as:

    BOINC_Arguments = --dir $(BOINC_HOME) \
              --attach_project [account_key]

    As a consequence of setting the BOINC home directory, some projects may fail with the authentication error:

    Scheduler request failed: Peer
    certificate cannot be authenticated
    with known CA certificates.

    To resolve this issue, copy the ca-bundle.crt file from the BOINC installation directory to $(BOINC_HOME). This file appears to be project and machine independent, and it can therefore be distributed as part of an automated HTCondor installation.

  3. The BOINC_Owner configuration variable behaves differently on Windows than it does on Unix. Its value may take one of two forms:

    • domain\user

    • user This form assumes that the user exists in the local domain (that is, on the computer itself).

    Setting this option causes the addition of the job attribute

    RunAsUser = True

    to the backfill client. This further implies that the configuration variable STARTER_ALLOW_RUNAS_OWNER be set to True to insure that the local condor_starter be able to run jobs in this manner. For more information on the RunAsUser attribute, see Executing Jobs as the Submitting User. For more information on the the STARTER_ALLOW_RUNAS_OWNER configuration variable, see Shared File System Configuration File Macros.

Examples of Policy Configuration

This section describes various policy configurations, including the default policy.

Default Policy

These settings are the default as shipped with HTCondor. They have been used for many years with no problems. The vanilla expressions are identical to the regular ones. (They are not listed here. If not defined, the standard expressions are used for vanilla jobs as well).

The following are macros to help write the expressions clearly.


Amount of time in seconds in the current state.


Amount of time in seconds in the current activity.


Amount of time in seconds that the job has been running on this machine.


The difference between the system load and the HTCondor load (the load generated by everything but HTCondor).


Amount of background load permitted on the machine and still start an HTCondor job.


If the $(NonCondorLoadAvg) goes over this, the CPU is considered too busy, and eviction of the HTCondor job should start.


Amount of time the keyboard must to be idle before HTCondor will start a job.


Amount of time the keyboard must to be idle before resumption of a suspended job.


Amount of time a job may be suspended before more drastic measures are taken.


A boolean expression that evaluates to TRUE when the keyboard is being used.


A boolean expression that evaluates to TRUE when the CPU is idle.


A boolean expression that evaluates to TRUE when the CPU is busy.


The CPU or the Keyboard is busy.


A boolean value set to the same value as CPUBusy.


The value 0 if CPUBusy is False; the time in seconds since CPUBusy became True.

These variable definitions exist in the example configuration file in order to help write legible expressions. They are not required, and perhaps will go unused by many configurations.

##  These macros are here to help write legible expressions:
MINUTE          = 60
HOUR            = (60 * $(MINUTE))
StateTimer      = (time() - EnteredCurrentState)
ActivityTimer   = (time() - EnteredCurrentActivity)
ActivationTimer = (time() - JobStart)

NonCondorLoadAvg        = (LoadAvg - CondorLoadAvg)
BackgroundLoad          = 0.3
HighLoad                = 0.5
StartIdleTime           = 15 * $(MINUTE)
ContinueIdleTime        = 5 * $(MINUTE)
MaxSuspendTime          = 10 * $(MINUTE)

KeyboardBusy            = KeyboardIdle < $(MINUTE)
ConsoleBusy             = (ConsoleIdle  < $(MINUTE))
CPUIdle                = $(NonCondorLoadAvg) <= $(BackgroundLoad)
CPUBusy                = $(NonCondorLoadAvg) >= $(HighLoad)
KeyboardNotBusy         = ($(KeyboardBusy) == False)
MachineBusy             = ($(CPUBusy) || $(KeyboardBusy)

Preemption is disabled as a default. Always desire to start jobs.

WANT_SUSPEND         = False
WANT_VACATE          = False
START                = True
SUSPEND              = False
CONTINUE             = True
PREEMPT              = False
# Kill jobs that take too long leaving gracefully.
MachineMaxVacateTime = 10 * $(MINUTE)
KILL                 = False

Test-job Policy Example

This example shows how the default macros can be used to set up a machine for running test jobs from a specific user. Suppose we want the machine to behave normally, except if user coltrane submits a job. In that case, we want that job to start regardless of what is happening on the machine. We do not want the job suspended, vacated or killed. This is reasonable if we know coltrane is submitting very short running programs for testing purposes. The jobs should be executed right away. This works with any machine (or the whole pool, for that matter) by adding the following 5 expressions to the existing configuration:

START      = ($(START)) || Owner == "coltrane"
SUSPEND    = ($(SUSPEND)) && Owner != "coltrane"
PREEMPT    = ($(PREEMPT)) && Owner != "coltrane"
KILL       = $(KILL)

Notice that there is nothing special in either the CONTINUE or KILL expressions. If Coltrane’s jobs never suspend, they never look at CONTINUE. Similarly, if they never preempt, they never look at KILL.

Time of Day Policy

HTCondor can be configured to only run jobs at certain times of the day. In general, we discourage configuring a system like this, since there will often be lots of good cycles on machines, even when their owners say “I’m always using my machine during the day.” However, if you submit mostly jobs that cannot produce checkpoints, it might be a good idea to only allow the jobs to run when you know the machines will be idle and when they will not be interrupted.

To configure this kind of policy, use the ClockMin and ClockDay attributes. These are special attributes which are automatically inserted by the condor_startd into its ClassAd, so you can always reference them in your policy expressions. ClockMin defines the number of minutes that have passed since midnight. For example, 8:00am is 8 hours after midnight, or 8 * 60 minutes, or 480. 5:00pm is 17 hours after midnight, or 17 * 60, or 1020. ClockDay defines the day of the week, Sunday = 0, Monday = 1, and so on.

To make the policy expressions easy to read, we recommend using macros to define the time periods when you want jobs to run or not run. For example, assume regular work hours at your site are from 8:00am until 5:00pm, Monday through Friday:

WorkHours = ( (ClockMin >= 480 && ClockMin < 1020) && \
              (ClockDay > 0 && ClockDay < 6) )
AfterHours = ( (ClockMin < 480 || ClockMin >= 1020) || \
               (ClockDay == 0 || ClockDay == 6) )

Of course, you can fine-tune these settings by changing the definition of AfterHours and WorkHours for your site.

To force HTCondor jobs to stay off of your machines during work hours:

# Only start jobs after hours.
START = $(AfterHours)

# Consider the machine busy during work hours, or if the keyboard or
# CPU are busy.
MachineBusy = ( $(WorkHours) || $(CPUBusy) || $(KeyboardBusy) )

This MachineBusy macro is convenient if other than the default SUSPEND and PREEMPT expressions are used.

Desktop/Non-Desktop Policy

Suppose you have two classes of machines in your pool: desktop machines and dedicated cluster machines. In this case, you might not want keyboard activity to have any effect on the dedicated machines. For example, when you log into these machines to debug some problem, you probably do not want a running job to suddenly be killed. Desktop machines, on the other hand, should do whatever is necessary to remain responsive to the user.

There are many ways to achieve the desired behavior. One way is to make a standard desktop policy and a standard non-desktop policy and to copy the desired one into the local configuration file for each machine. Another way is to define one standard policy (in the global configuration file) with a simple toggle that can be set in the local configuration file. The following example illustrates the latter approach.

For ease of use, an entire policy is included in this example. Some of the expressions are just the usual default settings.

# If "IsDesktop" is configured, make it an attribute of the machine ClassAd.

# Only consider starting jobs if:
# 1) the load average is low enough OR the machine is currently
#    running an HTCondor job
# 2) AND the user is not active (if a desktop)
START = ( ($(CPUIdle) || (State != "Unclaimed" && State != "Owner")) \
          && (IsDesktop =!= True || (KeyboardIdle > $(StartIdleTime))) )

# Suspend (instead of vacating/killing) for the following cases:
WANT_SUSPEND = ( $(SmallJob) || $(JustCpu) \
                 || $(IsVanilla) )

# When preempting, vacate (instead of killing) in the following cases:
WANT_VACATE  = ( $(ActivationTimer) > 10 * $(MINUTE) \
                 || $(IsVanilla) )

# Suspend jobs if:
# 1) The CPU has been busy for more than 2 minutes, AND
# 2) the job has been running for more than 90 seconds
# 3) OR suspend if this is a desktop and the user is active
SUSPEND = ( ((CpuBusyTime > 2 * $(MINUTE)) && ($(ActivationTimer) > 90)) \
            || ( IsDesktop =?= True && $(KeyboardBusy) ) )

# Continue jobs if:
# 1) the CPU is idle, AND
# 2) we've been suspended more than 5 minutes AND
# 3) the keyboard has been idle for long enough (if this is a desktop)
CONTINUE = ( $(CPUIdle) && ($(ActivityTimer) > 300) \
             && (IsDesktop =!= True || (KeyboardIdle > $(ContinueIdleTime))) )

# Preempt jobs if:
# 1) The job is suspended and has been suspended longer than we want
# 2) OR, we don't want to suspend this job, but the conditions to
#    suspend jobs have been met (someone is using the machine)
PREEMPT = ( ((Activity == "Suspended") && \
            ($(ActivityTimer) > $(MaxSuspendTime))) \
           || (SUSPEND && (WANT_SUSPEND == False)) )

# Replace 0 in the following expression with whatever amount of
# retirement time you want dedicated machines to provide.  The other part
# of the expression forces the whole expression to 0 on desktop
# machines.
MAXJOBRETIREMENTTIME = (IsDesktop =!= True) * 0

# Kill jobs if they have taken too long to vacate gracefully
MachineMaxVacateTime = 10 * $(MINUTE)
KILL = False

With this policy in the global configuration, the local configuration files for desktops can be easily configured with the following line:

IsDesktop = True

In all other cases, the default policy described above will ignore keyboard activity.

