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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 .
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 ...
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.
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 ...
For our example, the next iteration provided us with zero for the maximum lateness on machines 3 and 4, so their optimal sequences can be included in the drawing (see Iteration 3). At this point the Shifting Bottleneck Heuristic is complete. The drawing should now include all precedence constraints and all disjunctive constraints.
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 parallel computing, granularity (or grain size) of a task is a measure of the amount of work (or computation) which is performed by that task. [1] Another definition of granularity takes into account the communication overhead between multiple processors or processing elements. It defines granularity as the ratio of computation time to ...
Parallel programming models are closely related to models of computation. A model of parallel computation is an abstraction used to analyze the cost of computational processes, but it does not necessarily need to be practical, in that it can be implemented efficiently in hardware and/or software. A programming model, in contrast, does ...