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首页> 外文会议>Proceedings of the IASTED international conferences on informatics >A METHODOLOGY FOR ESTIMATING CO-SCHEDULING SLOWDOWNS DUE TO MEMORY BUS CONTENTION ON MULTICORE NODES
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A METHODOLOGY FOR ESTIMATING CO-SCHEDULING SLOWDOWNS DUE TO MEMORY BUS CONTENTION ON MULTICORE NODES

机译：估算多核节点上记忆总线含量导致的协同调度减慢的方法

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摘要

When two or more programs are co-scheduled on the same multicore computer they might experience a slowdown due to the limited off-chip memory bandwidth. According to our measurements, this slowdown does not depend on the total bandwidth use in a simple way. One thing we observe is that a higher memory bandwidth usage will not always lead to a larger slowdown. This means that relying on bandwidth usage as input to a job scheduler might cause non-optimal scheduling of processes on multicore nodes in clusters, clouds, and grids. To guide scheduling decisions, we instead propose a slowdown based characterization approach. Real slowdowns are complex to measure due to the exponential number of experiments needed. Thus, we present a novel method for estimating the slowdown programs will experience when co-scheduled on the same computer. We evaluate the method by comparing the predictions made with real slowdown data and the often used memory bandwidth based method. This study show that a scheduler relying on slowdown based categorization makes fewer incorrect co-scheduling choices and the negative impact on program execution times is less than when using a bandwidth based categorization method.
机译：当在同一台多核计算机上共同调度两个或多个程序时，由于片外内存带宽有限，它们可能会变慢。根据我们的测量，这种减速并不简单地取决于总带宽的使用。我们观察到的一件事是更高的内存带宽使用率并不总是会导致更大的减速。这意味着依赖带宽使用作为作业调度程序的输入可能会导致集群，云和网格中多核节点上的进程的非最佳调度。为了指导调度决策，我们改为提出一种基于减速的表征方法。由于所需的实验数量呈指数级增长，因此实际的降低速度很难衡量。因此，我们提出了一种新方法，用于估计在同一台计算机上共同调度时减速程序将遇到的速度。我们通过比较实际减速数据的预测结果和经常使用的基于内存带宽的方法来评估该方法。这项研究表明，依靠基于减速的分类的调度程序可以减少较少的不正确的联合调度选择，并且与使用基于带宽的分类方法相比，对程序执行时间的负面影响要小。

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