Disabling and Enabling Preemption

Preemption causes a running job to be suspended or killed, such that another job can run. Preemption is disabled by the default configuration. Configuration file examples disable preemption, but contain directions for enabling preemption.

Job Suspension

As new jobs are submitted that receive a higher priority than currently executing jobs, the executing jobs may be preempted. If the preempted jobs are not capable of writing checkpoints, they lose whatever forward progress they have made, and are sent back to the job queue to await starting over again as another machine becomes available. An alternative to this is to use suspension to freeze the job while some other task runs, and then unfreeze it so that it can continue on from where it left off. This does not require any special handling in the job, unlike most strategies that take checkpoints. However, it does require a special configuration of HTCondor. This example implements a policy that allows the job to decide whether it should be evicted or suspended. The jobs announce their choice through the use of the invented job ClassAd attribute IsSuspendableJob, that is also utilized in the configuration.

The implementation of this policy utilizes two categories of slots, identified as suspendable or nonsuspendable. A job identifies which category of slot it wishes to run on. This affects two aspects of the policy:

  • Of two jobs that might run on a slot, which job is chosen. The four cases that may occur depend on whether the currently running job identifies itself as suspendable or nonsuspendable, and whether the potentially running job identifies itself as suspendable or nonsuspendable.

    1. If the currently running job is one that identifies itself as suspendable, and the potentially running job identifies itself as nonsuspendable, the currently running job is suspended, in favor of running the nonsuspendable one. This occurs independent of the user priority of the two jobs.

    2. If both the currently running job and the potentially running job identify themselves as suspendable, then the relative priorities of the users and the preemption policy determines whether the new job will replace the existing job.

    3. If both the currently running job and the potentially running job identify themselves as nonsuspendable, then the relative priorities of the users and the preemption policy determines whether the new job will replace the existing job.

    4. If the currently running job is one that identifies itself as nonsuspendable, and the potentially running job identifies itself as suspendable, the currently running job continues running.

  • What happens to a currently running job that is preempted. A job that identifies itself as suspendable will be suspended, which means it is frozen in place, and will later be unfrozen when the preempting job is finished. A job that identifies itself as nonsuspendable is evicted, giving it a chance to write a checkpoint, and then is killed. The job will return to the idle state in the job queue, and it can try to run again in the future.

# Lie to HTCondor, to achieve 2 slots for each real slot
# There is no good way to tell HTCondor that the two slots should be treated
# as though they share the same real memory, so lie about how much
# memory we have.

# Slots 1 through DETECTED_CORES are nonsuspendable and the rest are
# suspendable
IsSuspendableSlot = SlotID > $(DETECTED_CORES)

# If I am a suspendable slot, my corresponding nonsuspendable slot is
# my SlotID plus $(DETECTED_CORES)
NonSuspendableSlotState = eval(strcat("slot",SlotID-$(DETECTED_CORES),"_State")

# The above expression looks at slotX_State, so we need to add
# State to the list of slot attributes to advertise.

# For convenience, advertise these expressions in the machine ad.
STARTD_ATTRS = $(STARTD_ATTRS) IsSuspendableSlot NonSuspendableSlotState

MyNonSuspendableSlotIsIdle = \
  (NonSuspendableSlotState =!= "Claimed" && NonSuspendableSlotState =!= "Preempting")

# NonSuspendable slots are always willing to start jobs.
# Suspendable slots are only willing to start if the NonSuspendable slot is idle.
  IsSuspendableSlot!=True && IsSuspendableJob=!=True || \
  IsSuspendableSlot && IsSuspendableJob==True && $(MyNonSuspendableSlotIsIdle)

# Suspend the suspendable slot if the other slot is busy.
  IsSuspendableSlot && $(MyNonSuspendableSlotIsIdle)!=True



Note that in this example, the job ClassAd attribute IsSuspendableJob has no special meaning to HTCondor. It is an invented name chosen for this example. To take advantage of the policy, a job that wishes to be suspended must submit the job so that this attribute is defined. The following line should be placed in the job’s submit description file:

+IsSuspendableJob = True

Configuration for Interactive Jobs

Policy may be set based on whether a job is an interactive one or not. Each interactive job has the job ClassAd attribute

InteractiveJob = True

and this may be used to identify interactive jobs, distinguishing them from all other jobs.

As an example, presume that slot 1 prefers interactive jobs. Set the machine’s RANK to show the preference:

RANK = ( (MY.SlotID == 1) && (TARGET.InteractiveJob =?= True) )

Or, if slot 1 should be reserved for interactive jobs:

START = ( (MY.SlotID == 1) && (TARGET.InteractiveJob =?= True) )

Custom and system slot attributes

The condor_startd advertises one classad per slot to the condor_collector. Each of these ads has many attributes. See Machine ClassAd Attributes for the complete list of the attributes defined and used by the system. These attributes, and the custom attributes describe below, which can be used exactly as the predefined ones, have many uses. Let’s consider the machine classad attribute OpSysAndVer. This is a more specific version of the attribute OpSys, which will just be “Linux” on any Linux system, whereas OpSysAndVer might be CentOS8 on such a system.

First, we might use this attribute to examine one particular machine. We can use condor_status to query this attribute on a machine named “vulture” by running:

$ condor_status vulture -af OpSysAndVer

Or, we can use this attribute in job submit file to request that a job only run on machines with this OS version:

executable = my_program
Requirements = Target.OpSysAndVer == "CentOS8"
request_memory = 1G
request_disk   = 100M
request_cpus   = 1
should_transfer_files = yes
when_to_transfer_output = on_exit

Or, we can expand the value of this slot attribute into our job. Let’s say that we have two different programs compiled, one for CentOS8, named my_program.CentOS8, and another, compiled for CentOS9, named my_program.CentOS9. We can use $$ expansion in our submit file to select the correct program when the job is matched to a particular machine:

executable = my_program.$$(OpSysAndVer)
request_memory = 1G
request_disk   = 100M
request_cpus   = 1
should_transfer_files = yes
when_to_transfer_output = on_exit

Adding custom static attributes with STARTD_ATTRS

Administrators can add custom attributes to the slot classad that can be used exactly as the built-in ones, as demonstrated above. Let’s say that many jobs need an enormous genomic database, that has been pre-staged on a particular directory on some EPs. The EP administrator can advertise a custom slot classad attribute that names the file path to this database for jobs to use. This requires setting two EP configuration variables: STARTD_ATTRS, and a variable which defines the attributes itself. Let’s call the custom attribute we want to add GenomeDBPath. To set this to the string value of “/path/to/db”, we would first put GenomeDBPath into STARTD_ATTRS, then define GenomeDBPath itself as a configuration macro, like this in the EP configuration:

GenomeDBPath = "/path/to/db"

Note the convention to always append to any existing STARTD_ATTRS, so that multiple configuration files can compose together. Once this is done, and the condor_startd reconfigured, all slots on this EP will advertise this new attribute.

Beginning users may be tempted to hard-code, or assume the knowledge that certain well-known machines in their poool might have this database installed at some path. But, by advertising this value as a custom EP attribute, administrators have gained a level of indirection, and are free to move the database to a different path, or perhaps add machines to the pool without such database.

Configuring STARTD_ATTRS on a per-slot basis

The STARTD_ATTRS can be also configured on a per-slot basis. The condor_startd daemon builds the list of items to advertise by combining the lists in this order:



For example, consider the following configuration:

STARTD_ATTRS = favorite_color, favorite_season
SLOT1_STARTD_ATTRS = favorite_movie
SLOT2_STARTD_ATTRS = favorite_song

This will result in the condor_startd ClassAd for slot1 defining values for favorite_color, favorite_season, and favorite_movie. Slot2 will have values for favorite_color, favorite_season, and favorite_song.

Attributes themselves in the STARTD_ATTRS list can also be defined on a per-slot basis. Here is another example:

favorite_color = "blue"
favorite_season = "spring"
STARTD_ATTRS = favorite_color, favorite_season
SLOT2_favorite_color = "green"
SLOT3_favorite_season = "summer"

For this example, the condor_startd ClassAds are


favorite_color = "blue"
favorite_season = "spring"


favorite_color = "green"
favorite_season = "spring"


favorite_color = "blue"
favorite_season = "summer"

Startd Cron

Daemon ClassAd Hooks


The Startd Cron and Schedd Cron Daemon ClassAd Hooks mechanism are used to run executables (called jobs) directly from the condor_startd and condor_schedd daemons. The output from these jobs is incorporated into the machine ClassAd generated by the respective daemon. This mechanism and associated jobs have been identified by various names, including Startd Cron, dynamic attributes, and daemon hooks.

