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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 .
Due to the inherent difficulties in full automatic parallelization, several easier approaches exist to get a parallel program in higher quality. One of these is to allow programmers to add "hints" to their programs to guide compiler parallelization, such as HPF for distributed memory systems and OpenMP or OpenHMPP for shared memory systems.
As such, it provides algorithmic skeleton programming as a Java library. Both task and data parallel skeletons are fully nestable; and are instantiated via parametric skeleton objects, not inheritance. Calcium supports the execution of skeleton applications on top of the ProActive environment for distributed cluster like infrastructure ...
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.
The basic form of the problem of scheduling jobs with multiple (M) operations, over M machines, such that all of the first operations must be done on the first machine, all of the second operations on the second, etc., and a single job cannot be performed in parallel, is known as the flow-shop scheduling problem.
The idea of work stealing goes back to the implementation of the Multilisp programming language and work on parallel functional programming languages in the 1980s. [2] It is employed in the scheduler for the Cilk programming language, [3] the Java fork/join framework, [4] the .NET Task Parallel Library, [5] and the Rust Tokio runtime. [6] [7]
In Auguin’s SPMD model, the same (parallel) task (“same program”) is executed on different (SIMD) processors (“operating in lock-step mode” [1] acting on a part (“slice”) of the data-vector. Specifically, in their 1985 paper [2] (and similarly in [3] [1]) is stated: “we consider the SPMD (Single Program, Multiple Data) operating ...