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首页> 外文期刊>International Journal of Innovative Computing Information and Control >MULTICORE SCHEDULING BASED ON LEARNING FROM OPTIMIZATION MODELS
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MULTICORE SCHEDULING BASED ON LEARNING FROM OPTIMIZATION MODELS

机译：基于优化模型学习的多核调度

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Scheduling for multicore computer systems is a challenge since subsets of cores may share resources, such as a cache. Performance for workloads may therefore vary depending on which tasks are scheduled to run on the same subset of cores. There is therefore a need for contention-aware scheduling. Our study involves implementation of an optimal multicore scheduler which has perfect prediction of negative consequences of all combinations of tasks scheduled on the hardware platform we are using. It uses an Integer Program. We show that it is indeed optimal for our workloads, supporting its authors' claims. We also implemented a scheduler which uses a combination of a machine learning model (M5 Prime) and Linear Programming to schedule tasks on CPU cores and compared it to a state-of-the-art scheduler known as Distributed Intensity (DI). Some workloads exhibited moderate improvements in unfairness, but not much in runtime. A scheduler based on another machine learning model was implemented, this time a Multilayer Perceptron (MLP). To do this, we had to generate workloads for the Optimal Scheduler, store its decision for various types of workloads, and train a Multilayer Perceptron model on this data. We then implemented a scheduler with the Multilayer Perceptron model, which tries to mimic the decision of the Optimal Scheduler. Our results show that our scheduler is better than DI in 7 out of 9 test workloads (mostly by 10%), and approximately equal to the Optimal Scheduler in 6 out of 9 test workloads (exactly equal in 4). The MLP scheduler is faster than the Optimal Scheduler by a wide margin, and faster than the Linear Programming scheduler.
机译：由于内核子集可能共享资源（例如缓存），因此多核计算机系统的调度是一个挑战。因此，工作负载的性能可能会有所不同，具体取决于计划在同一核子集上运行的任务。因此需要竞争感知调度。我们的研究涉及最佳多核调度程序的实现，该调度程序可以完美预测在我们使用的硬件平台上调度的所有任务组合的负面影响。它使用一个整数程序。我们证明，对于我们的工作负载，它确实是最佳选择，可以支持其作者的主张。我们还实现了一个调度程序，该程序结合了机器学习模型（M5 Prime）和线性编程在CPU内核上调度任务，并将其与最新的调度程序进行了比较，该调度程序称为Distributed Intensity（DI）。某些工作负载在不公平方面表现出适度的改进，但在运行时却没有太大的改进。实现了基于另一个机器学习模型的调度程序，这次是多层感知器（MLP）。为此，我们必须为最优调度程序生成工作负载，存储其对各种类型的工作负载的决策，并根据此数据训练多层感知器模型。然后，我们使用多层感知器模型实现了一个调度程序，该模型试图模仿最优调度程序的决策。我们的结果表明，在9个测试工作负载中，有7个（大多数情况下为10％）的调度程序比DI更好，并且在9个测试工作负载中有6个（与4个完全相等）近似于最优调度程序。 MLP调度程序比最优调度程序快很多，并且比线性编程调度程序快。

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