Pool management tasks can be enhanced by using a daemon’s ability to periodically run executables. The executables are expected to generate ClassAd attributes as their output; these ClassAds are then incorporated into the machine ClassAd. Policy expressions can then reference dynamic attributes (created by the ClassAd hook jobs) in the machine ClassAd.

Job output

The output of the job is incorporated into one or more ClassAds when the job exits. When the job outputs the special line:

- update:true

the output of the job is merged into all proper ClassAds, and an update goes to the condor_collector daemon.

If the job exits with a non-zero exit code, and the config knob STARTD_CRON_LOG_NON_ZERO_EXIT is true, any unparsed stdout and all of the standard error of the cron job will be logged in the StartLog file. This can be very useful for debugging.

It is possible for a Startd Cron job (but not a Schedd Cron job) to define multiple ClassAds, using the mechanism defined below:

  • An output line starting with '-' has always indicated end-of-ClassAd. The '-' can now be followed by a uniqueness tag to indicate the name of the ad that should be replaced by the new ad. This name is joined to the name of the Startd Cron job to produced a full name for the ad. This allows a single Startd Cron job to return multiple ads by giving each a unique name, and to replace multiple ads by using the same unique name as a previous invocation. The optional uniqueness tag can also be followed by the optional keyword update:<bool>, which can be used to override the Startd Cron configuration and suppress or force immediate updates.

    In other words, the syntax is:

    • [name ] [update: bool]

  • Each ad can contain one of four possible attributes to control what slot ads the ad is merged into when the condor_startd sends updates to the collector. These attributes are, in order of highest to lower priority (in other words, if SlotMergeConstraint matches, the other attributes are not considered, and so on):

    • SlotMergeConstraint expression: the current ad is merged into all slot ads for which this expression is true. The expression is evaluated with the slot ad as the TARGET ad.

    • SlotName|Name string: the current ad is merged into all slots whose Name attributes match the value of SlotName up to the length of SlotName.

    • SlotTypeId integer: the current ad is merged into all ads that have the same value for their SlotTypeId attribute.

    • SlotId integer: the current ad is merged into all ads that have the same value for their SlotID attribute.

For example, if the Startd Cron job returns:

- update:true

it will set Value=10 for all slots except slot1 and slot2. On those slots it will set Value=1 and Value=2 respectively. It will also send updates to the collector immediately.


Configuration variables related to Daemon ClassAd Hooks are defined in Configuration File Entries Relating to Daemon ClassAd Hooks: Startd Cron and Schedd Cron

Here is a complete configuration example. It defines all three of the available types of jobs: ones that use the condor_startd, benchmark jobs, and ones that use the condor_schedd.

# Startd Cron Stuff
# helper variable to use in identifying locations of files
MODULES = $(ROOT)/modules

STARTD_CRON_CONFIG_VAL = $(RELEASE_DIR)/bin/condor_config_val

# If something goes wrong, and a start cron exits with non-zero
# log the stdout/stderr to the StartLog


# Test job

# job 'date'

# Job 'foo'

# Benchmark Stuff

# MIPS benchmark

# KFLOPS benchmark

# Schedd Cron Stuff. Unlike the Startd,
# a restart of the Schedd is required for changes to take effect
SCHEDD_CRON_CONFIG_VAL = $(RELEASE_DIR)/bin/condor_config_val

# Test job

Container/VM support: Docker, Apptainer, Singularity and Xen/VMware

Docker Universe

The execution of a docker universe job causes the instantiation of a Docker container on an execute host.

The docker universe job is mapped to a vanilla universe job, and the submit description file must specify the submit command docker_image to identify the Docker image. The job’s requirement ClassAd attribute is automatically appended, such that the job will only match with an execute machine that has Docker installed.

The Docker service must be pre-installed on each execute machine that can execute a docker universe job. Upon start up of the condor_startd daemon, the capability of the execute machine to run docker universe jobs is probed, and the machine ClassAd attribute HasDocker is advertised for a machine that is capable of running Docker universe jobs.

When a docker universe job is matched with a Docker-capable execute machine, HTCondor invokes the Docker CLI to instantiate the image-specific container. The job’s scratch directory tree is mounted as a Docker volume. When the job completes, is put on hold, or is evicted, the container is removed.

An administrator of a machine can optionally make additional directories on the host machine readable and writable by a running container. To do this, the admin must first give an HTCondor name to each directory with the DOCKER_VOLUMES parameter. Then, each volume must be configured with the path on the host OS with the DOCKER_VOLUME_DIR_XXX parameter. Finally, the parameter DOCKER_MOUNT_VOLUMES tells HTCondor which of these directories to always mount onto containers running on this machine.

For example,


The condor_startd will advertise which docker volumes it has available for mounting with the machine attributes HasDockerVolumeSOME_NAME = true so that jobs can match to machines with volumes they need.

Optionally, if the directory name is two directories, separated by a colon, the first directory is the name on the host machine, and the second is the value inside the container. If a “:ro” is specified after the second directory name, the volume will be mounted read-only inside the container.

These directories will be bind-mounted unconditionally inside the container. If an administrator wants to bind mount a directory only for some jobs, perhaps only those submitted by some trusted user, the setting DOCKER_VOLUME_DIR_xxx_MOUNT_IF may be used. This is a class ad expression, evaluated in the context of the job ad and the machine ad. Only when it evaluted to TRUE, is the volume mounted. Extending the above example,

DOCKER_VOLUME_DIR_SOME_DIR_MOUNT_IF = WantSomeDirMounted && Owner == "smith"

In this case, the directory /path1 will get mounted inside the container only for jobs owned by user “smith”, and who set +WantSomeDirMounted = true in their submit file.

In addition to installing the Docker service, the single configuration variable DOCKER must be set. It defines the location of the Docker CLI and can also specify that the condor_starter daemon has been given a password-less sudo permission to start the container as root. Details of the DOCKER configuration variable are in the condor_startd Configuration File Macros section.

Docker must be installed as root by following these steps on an Enterprise Linux machine.

  1. Acquire and install the docker-engine community edition by following the installations instructions from

  2. Set up the groups:

    $ usermod -aG docker condor
  3. Invoke the docker software:

    $ systemctl start docker
    $ systemctl enable docker
  4. Reconfigure the execute machine, such that it can set the machine ClassAd attribute HasDocker:

    $ condor_reconfig
  5. Check that the execute machine properly advertises that it is docker-capable with:

    $ condor_status -l | grep -i docker

    The output of this command line for a correctly-installed and docker-capable execute host will be similar to

    HasDocker = true
    DockerVersion = "Docker Version 1.6.0, build xxxxx/1.6.0"

By default, HTCondor will keep the 8 most recently used Docker images on the local machine. This number may be controlled with the configuration variable DOCKER_IMAGE_CACHE_SIZE, to increase or decrease the number of images, and the corresponding disk space, used by Docker.

By default, Docker containers will be run with all rootly capabilities dropped, and with setuid and setgid binaries disabled, for security reasons. If you need to run containers with root privilege, you may set the configuration parameter DOCKER_DROP_ALL_CAPABILITIES to an expression that evaluates to false. This expression is evaluted in the context of the machine ad (my) and the job ad (target).

Docker support an enormous number of command line options when creating containers. While HTCondor tries to map as many useful options from submit files and machine descriptions to command line options, an administrator may want additional options passed to the docker container create command. To do so, the parameter DOCKER_EXTRA_ARGUMENTS can be set, and condor will append these to the docker container create command.

Docker universe jobs may fail to start on certain Linux machines when SELinux is enabled. The symptom is a permission denied error when reading or executing from the condor scratch directory. To fix this problem, an administrator will need to run the following command as root on the execute directories for all the startd machines:

$ chcon -Rt svirt_sandbox_file_t /var/lib/condor/execute

All docker universe jobs can request either host-based networking or no networking at all. The latter might be for security reasons. If the worker node administrator has defined additional custom docker networks, perhaps a VPN or other custom type, those networks can be defined for HTCondor jobs to opt into with the docker_network_type submit command. Simple set

DOCKER_NETWORKS = some_virtual_network, another_network

And these two networks will be advertised by the startd, and jobs that request these network type will only match to machines that support it. Note that HTCondor cannot test the validity of these networks, and merely trusts that the administrator has correctly configured them.

