分层抽样以实现均匀的工作负载分区

2014 23rd International Conference on Parallel Architecture and Compilation (PACT) Pub Date : 2014-08-24 DOI:10.1145/2628071.2671422

Jeeva Paudel, J. N. Amaral

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引用次数: 2

摘要

这项工作提出了一种新的算法，工作负载分区和调度(WPS)，用于均匀分区分布式/共享内存系统上基于隐式定义的工作列表的大型应用程序的计算工作负载。WPS使用分层抽样来估计将在应用程序的每个步骤中处理的工作项的数量。WPS使用这种估计来均匀地划分和分配计算工作负载。对大型应用程序的经验评估-迭代深化A∗(IDA∗)应用于(4×4)-滑动瓷砖拼图，Delaunay网格生成和Delaunay网格优化-表明WPS适用于一系列问题，并且比现有的工作偷窃调度程序单独产生28%到49%的速度。

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Stratified sampling for even workload partitioning

This work presents a novel algorithm, Workload Partitioning and Scheduling (WPS), for evenly partitioning the computational workload of large implicitly-defined work-list based applications on distributed/shared-memory systems. WPS uses stratified sampling to estimate the number of work items that will be processed in each step of an application. WPS uses such estimation to evenly partition and distribute the computational workload. An empirical evaluation on large applications — Iterative-Deepening A∗ (IDA∗) applied to (4×4)-Sliding-Tile Puzzles, Delaunay Mesh Generation, and Delaunay Mesh Refinement — shows that WPS is applicable to a range of problems, and yields 28% to 49% speedups over existing work-stealing schedulers alone.

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来源期刊

2014 23rd International Conference on Parallel Architecture and Compilation (PACT)

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