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Parallel task scheduling (also called parallel job scheduling [1] [2] or parallel processing scheduling [3]) is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling .
Implementations of the fork–join model will typically fork tasks, fibers or lightweight threads, not operating-system-level "heavyweight" threads or processes, and use a thread pool to execute these tasks: the fork primitive allows the programmer to specify potential parallelism, which the implementation then maps onto actual parallel execution. [1]
Optimal job scheduling is a class of optimization problems related to scheduling. The inputs to such problems are a list of jobs (also called processes or tasks) and a list of machines (also called processors or workers). The required output is a schedule – an assignment of jobs to machines. The schedule should optimize a certain objective ...
Task parallelism (also known as function parallelism and control parallelism) is a form of parallelization of computer code across multiple processors in parallel computing environments. Task parallelism focuses on distributing tasks —concurrently performed by processes or threads —across different processors.
In distinction, with fork-and-join approaches, the program starts executing on one processor and the execution splits in a parallel region, which is started when parallel directives are encountered; in a parallel region, the processors execute a parallel task on different data. A typical example is the parallel DO loop, where different ...
A task-parallel model focuses on processes, or threads of execution. These processes will often be behaviourally distinct, which emphasises the need for communication. Task parallelism is a natural way to express message-passing communication. In Flynn's taxonomy, task parallelism is usually classified as MIMD/MPMD or MISD.
These tasks are assigned individually to many processors. The amount of work associated with a parallel task is low and the work is evenly distributed among the processors. Hence, fine-grained parallelism facilitates load balancing. [3] As each task processes less data, the number of processors required to perform the complete processing is high.
In computer science, gang scheduling is a scheduling algorithm for parallel systems that schedules related threads or processes to run simultaneously on different processors. Usually these will be threads all belonging to the same process, but they may also be from different processes, where the processes could have a producer-consumer ...