To deal with a potentially user influencing option, there is an optional knob that can be configured to adapt the --shm-size Docker container create argument taking the machine’s and job’s classAds into account. Exemplary, setting the /dev/shm size to half the requested memory is achieved by:

DOCKER_SHM_SIZE = Memory * 1024 * 1024 / 2

or, using a user provided value DevShmSize if available and within the requested memory limit:

DOCKER_SHM_SIZE = ifThenElse(DevShmSize isnt Undefined && isInteger(DevShmSize) && int(DevShmSize) <= (Memory * 1024 * 1024), int(DevShmSize), 2 * 1024 * 1024 * 1024)

Note: Memory is in MB, thus it needs to be scaled to bytes.

Apptainer and Singularity Support

Singularity ( is a container runtime system popular in scientific and HPC communities. Apptainer ( is an open source fork of Singularity that is API and CLI compatible with singularity. HTCondor can run jobs inside Singularity containers either in a transparent way, where the job does not know that it is being contained, or, the HTCondor administrator can configure the HTCondor startd so that a job can opt into running inside a container. This allows the operating system that the job sees to be different than the one on the host system, and provides more isolation between processes running in one job and another.


Everything in this document that pertains to Singularity is also true for the Apptainer container runtime.

The decision to run a job inside Singularity ultimately resides on the worker node, although it can delegate that to the job.

By default, jobs will not be run in Singularity.

For Singularity to work, the administrator must install Singularity on the worker node. The HTCondor startd will detect this installation at startup. When it detects a usable installation, it will advertise two attributes in the slot ad:

HasSingularity = true
SingularityVersion = "singularity version 3.7.0-1.el7"

If the detected Singularity installation fails to run test containers at startd startup, HasSingularity will be set to false, and the slot ad attribute SingularityOfflineReason will contain an error string.

HTCondor will run a job under Singularity when the startd configuration knob SINGULARITY_JOB evaluates to true. This is evaluated in the context of the slot ad and the job ad. If it evaluates to false or undefined, the job will run as normal, without singularity.

When SINGULARITY_JOB evaluates to true, a second HTCondor knob is required to name the singularity image that must be run, SINGULARITY_IMAGE_EXPR. This also is evaluated in the context of the machine and the job ad, and must evaluate to a string. This image name is passed to the singularity exec command, and can be any valid value for a singularity image name. So, it may be a path to file on a local file system that contains an singularity image, in any format that singularity supports. It may be a string that begins with docker://, and refer to an image located on docker hub, or other repository. It can begin with http://, and refer to an image to be fetched from an HTTP server. In this case, singularity will fetch the image into the job’s scratch directory, convert it to a .sif file and run it from there. Note this may require the job to request more disk space that it otherwise would need. It can be a relative path, in which case it refers to a file in the scratch directory, so that the image can be transferred by HTCondor’s file transfer mechanism.

Here’s the simplest possible configuration file. It will force all jobs on this machine to run under Singularity, and to use an image that it located in the file system in the path /cvfms/

# Forces _all_ jobs to run inside singularity.

# Forces all jobs to use the CernVM-based image.

Another common configuration is to allow the job to select whether to run under Singularity, and if so, which image to use. This looks like:

SINGULARITY_JOB = !isUndefined(TARGET.SingularityImage)

Then, users would add the following to their submit file (note the quoting):

+SingularityImage = "/cvmfs/"

or maybe

+SingularityImage = "docker://ubuntu:20"

By default, singularity will bind mount the scratch directory that contains transferred input files, working files, and other per-job information into the container, and make this the initial working directory of the job. Thus, file transfer for singularity jobs works just like with vanilla universe jobs. Any new files the job writes to this directory will be copied back to the submit node, just like any other sandbox, subject to transfer_output_files, as in vanilla universe.

Assuming singularity is configured on the startd as described above, A complete submit file that uses singularity might look like

executable = /usr/bin/sleep
arguments = 30
+SingularityImage = "docker://ubuntu"

Requirements = HasSingularity

Request_Disk = 1024
Request_Memory = 1024
Request_cpus = 1

should_transfer_files = yes
transfer_input_files = some_input
when_to_transfer_output = on_exit

log = log
output = out.$(PROCESS)
error = err.$(PROCESS)

queue 1

HTCondor can also transfer the whole singularity image, just like any other input file, and use that as the container image. Given a singularity image file in the file named “image” in the submit directory, the submit file would look like:

executable = /usr/bin/sleep
arguments = 30
+SingularityImage = "image"

Requirements = HasSingularity

Request_Disk = 1024
Request_Memory = 1024
Request_cpus = 1

should_transfer_files = yes
transfer_input_files = image
when_to_transfer_output = on_exit

log = log
output = out.$(PROCESS)
error = err.$(PROCESS)

queue 1

The administrator can optionally specify additional directories to be bind mounted into the container. For example, if there is some common shared input data located on a machine, or on a shared file system, this directory can be bind-mounted and be visible inside the container. This is controlled by the configuration parameter SINGULARITY_BIND_EXPR. This is an expression, which is evaluated in the context of the machine and job ads, and which should evaluated to a string which contains a space separated list of directories to mount.

So, to always bind mount a directory named /nfs into the image, and administrator could set


Or, if a trusted user is allowed to bind mount anything on the host, an expression could be

SINGULARITY_BIND_EXPR = (Target.Owner == "TrustedUser") ? SomeExpressionFromJob : ""

If the source directory for the bind mount is missing on the host machine, HTCondor will skip that mount and run the job without it. If the image is an exploded file directory, and the target directory is missing inside the image, and the configuration parameter SINGULARITY_IGNORE_MISSING_BIND_TARGET is set to true (the default is false), then this mount attempt will also be skipped. Otherwise, the job will return an error when run.

In general, HTCondor will try to set as many Singularity command line options as possible from settings in the machine ad and job ad, as make sense. For example, if the slot the job runs in is provisioned with GPUs, perhaps in response to a request_GPUs line in the submit file, the Singularity flag -nv will be passed to Singularity, which should make the appropriate nvidia devices visible inside the container. If the submit file requests environment variables to be set for the job, HTCondor passes those through Singularity into the job.

Before the condor_starter runs a job with singularity, it first runs singularity test on that image. If no test is defined inside the image, it runs /bin/sh /bin/true. If the test returns non-zero, for example if the image is missing, or malformed, the job is put on hold. This is controlled by the condor knob SINGULARITY_RUN_TEST_BEFORE_JOB, which defaults to true.

If an administrator wants to pass additional arguments to the singularity exec command instead of the defaults used by HTCondor, several parameters exist to do this - see the condor_starter configuration parameters that begin with the prefix SINGULARITY in defined in section condor_starter Configuration File Entries. There you will find parameters to customize things such as the use of PID namespaces, cache directory, and several other options. However, should an administrator need to customize Singularity behavior that HTCondor does not currently support, the parameter SINGULARITY_EXTRA_ARGUMENTS allows arbitrary additional parameters to be passed to the singularity exec command. Note that this can be a classad expression, evaluated in the context of the slot ad and the job ad, where the slot ad can be referenced via “MY.”, and the job ad via the “TARGET.” reference. In this way, the admin could set different options for different kinds of jobs. For example, to pass the -w argument, to make the image writable, an administrator could set


There are some rarely-used settings that some administrators may need to set. By default, HTCondor looks for the Singularity runtime in /usr/bin/singularity, but this can be overridden with the SINGULARITY parameter:

SINGULARITY = /opt/singularity/bin/singularity

By default, the initial working directory of the job will be the scratch directory, just like a vanilla universe job. This directory probably doesn’t exist in the image’s file system. Usually, Singularity will be able to create this directory in the image, but unprivileged versions of singularity with certain image types may not be able to do so. If this is the case, the current directory on the inside of the container can be set via a knob. This will still map to the scratch directory outside the container.

# Maps $_CONDOR_SCRATCH_DIR on the host to /srv inside the image.

If SINGULARITY_TARGET_DIR is not specified by the admin, it may be specified in the job submit file via the submit command container_target_dir. If both are set, the config knob version takes precedence.

When the HTCondor starter runs a job under Singularity, it always prints to the log the exact command line used. This can be helpful for debugging or for the curious. An example command line printed to the StarterLog might look like the following:

About to exec /usr/bin/singularity -s exec -S /tmp -S /var/tmp --pwd /execute/dir_462373 -B /execute/dir_462373 --no-home -C /images/debian /execute/dir_462373/demo 3

In this example, no GPUs have been requested, so there is no -nv option. MOUNT_UNDER_SCRATCH is set to the default of /tmp,/var/tmp, so condor translates those into -S (scratch directory) requests in the command line. The --pwd is set to the scratch directory, -B bind mounts the scratch directory with the same name on the inside of the container, and the -C option is set to contain all namespaces. Then the image is named, and the executable, which in this case has been transferred by HTCondor into the scratch directory, and the job’s argument (3). Not visible in the log are any environment variables that HTCondor is setting for the job.

All of the singularity container runtime’s logging, warning and error messages are written to the job’s stderr. This is an unfortunate aspect of the runtime we hope to fix in the future. By default, HTCondor passes “-s” (silent) to the singularity runtime, so that the only messages it writes to the job’s stderr are fatal error messages. If a worker node administrator needs more debugging information, they can change the value of the worker node config parameter SINGULARITY_VERBOSITY and set it to -d or -v to increase the debugging level.

