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Resource Allocation for Steerable Parallel Parameter Searches

机译:可控并行参数搜索的资源分配

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摘要

Computational Grids lend themselves well to parameter sweep applications, in which independent tasks calculate results for points in a parameter space. It is possible for a parameter space to become so large as to pose prohibitive system requirements. In these cases, user-directed steering promises to reduce overall computation time. In this paper, we address an interesting challenge posed by these user-directed searches: how should compute resources be allocated to application tasks as the overall computation is being steered by the user? We present a model for user-directed searches, and then propose a number of resource allocation strategies and evaluate them in simulation. We find that prioritizing the assignments of tasks to compute resources throughout the search can lead to substantial performance improvements.
机译:计算网格非常适合参数扫描应用程序,在该应用程序中,独立任务可计算参数空间中点的结果。参数空间可能会变得太大,以至于对系统提出了过高的要求。在这些情况下,用户控制的转向有望减少总体计算时间。在本文中,我们解决了这些以用户为导向的搜索带来的有趣挑战:随着用户控制整体计算,应如何将计算资源分配给应用程序任务?我们提出了一种面向用户的搜索模型,然后提出了多种资源分配策略并在仿真中对其进行了评估。我们发现,在整个搜索过程中对任务分配进行优先级分配以计算资源可以显着提高性能。

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