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首页> 外文期刊>International Journal of Computer Science Engineering and Information Technology Research >HETEROGENEOUS MULTIPROCESSOR TASK SCHEDULING USING GENETIC AND SIMULATED ANNEALING ALGORITHMS AND THEIR HYBRIDIZATION
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HETEROGENEOUS MULTIPROCESSOR TASK SCHEDULING USING GENETIC AND SIMULATED ANNEALING ALGORITHMS AND THEIR HYBRIDIZATION

机译:遗传和模拟退火算法的异类多处理器任务调度及其混合

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摘要

In a heterogeneous multiprocessor system, a large program is decomposed into a set of tasks that have data dependencies. The most important problem, which is encountered in such systems, is task matching and scheduling. It consists of assigning tasks to processors, ordering task execution for each processor, and ordering interprocessor data transfers. The goal is to schedule all the tasks on the available processors so as to minimize the overall length of time required to execute the entire program without violating precedence constraints. This efficient scheduling reduces processing time and increases processor utilization, i.e. achieves high performance. This paper studies a previously developed problem-space genetic algorithm (PSGA)-based technique for task matching and scheduling on a heterogeneous multiprocessor system, and modifies it to mitigate its drawbacks. The modified algorithm is called MPSGA. Then, the paper presents a proposed Simulated Annealing (SA)-based task scheduling algorithm. Finally, it presents a proposed hybrid task scheduling algorithm that combines the proposed SA with MPSGA, which is called MPSGASA. Experiments have been conducted to evaluate the performance of the proposed scheduling techniques.
机译:在异构多处理器系统中,大型程序被分解为一组具有数据依赖性的任务。在此类系统中遇到的最重要的问题是任务匹配和调度。它包括向处理器分配任务,为每个处理器排序任务执行以及对处理器间数据传输进行排序。目的是在可用处理器上安排所有任务,以便在不违反优先顺序约束的情况下,最小化执行整个程序所需的总时间。这种有效的调度减少了处理时间并提高了处理器利用率,即实现了高性能。本文研究了以前开发的基于问题空间遗传算法(PSGA)的技术,用于在异构多处理器系统上进行任务匹配和调度,并对它进行了修改以减轻其缺点。修改后的算法称为MPSGA。然后,本文提出了一种基于模拟退火(SA)的任务调度算法。最后,它提出了一种提出的混合任务调度算法,该算法将提出的SA与MPSGA相结合,称为MPSGASA。已经进行实验以评估所提出的调度技术的性能。

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