The VM Universe

vm universe jobs may be executed on any execution site with Xen (via libvirt) or KVM. To do this, HTCondor must be informed of some details of the virtual machine installation, and the execution machines must be configured correctly.

What follows is not a comprehensive list of the options that help set up to use the vm universe; rather, it is intended to serve as a starting point for those users interested in getting vm universe jobs up and running quickly. Details of configuration variables are in the Configuration File Entries Relating to Virtual Machines section.

Begin by installing the virtualization package on all execute machines, according to the vendor’s instructions. We have successfully used Xen and KVM.

For Xen, there are three things that must exist on an execute machine to fully support vm universe jobs.

  1. A Xen-enabled kernel must be running. This running Xen kernel acts as Dom0, in Xen terminology, under which all VMs are started, called DomUs Xen terminology.

  2. The libvirtd daemon must be available, and Xend services must be running.

  3. The pygrub program must be available, for execution of VMs whose disks contain the kernel they will run.

For KVM, there are two things that must exist on an execute machine to fully support vm universe jobs.

  1. The machine must have the KVM kernel module installed and running.

  2. The libvirtd daemon must be installed and running.

Configuration is required to enable the execution of vm universe jobs. The type of virtual machine that is installed on the execute machine must be specified with the VM_TYPE variable. For now, only one type can be utilized per machine. For instance, the following tells HTCondor to use KVM:

VM_TYPE = kvm

The location of the condor_vm-gahp and its log file must also be specified on the execute machine. On a Windows installation, these options would look like this:

VM_GAHP_SERVER = $(SBIN)/condor_vm-gahp.exe

Xen-Specific and KVM-Specific Configuration

Once the configuration options have been set, restart the condor_startd daemon on that host. For example:

$ condor_restart -startd leovinus

The condor_startd daemon takes a few moments to exercise the VM capabilities of the condor_vm-gahp, query its properties, and then advertise the machine to the pool as VM-capable. If the set up succeeded, then condor_status will reveal that the host is now VM-capable by printing the VM type and the version number:

$ condor_status -vm leovinus

After a suitable amount of time, if this command does not give any output, then the condor_vm-gahp is having difficulty executing the VM software. The exact cause of the problem depends on the details of the VM, the local installation, and a variety of other factors. We can offer only limited advice on these matters:

For Xen and KVM, the vm universe is only available when root starts HTCondor. This is a restriction currently imposed because root privileges are required to create a virtual machine on top of a Xen-enabled kernel. Specifically, root is needed to properly use the libvirt utility that controls creation and management of Xen and KVM guest virtual machines. This restriction may be lifted in future versions, depending on features provided by the underlying tool libvirt.

When a vm Universe Job Fails to Start

If a vm universe job should fail to launch, HTCondor will attempt to distinguish between a problem with the user’s job description, and a problem with the virtual machine infrastructure of the matched machine. If the problem is with the job, the job will go on hold with a reason explaining the problem. If the problem is with the virtual machine infrastructure, HTCondor will reschedule the job, and it will modify the machine ClassAd to prevent any other vm universe job from matching. vm universe configuration is not slot-specific, so this change is applied to all slots.

When the problem is with the virtual machine infrastructure, these machine ClassAd attributes are changed:

Since condor_submit adds HasVM == True to a vm universe job’s requirements, no further vm universe jobs will match.

Once any problems with the infrastructure are fixed, to change the machine ClassAd attributes such that the machine will once again match to vm universe jobs, an administrator has three options. All have the same effect of setting the machine ClassAd attributes to the correct values such that the machine will not reject matches for vm universe jobs.

  1. Restart the condor_startd daemon.

  2. Submit a vm universe job that explicitly matches the machine. When the job runs, the code detects the running job and causes the attributes related to the vm universe to be set indicating that vm universe jobs can match with this machine.

  3. Run the command line tool condor_update_machine_ad to set machine ClassAd attribute HasVM to True, and this will cause the other attributes related to the vm universe to be set indicating that vm universe jobs can match with this machine. See the condor_update_machine_ad manual page for examples and details.

Configuring GPUs

HTCondor supports incorporating GPU resources and making them available for jobs. First, GPUs must be detected as available resources. Then, machine ClassAd attributes advertise this availability. Both detection and advertisement are accomplished by having this configuration for each execute machine that has GPUs:

use feature : GPUs

Use of this configuration template invokes the condor_gpu_discovery tool to create a custom resource, with a custom resource name of GPUs, and it generates the ClassAd attributes needed to advertise the GPUs. condor_gpu_discovery is invoked in a mode that discovers and advertises both CUDA and OpenCL GPUs.

This configuration template refers to macro GPU_DISCOVERY_EXTRA, which can be used to define additional command line arguments for the condor_gpu_discovery tool. For example, setting

use feature : GPUs

causes the condor_gpu_discovery tool to output more attributes that describe the detected GPUs on the machine.

condor_gpu_discovery defaults to using nested ClassAds for GPU properties. The administrator can be explicit about which form to use for properties by adding either the -nested or -not-nested option to GPU_DISCOVERY_EXTRA.

The format – nested or not – of GPU properties in the slot ad is the same as published by condor_gpu_discovery. The use of nested GPU property ads is necessary to do GPU matchmaking and to properly support heterogeneous GPUs.

For resources like GPUs that have individual properties, when configuring slots the slot configuration can specify a constraint on those properties for the purpose of choosing which GPUs are assigned to which slots. This serves the same purpose as the require_gpus submit keyword, but in this case it controls the slot configuration on startup.

The resource constraint can be specified by following the resource quantity with a colon and then a constraint expression. The constraint expression can refer to resource property attributes like the GPU properties from condor_gpu_discovery -nested output. If the constraint expression is a string literal, it will be matched automatically against the resource id, otherwise it will be evaluated against each of the resource property ads.

When using resource constraints, it is recommended that you put each resource quantity on a separate line as in the following example, otherwise the constraint expression may be truncated.

# Assuming a machine that has two types of GPUs, 2 of which have Capability 8.0
# and the remaining GPUs are less powerful

# declare a partitionable slot that has the 2 powerful GPUs
# and 90% of the other resources:
SLOT_TYPE_1 @=slot
   GPUs = 2 : Capability >= 8.0

# declare a small static slot and assign it a specific GPU by id
SLOT_TYPE_2 @=slot
   GPUs = 1 : "GPU-6a96bd13"
   CPUs = 1
           Memory = 10

# declare two static slots that split up the remaining resources which may or may not include GPUs
SLOT_TYPE_3 = auto

condor_negotiator-Side Resource Consumption Policies


Consumption policies are an experimental feature and may not work well in combination with other HTCondor features.

For partitionable slots, the specification of a consumption policy permits matchmaking at the negotiator. A dynamic slot carved from the partitionable slot acquires the required quantities of resources, leaving the partitionable slot with the remainder. This differs from scheduler matchmaking in that multiple jobs can match with the partitionable slot during a single negotiation cycle.

All specification of the resources available is done by configuration of the partitionable slot. The machine is identified as having a resource consumption policy enabled with


A defined slot type that is partitionable may override the machine value with


A job seeking a match may always request a specific number of cores, amount of memory, and amount of disk space. Availability of these three resources on a machine and within the partitionable slot is always defined and have these default values:

CONSUMPTION_CPUS = quantize(target.RequestCpus,{1})
CONSUMPTION_MEMORY = quantize(target.RequestMemory,{128})
CONSUMPTION_DISK = quantize(target.RequestDisk,{1024})

Here is an example-driven definition of a consumption policy. Assume a single partitionable slot type on a multi-core machine with 8 cores, and that the resource this policy cares about allocating are the cores. Configuration for the machine includes the definition of the slot type and that it is partitionable.

SLOT_TYPE_1 = cpus=8

Enable use of the condor_negotiator-side resource consumption policy, allocating the job-requested number of cores to the dynamic slot, and use SLOT_WEIGHT to assess the user usage that will affect user priority by the number of cores allocated. Note that the only attributes valid within the SLOT_WEIGHT expression are Cpus, Memory, and disk. This must the set to the same value on all machines in the pool.


If custom resources are available within the partitionable slot, they may be used in a consumption policy, by specifying the resource. Using a machine with 4 GPUs as an example custom resource, define the resource and include it in the definition of the partitionable slot:

SLOT_TYPE_2 = cpus=8, gpus=4

Add the consumption policy to incorporate availability of the GPUs:


Power Management

HTCondor supports placing machines in low power states. A machine in the low power state is identified as being offline. Power setting decisions are based upon HTCondor configuration.

Power conservation is relevant when machines are not in heavy use, or when there are known periods of low activity within the pool.

Entering a Low Power State

By default, HTCondor does not do power management. When desired, the ability to place a machine into a low power state is accomplished through configuration. This occurs when all slots on a machine agree that a low power state is desired.

