High-Throughput Computing (HTC) and its Requirements¶
The quality of many projects is dependent upon the quantity of computing cycles available. Many problems require years of computation to solve. These problems demand a computing environment that delivers large amounts of computational power over a long period of time. Such an environment is called a High-Throughput Computing (HTC) environment. In contrast, High Performance Computing (HPC) environments deliver a tremendous amount of compute power over a short period of time. HPC environments are often measured in terms of Floating point Operations Per Second (FLOPS). A growing community is not concerned about operations per second, but operations per month or per year (FLOPY). They are more interested in how many jobs they can complete over a long period of time instead of how fast an individual job can finish.
The key to HTC is to efficiently harness the use of all available resources. Years ago, the engineering and scientific community relied on a large, centralized mainframe or a supercomputer to do computational work. A large number of individuals and groups needed to pool their financial resources to afford such a machine. Users had to wait for their turn on the mainframe, and they had a limited amount of time allocated. While this environment was inconvenient for users, the utilization of the mainframe was high; it was busy nearly all the time.
As computers became smaller, faster, and cheaper, users moved away from centralized mainframes. Today, most organizations own or lease many different kinds of computing resources in many places. Racks of departmental servers, desktop machines, leased resources from the Cloud, allocations from national supercomputer centers are all examples of these resources. This is an environment of distributed ownership, where individuals throughout an organization own their own resources. The total computational power of the institution as a whole may be enormous, but because of distributed ownership, groups have not been able to capitalize on the aggregate institutional computing power. And, while distributed ownership is more convenient for the users, the utilization of the computing power is lower. Many machines sit idle for very long periods of time while their owners have no work for the machines to do.