Efficient scheduling algorithms are key for attaining high performance in heterogeneous computing systems. In this article, we propose a new list scheduling algorithm for assigning task graphs to fully connected heterogeneous processors with an aim to minimize the scheduling length. The proposed algorithm, called Improved Predict Priority Task Scheduling (IPPTS) algorithm has two phases: task prioritization phase, which gives priority to tasks, and processor selection phase, which selects a processor for a task. The IPPTS algorithm has a quadratic time complexity as the related algorithms for the same goal, that is $O(t^{2} \times p)$O(t2×p), for $t$t tasks and $p$p processors. Our algorithm reduces the scheduling length significantly by looking ahead in both task prioritization phase and processor selection phase. In this way, the algorithm is looking ahead to schedule a task and its heaviest successor task to the optimistic processor, i.e., the processor that minimizes their computation and communication costs. The experiments based on both randomly generated graphs and graphs of real-world applications show that the IPPTS algorithm significantly outperforms previous list scheduling algorithms in terms of makespan, speedup, makespan standard deviation, efficiency, and frequency of best results.

中文翻译：

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IPPTS：异构计算系统中科学工作流调度的高效算法

高效的调度算法是在异构计算系统中获得高性能的关键。在本文中，我们提出了一种新的列表调度算法，用于将任务图分配给完全连接的异构处理器，旨在最小化调度长度。所提出的算法称为改进的预测优先级任务调度 (IPPTS) 算法，它有两个阶段：任务优先级阶段，为任务提供优先级，以及处理器选择阶段，为任务选择处理器。IPPTS 算法具有二次时间复杂度作为相同目标的相关算法，即$O(t^{2} \times p)$哦(吨2×磷)， 为了 $t$吨 任务和 $p$磷处理器。我们的算法通过在任务优先级阶段和处理器选择阶段向前看，显着减少了调度长度。通过这种方式，该算法将任务及其最重的后续任务调度到乐观处理器，即最小化其计算和通信成本的处理器。基于随机生成图和实际应用图的实验表明，IPPTS 算法在完工时间、加速比、完工时间标准偏差、效率和最佳结果频率方面明显优于以前的列表调度算法。