A slot’s readiness to hibernate is determined by the evaluating the HIBERNATE configuration variable (see the condor_startd Configuration File Macros section) within the context of the slot. Readiness is evaluated at fixed intervals, as determined by the HIBERNATE_CHECK_INTERVAL configuration variable. A non-zero value of this variable enables the power management facility. It is an integer value representing seconds, and it need not be a small value. There is a trade off between the extra time not at a low power state and the unnecessary computation of readiness.

To put the machine in a low power state rapidly after it has become idle, consider checking each slot’s state frequently, as in the example configuration:


This checks each slot’s readiness every 20 seconds. A more common value for frequency of checks is 300 (5 minutes). A value of 300 loses some degree of granularity, but it is more reasonable as machines are likely to be put in to a low power state after a few hours, rather than minutes.

A slot’s readiness or willingness to enter a low power state is determined by the HIBERNATE expression. Because this expression is evaluated in the context of each slot, and not on the machine as a whole, any one slot can veto a change of power state. The HIBERNATE expression may reference a wide array of variables. Possibilities include the change in power state if none of the slots are claimed, or if the slots are not in the Owner state.

Here is a concrete example. Assume that the START expression is not set to always be True. This permits an easy determination whether or not the machine is in an Unclaimed state through the use of an auxiliary macro called ShouldHibernate.

TimeToWait  = (2 * $(HOUR))
ShouldHibernate = ( (KeyboardIdle > $(StartIdleTime)) \
                    && $(CPUIdle) \
                    && ($(StateTimer) > $(TimeToWait)) )

This macro evaluates to True if the following are all True:

  • The keyboard has been idle long enough.

  • The CPU is idle.

  • The slot has been Unclaimed for more than 2 hours.

The sample HIBERNATE expression that enters the power state called “RAM”, if ShouldHibernate evaluates to True, and remains in its current state otherwise is

HibernateState  = "RAM"
HIBERNATE = ifThenElse($(ShouldHibernate), $(HibernateState), "NONE" )

If any slot returns “NONE”, that slot vetoes the decision to enter a low power state. Only when values returned by all slots are all non-zero is there a decision to enter a low power state. If all agree to enter the low power state, but differ in which state to enter, then the largest magnitude value is chosen.

Returning From a Low Power State

The HTCondor command line tool condor_power may wake a machine from a low power state by sending a UDP Wake On LAN (WOL) packet. See the condor_power manual page.

To automatically call condor_power under specific conditions, condor_rooster may be used. The configuration options for condor_rooster are described in the condor_rooster Configuration File Macros section.

Keeping a ClassAd for a Hibernating Machine

A pool’s condor_collector daemon can be configured to keep a persistent ClassAd entry for each machine, once it has entered hibernation. This is required by condor_rooster so that it can evaluate the UNHIBERNATE expression of the offline machines.

To do this, define a log file using the COLLECTOR_PERSISTENT_AD_LOG configuration variable. See the condor_startd Configuration File Macros section for the definition. An optional expiration time for each ClassAd can be specified with OFFLINE_EXPIRE_ADS_AFTER. The timing begins from the time the hibernating machine’s ClassAd enters the condor_collector daemon. See the condor_startd Configuration File Macros section for the definition.

Linux Platform Details

Depending on the Linux distribution and version, there are three methods for controlling a machine’s power state. The methods:

  1. pm-utils is a set of command line tools which can be used to detect and switch power states. In HTCondor, this is defined by the string “pm-utils”.

  2. The directory in the virtual file system /sys/power contains virtual files that can be used to detect and set the power states. In HTCondor, this is defined by the string “/sys”.

  3. The directory in the virtual file system /proc/acpi contains virtual files that can be used to detect and set the power states. In HTCondor, this is defined by the string “/proc”.

By default, the HTCondor attempts to detect the method to use in the order shown. The first method detected as usable on the system is chosen.

This ordered detection may be bypassed, to use a specified method instead by setting the configuration variable LINUX_HIBERNATION_METHOD with one of the defined strings. This variable is defined in the condor_startd Configuration File Macros section. If no usable methods are detected or the method specified by LINUX_HIBERNATION_METHOD is either not detected or invalid, hibernation is disabled.

The details of this selection process, and the final method selected can be logged via enabling D_FULLDEBUG in the relevant subsystem’s log configuration.

Windows Platform Details

If after a suitable amount of time, a Windows machine has not entered the expected power state, then HTCondor is having difficulty exercising the operating system’s low power capabilities. While the cause will be specific to the machine’s hardware, it may also be due to improperly configured software. For hardware difficulties, the likely culprit is the configuration within the machine’s BIOS, for which HTCondor can offer little guidance. For operating system difficulties, the powercfg tool can be used to discover the available power states on the machine. The following command demonstrates how to list all of the supported power states of the machine:

> powercfg -A
The following sleep states are available on this system:
Standby (S3) Hibernate Hybrid Sleep
The following sleep states are not available on this system:
Standby (S1)
        The system firmware does not support this standby state.
Standby (S2)
        The system firmware does not support this standby state.

Note that the HIBERNATE expression is written in terms of the Sn state, where n is the value evaluated from the expression.

This tool can also be used to enable and disable other sleep states. This example turns hibernation on.

> powercfg -h on

If this tool is insufficient for configuring the machine in the manner required, the Power Options control panel application offers the full extent of the machine’s power management abilities.


A hook is an external program or script invoked by an HTCondor daemon to change its behavior or implement some policy. There are three kinds of Job hooks in HTCondor: Fetch work job hooks, Prepare Job hooks, and Job Router hooks.

Job Hooks That Fetch Work

In the past, HTCondor has always sent work to the execute machines by pushing jobs to the condor_startd daemon from the condor_schedd daemon. Beginning with the HTCondor version 7.1.0, the condor_startd daemon now has the ability to pull work by fetching jobs via a system of plug-ins or hooks. Any site can configure a set of hooks to fetch work, completely outside of the usual HTCondor matchmaking system.

A projected use of the hook mechanism implements what might be termed a glide-in factory, especially where the factory is behind a firewall. Without using the hook mechanism to fetch work, a glide-in condor_startd daemon behind a firewall depends on CCB to help it listen and eventually receive work pushed from elsewhere. With the hook mechanism, a glide-in condor_startd daemon behind a firewall uses the hook to pull work. The hook needs only an outbound network connection to complete its task, thereby being able to operate from behind the firewall, without the intervention of CCB.

Periodically, each execution slot managed by a condor_startd will invoke a hook to see if there is any work that can be fetched. Whenever this hook returns a valid job, the condor_startd will evaluate the current state of the slot and decide if it should start executing the fetched work. If the slot is unclaimed and the Start expression evaluates to True, a new claim will be created for the fetched job. If the slot is claimed, the condor_startd will evaluate the Rank expression relative to the fetched job, compare it to the value of Rank for the currently running job, and decide if the existing job should be preempted due to the fetched job having a higher rank. If the slot is unavailable for whatever reason, the condor_startd will refuse the fetched job and ignore it. Either way, once the condor_startd decides what it should do with the fetched job, it will invoke another hook to reply to the attempt to fetch work, so that the external system knows what happened to that work unit.

If the job is accepted, a claim is created for it and the slot moves into the Claimed state. As soon as this happens, the condor_startd will spawn a condor_starter to manage the execution of the job. At this point, from the perspective of the condor_startd, this claim is just like any other. The usual policy expressions are evaluated, and if the job needs to be suspended or evicted, it will be. If a higher-ranked job being managed by a condor_schedd is matched with the slot, that job will preempt the fetched work.

The condor_starter itself can optionally invoke additional hooks to help manage the execution of the specific job. There are hooks to prepare or validate the execution environment for the job, periodically update information about the job as it runs, notify when the job exits, and to take special actions when the job is being evicted.

Assuming there are no interruptions, the job completes, and the condor_starter exits, the condor_startd will invoke the hook to fetch work again. If another job is available, the existing claim will be reused and a new condor_starter is spawned. If the hook returns that there is no more work to perform, the claim will be evicted, and the slot will return to the Owner state.

To aid with the development and debugging of hooks, output sent to stderr by the hooks will be preserved in daemon logs of either the condor_starter or condor_startd as appropriate.

Work Fetching Hooks Invoked by HTCondor

There are a handful of hooks invoked by HTCondor related to fetching work, some of which are called by the condor_startd and others by the condor_starter. Each hook is described, including when it is invoked, what task it is supposed to accomplish, what data is passed to the hook, what output is expected, and, when relevant, the exit status expected.

  • The hook defined by the configuration variable <Keyword>_HOOK_FETCH_WORK is invoked whenever the condor_startd wants to see if there is any work to fetch. There is a related configuration variable called FetchWorkDelay which determines how long the condor_startd will wait between attempts to fetch work, which is described in detail in Job Hooks That Fetch Work. <Keyword>_HOOK_FETCH_WORK is the most important hook in the whole system, and is the only hook that must be defined for any of the other condor_startd hooks to operate.

    Command-line arguments passed to the hook


    Standard input given to the hook

    ClassAd of the slot that is looking for work.

