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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 .
If that activity time is for the first work center, then schedule the job first. If that activity time is for the second work center then schedule the job last. Break ties arbitrarily. Eliminate the shortest job from further consideration. Repeat steps 2 and 3, working towards the center of the job schedule until all jobs have been scheduled.
Fork–join is the main model of parallel execution in the OpenMP framework, although OpenMP implementations may or may not support nesting of parallel sections. [6] It is also supported by the Java concurrency framework, [ 7 ] the Task Parallel Library for .NET, [ 8 ] and Intel's Threading Building Blocks (TBB). [ 1 ]
In single-stage job scheduling problems, there are four main categories of machine environments: 1: Single-machine scheduling. There is a single machine. P: Identical-machines scheduling. There are parallel machines, and they are identical. Job takes time on any machine it is scheduled to.
[2] 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]
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.
In computer science, a parallel algorithm, as opposed to a traditional serial algorithm, is an algorithm which can do multiple operations in a given time. It has been a tradition of computer science to describe serial algorithms in abstract machine models, often the one known as random-access machine.
The inclusion of the suppressed information is guided by the proof of a scheduling theorem due to Brent, [2] which is explained later in this article. The WT framework is useful since while it can greatly simplify the initial description of a parallel algorithm, inserting the details suppressed by that initial description is often not very ...