    Expected standard output from the hook

    ClassAd of a job that can be run. If there is no work, the hook should return no output.

    User id that the hook runs as

    <Keyword>_HOOK_FETCH_WORK hook runs with the same privileges as the condor_startd. When Condor was started as root, this is usually the condor user, or the user specified in the CONDOR_IDS configuration variable.

    Exit status of the hook


    The job ClassAd returned by the hook needs to contain enough information for the condor_starter to eventually spawn the work. The required and optional attributes in this ClassAd are listed here:

    Attributes for a FetchWork application are either required or optional. The following attributes are required:


    This attribute defines the full path to the executable program to be run as a FetchWork application. Since HTCondor does not currently provide any mechanism to transfer files on behalf of FetchWork applications, this path should be a valid path on the machine where the application will be run. It is a string attribute, and must therefore be enclosed in quotation marks (“). There is no default.


    If the condor_startd daemon is executing as root on the resource where a FetchWork application will run, the user must also define Owner to specify what user name the application will run as. On Windows, the condor_startd daemon always runs as an Administrator service, which is equivalent to running as root on Unix platforms. Owner must contain a valid user name on the given FetchWork resource. It is a string attribute, and must therefore be enclosed in quotation marks (“).


    Required when running on a condor_startd that uses partitionable slots. It specifies the number of CPU cores from the partitionable slot allocated for this job.


    Required when running on a condor_startd that uses partitionable slots. It specifies the disk space, in Megabytes, from the partitionable slot allocated for this job.


    Required when running on a condor_startd that uses partitionable slots. It specifies the memory, in Megabytes, from the partitionable slot allocated for this job.

    The following list of attributes are optional:


    This attribute defines what HTCondor job universe to use for the given FetchWork application. The only tested universes are vanilla and java. This attribute must be an integer, with vanilla using the value 5, and java using the value 10.


    IWD is an acronym for Initial Working Directory. It defines the full path to the directory where a given FetchWork application are to be run. Unless the application changes its current working directory, any relative path names used by the application will be relative to the IWD. If any other attributes that define file names (for example, In, Out, and so on) do not contain a full path, the IWD will automatically be pre-pended to those file names. It is a string attribute, and must therefore be enclosed in quotation marks (“). If the IWD is not specified, the temporary execution sandbox created by the condor_starter will be used as the initial working directory.


    This string defines the path to the file on the FetchWork resource that should be used as standard input (stdin) for the FetchWork application. This file (and all parent directories) must be readable by whatever user the FetchWork application will run as. If not specified, the default is /dev/null. It is a string attribute, and must therefore be enclosed in quotation marks (“).


    This string defines the path to the file on the FetchWork resource that should be used as standard output (stdout) for the FetchWork application. This file must be writable (and all parent directories readable) by whatever user the FetchWork application will run as. If not specified, the default is /dev/null. It is a string attribute, and must therefore be enclosed in quotation marks (“).


    This string defines the path to the file on the FetchWork resource that should be used as standard error (stderr) for the FetchWork application. This file must be writable (and all parent directories readable) by whatever user the FetchWork application will run as. If not specified, the default is /dev/null. It is a string attribute, and must therefore be enclosed in quotation marks (“).


    This string defines environment variables to set for a given FetchWork application. Each environment variable has the form NAME=value. Multiple variables are delimited with a semicolon. An example: Env = “PATH=/usr/local/bin:/usr/bin;TERM=vt100” It is a string attribute, and must therefore be enclosed in quotation marks (“).


    This string attribute defines the list of arguments to be supplied to the program on the command-line. The arguments are delimited (separated) by space characters. There is no default. If the JobUniverse corresponds to the Java universe, the first argument must be the name of the class containing main. It is a string attribute, and must therefore be enclosed in quotation marks (“).


    This string attribute is only used if JobUniverse is 10 (the Java universe). If a given FetchWork application is a Java program, specify the JAR files that the program requires with this attribute. There is no default. It is a string attribute, and must therefore be enclosed in quotation marks (“). Multiple file names may be delimited with either commas or white space characters, and therefore, file names can not contain spaces.


    This attribute specifies what signal should be sent whenever the HTCondor system needs to gracefully shutdown the FetchWork application. It can either be specified as a string containing the signal name (for example KillSig = “SIGQUIT”), or as an integer (KillSig = 3) The default is to use SIGTERM.


    This string specifies a file name for a log file that the condor_starter daemon can write with entries for relevant events in the life of a given FetchWork application. It is similar to the job event log file specified for regular HTCondor jobs with the log command in a submit description file. However, certain attributes that are placed in a job event log do not make sense in the FetchWork environment, and are therefore omitted. The default is not to write this log file. It is a string attribute, and must therefore be enclosed in quotation marks (“).


    If the StarterUserLog attribute is defined, the default format is a human-readable format. However, HTCondor can write out this log in an XML representation, instead. To enable the XML format for this job event log, the StarterUserLogUseXML boolean is set to TRUE. The default if not specified is FALSE.

    If any attribute that specifies a path (Cmd, In, Out,Err, StarterUserLog) is not a full path name, HTCondor automatically prepends the value of IWD.

  • The hook defined by the configuration variable <Keyword>_HOOK_REPLY_FETCH is invoked whenever <Keyword>_HOOK_FETCH_WORK returns data and the condor_startd decides if it is going to accept the fetched job or not.

    The condor_startd will not wait for this hook to return before taking other actions, and it ignores all output. The hook is simply advisory, and it has no impact on the behavior of the condor_startd.

    Command-line arguments passed to the hook

    Either the string accept or reject.

    Standard input given to the hook

    A copy of the job ClassAd and the slot ClassAd (separated by the string —– and a new line).

    Expected standard output from the hook


    User id that the hook runs as

    The <Keyword>_HOOK_REPLY_FETCH hook runs with the same privileges as the condor_startd. When Condor was started as root, this is usually the condor user, or the user specified in the CONDOR_IDS configuration variable.

    Exit status of the hook


  • The hook defined by the configuration variable <Keyword>_HOOK_EVICT_CLAIM is invoked whenever the condor_startd needs to evict a claim representing fetched work.

    The condor_startd will not wait for this hook to return before taking other actions, and ignores all output. The hook is simply advisory, and has no impact on the behavior of the condor_startd.

    Command-line arguments passed to the hook


    Standard input given to the hook

    A copy of the job ClassAd and the slot ClassAd (separated by the string —– and a new line).

    Expected standard output from the hook


    User id that the hook runs as

    The <Keyword>_HOOK_EVICT_CLAIM hook runs with the same privileges as the condor_startd. When Condor was started as root, this is usually the condor user, or the user specified in the CONDOR_IDS configuration variable.

    Exit status of the hook


Keywords to Define Job Fetch Hooks in the HTCondor Configuration files

Hooks are defined in the HTCondor configuration files by prefixing the name of the hook with a keyword. This way, a given machine can have multiple sets of hooks, each set identified by a specific keyword.

Each slot on the machine can define a separate keyword for the set of hooks that should be used with SLOT<N>_JOB_HOOK_KEYWORD. For example, on slot 1, the variable name will be called SLOT1_JOB_HOOK_KEYWORD. If the slot-specific keyword is not defined, the condor_startd will use a global keyword as defined by STARTD_JOB_HOOK_KEYWORD.

Once a job is fetched via <Keyword>_HOOK_FETCH_WORK, the condor_startd will insert the keyword used to fetch that job into the job ClassAd as HookKeyword. This way, the same keyword will be used to select the hooks invoked by the condor_starter during the actual execution of the job. The STARTER_DEFAULT_JOB_HOOK_KEYWORD config knob can define a default hook keyword to use in the event that keyword defined by the job is invalid or not specified. Alternatively, the STARTER_JOB_HOOK_KEYWORD can be defined to force the condor_starter to always use a given keyword for its own hooks, regardless of the value in the job ClassAd for the HookKeyword attribute.

For example, the following configuration defines two sets of hooks, and on a machine with 4 slots, 3 of the slots use the global keyword for running work from a database-driven system, and one of the slots uses a custom keyword to handle work fetched from a web service.

# Most slots fetch and run work from the database system.

# Slot4 fetches and runs work from a web service.

# The database system needs to both provide work and know the reply
# for each attempted claim.
DATABASE_HOOK_DIR = /usr/local/condor/fetch/database

# The web system only needs to fetch work.
WEB_HOOK_DIR = /usr/local/condor/fetch/web
WEB_HOOK_FETCH_WORK = $(WEB_HOOK_DIR)/fetch_work.php

The keywords "DATABASE" and "WEB" are completely arbitrary, so each site is encouraged to use different (more specific) names as appropriate for their own needs.

Defining the FetchWorkDelay Expression

There are two events that trigger the condor_startd to attempt to fetch new work:

  • the condor_startd evaluates its own state

  • the condor_starter exits after completing some fetched work

Even if a given compute slot is already busy running other work, it is possible that if it fetched new work, the condor_startd would prefer this newly fetched work (via the Rank expression) over the work it is currently running. However, the condor_startd frequently evaluates its own state, especially when a slot is claimed. Therefore, administrators can define a configuration variable which controls how long the condor_startd will wait between attempts to fetch new work. This variable is called FetchWorkDelay .

The FetchWorkDelay expression must evaluate to an integer, which defines the number of seconds since the last fetch attempt completed before the condor_startd will attempt to fetch more work. However, as a ClassAd expression (evaluated in the context of the ClassAd of the slot considering if it should fetch more work, and the ClassAd of the currently running job, if any), the length of the delay can be based on the current state the slot and even the currently running job.

For example, a common configuration would be to always wait 5 minutes (300 seconds) between attempts to fetch work, unless the slot is Claimed/Idle, in which case the condor_startd should fetch immediately:

FetchWorkDelay = ifThenElse(State == "Claimed" && Activity == "Idle", 0, 300)

If the condor_startd wants to fetch work, but the time since the last attempted fetch is shorter than the current value of the delay expression, the condor_startd will set a timer to fetch as soon as the delay expires.

If this expression is not defined, the condor_startd will default to a five minute (300 second) delay between all attempts to fetch work.

Job Hooks That Modify and Monitor Execution

The Job ClassAd can be modified before execution, and the progress of the job can be modified using hooks. These hooks are executed by the condor_starter and can be used with or without using Fetch Work hooks.

  • The hook defined by the configuration variable <Keyword>_HOOK_PREPARE_JOB_BEFORE_TRANSFER is invoked by the condor_starter immediately before transferring the job’s input files. This hook provides a chance to execute commands to set up or validate the job environment, and/or edit the job classad that is used by the condor_starter.

    The condor_starter waits until this hook returns before attempting to transfer the input files for the job. If the hook returns a non-zero exit status, the condor_starter will assume an error was reached while attempting to set up the job environment and abort the job.

    Command-line arguments passed to the hook


    Standard input given to the hook

    A copy of the job ClassAd.

    Expected standard output from the hook

    A set of attributes to insert or update into the job ad. For example, changing the Cmd attribute to a quoted string changes the executable to be run. Two special attributes can also be specified: HookStatusCode and HookStatusMessage. HookStatusCode, if specified and is not a negative number, will be used instead of the exit status of the hook unless the hook process exited due to a signal. A status code of 0 is success, and a positive integer indicates failure. A status code between 1 and 299 (inclusive) will result in the job going on hold; 300 or greater will result in the job going back to the Idle state. The HookStatusMessage will be echoed into the job’s event log file, and also be used as the Hold Reason string if the job is placed on hold.

    User id that the hook runs as

    The <Keyword>_HOOK_PREPARE_JOB hook runs with the same privileges as the job itself. If slot users are defined, the hook runs as the slot user, just as the job does.

    Exit status of the hook

    0 for success preparing the job, any non-zero value on failure.

  • The hook defined by the configuration variable <Keyword>_HOOK_PREPARE_JOB is invoked by the condor_starter before a job is going to be run but after the job’s input files have been transferred. This hook provides a chance to execute commands to set up or validate the job environment, and/or edit the job classad that is used by the condor_starter.

    The condor_starter waits until this hook returns before attempting to execute the job. If the hook returns a non-zero exit status, the condor_starter will assume an error was reached while attempting to set up the job environment and abort the job.

    Command-line arguments passed to the hook


    Standard input given to the hook

    A copy of the job ClassAd.

    Expected standard output from the hook

    A set of attributes to insert or update into the job ad. For example, changing the Cmd attribute to a quoted string changes the executable to be run. Two special attributes can also be specified: HookStatusCode and HookStatusMessage. HookStatusCode, if specified and is not a negative number, will be used instead of the exit status of the hook unless the hook process exited due to a signal. A status code of 0 is success, and a positive integer indicates failure. A status code between 1 and 299 (inclusive) will result in the job going on hold; 300 or greater will result in the job going back to the Idle state. The HookStatusMessage will be echoed into the job’s event log file, and also be used as the Hold Reason string if the job is placed on hold.

    User id that the hook runs as

    The <Keyword>_HOOK_PREPARE_JOB hook runs with the same privileges as the job itself. If slot users are defined, the hook runs as the slot user, just as the job does.

    Exit status of the hook

    0 for success preparing the job, any non-zero value on failure.

  • The hook defined by the configuration variable <Keyword>_HOOK_UPDATE_JOB_INFO is invoked periodically during the life of the job to update information about the status of the job. When the job is first spawned, the condor_starter will invoke this hook after STARTER_INITIAL_UPDATE_INTERVAL seconds (defaults to 8). Thereafter, the condor_starter will invoke the hook every STARTER_UPDATE_INTERVAL seconds (defaults to 300, which is 5 minutes).

    The condor_starter will not wait for this hook to return before taking other actions, and ignores all output. The hook is simply advisory, and has no impact on the behavior of the condor_starter.

    Command-line arguments passed to the hook


    Standard input given to the hook

    A copy of the job ClassAd that has been augmented with additional attributes describing the current status and execution behavior of the job.

    The additional attributes included inside the job ClassAd are:


    The current state of the job. Can be either "Running" or "Suspended".


    The process identifier for the initial job directly spawned by the condor_starter.


    The number of processes that the job has currently spawned.


    The epoch time when the job was first spawned by the condor_starter.


    The total number of seconds of system CPU time (the time spent at system calls) the job has used.


    The total number of seconds of user CPU time the job has used.


    The memory image size of the job in Kbytes.

    Expected standard output from the hook


    User id that the hook runs as

    The <Keyword>_HOOK_UPDATE_JOB_INFO hook runs with the same privileges as the job itself.

    Exit status of the hook


  • The hook defined by the configuration variable <Keyword>_HOOK_JOB_EXIT is invoked by the condor_starter whenever a job exits, either on its own or when being evicted from an execution slot.

    The condor_starter will wait for this hook to return before taking any other actions. In the case of jobs that are being managed by a condor_shadow, this hook is invoked before the condor_starter does its own optional file transfer back to the submission machine, writes to the local job event log file, or notifies the condor_shadow that the job has exited.

    Command-line arguments passed to the hook

    A string describing how the job exited:

    • exit The job exited or died with a signal on its own.

    • remove The job was removed with condor_rm or as the result of user job policy expressions (for example, PeriodicRemove).

    • hold The job was held with condor_hold or the user job policy expressions (for example, PeriodicHold).

    • evict The job was evicted from the execution slot for any other reason (PREEMPT evaluated to TRUE in the condor_startd, condor_vacate, condor_off, etc).

    Standard input given to the hook

    A copy of the job ClassAd that has been augmented with additional attributes describing the execution behavior of the job and its final results.

    The job ClassAd passed to this hook contains all of the extra attributes described above for <Keyword>_HOOK_UPDATE_JOB_INFO, and the following additional attributes that are only present once a job exits:


    A human-readable string describing why the job exited.


    A boolean indicating if the job exited due to being killed by a signal, or if it exited with an exit status.


    If ExitBySignal is true, the signal number that killed the job.


    If ExitBySignal is false, the integer exit code of the job.


    The number of seconds that the job ran during this invocation.

    Expected standard output from the hook


    User id that the hook runs as

    The <Keyword>_HOOK_JOB_EXIT hook runs with the same privileges as the job itself.

    Exit status of the hook


Example Hook: Specifying the Executable at Execution Time

The availability of multiple versions of an application leads to the need to specify one of the versions. As an example, consider that the java universe utilizes a single, fixed JVM. There may be multiple JVMs available, and the HTCondor job may need to make the choice of JVM version. The use of a job hook solves this problem. The job does not use the java universe, and instead uses the vanilla universe in combination with a prepare job hook to overwrite the Cmd attribute of the job ClassAd. This attribute is the name of the executable the condor_starter daemon will invoke, thereby selecting the specific JVM installation.

In the configuration of the execute machine:

JAVA5_HOOK_PREPARE_JOB = $(LIBEXEC)/java5_prepare_hook

With this configuration, a job that sets the HookKeyword attribute with

+HookKeyword = "JAVA5"

in the submit description file causes the condor_starter will run the hook specified by JAVA5_HOOK_PREPARE_JOB before running this job. Note that the double quote marks are required to correctly define the attribute. Any output from this hook is an update to the job ClassAd. Therefore, the hook that changes the executable may be


# Read and discard the job ClassAd
cat > /dev/null
echo 'Cmd = "/usr/java/java5/bin/java"'

If some machines in your pool have this hook and others do not, this fact should be advertised. Add to the configuration of every execute machine that has the hook:

HasJava5PrepareHook = True

The submit description file for this example job may be

universe = vanilla
executable = /usr/bin/java
arguments = Hello
# match with a machine that has the hook
requirements = HasJava5PrepareHook

should_transfer_files = always
when_to_transfer_output = on_exit
transfer_input_files = Hello.class

output = hello.out
error  = hello.err
log    = hello.log



Note that the job requirements submit command ensures that this job matches with a machine that has JAVA5_HOOK_PREPARE_JOB defined.