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arXiv 1203.0889math.NAcs.NA
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快速多极方法的数据驱动执行

Data-Driven Execution of Fast Multipole Methods

KAUST(阿卜杜拉国王科技大学)

Hatem Ltaief, Rio Yokota

AI总结:

针对快速多极方法在非规则粒子分布下的负载均衡难题,提出将最耗时阶段分解为任务,利用QUARK运行时环境异步调度任务,实现数据驱动的无序执行,在四路四核Intel Xeon系统上获得线性加速。

摘要:

快速多极方法具有O(N)复杂度、计算密集且几乎不需要同步,使其成为下一代超级计算机上的有利算法。它们最常见的应用是加速N体问题,但也可用于求解边界积分方程。当粒子分布不规则且树结构自适应时,负载均衡成为一个重要问题。一种常见的FMM负载均衡策略是使用前一步的工作负载作为权重来静态地重新划分下一步。作者在论文中讨论了另一种基于数据驱动执行的方法,以有效应对这一具有挑战性的负载均衡问题。核心思想是将FMM最耗时的阶段分解为更小的任务。然后,算法可以表示为一个有向无环图(DAG),其中节点代表任务,边代表任务之间的依赖关系。算法的执行通过使用QUARK运行时环境异步调度任务来完成,这种方式确保数据依赖关系不被违反以保持数值正确性。这种异步调度导致无序执行。数据驱动FMM执行的性能结果优于先前的策略,并在四路四核Intel Xeon系统上显示出线性加速。

英文摘要:

Fast multipole methods have O(N) complexity, are compute bound, and require very little synchronization, which makes them a favorable algorithm on next-generation supercomputers. Their most common application is to accelerate N-body problems, but they can also be used to solve boundary integral equations. When the particle distribution is irregular and the tree structure is adaptive, load-balancing becomes a non-trivial question. A common strategy for load-balancing FMMs is to use the work load from the previous step as weights to statically repartition the next step. The authors discuss in the paper another approach based on data-driven execution to efficiently tackle this challenging load-balancing problem. The core idea consists of breaking the most time-consuming stages of the FMMs into smaller tasks. The algorithm can then be represented as a Directed Acyclic Graph (DAG) where nodes represent tasks, and edges represent dependencies among them. The execution of the algorithm is performed by asynchronously scheduling the tasks using the QUARK runtime environment, in a way such that data dependencies are not violated for numerical correctness purposes. This asynchronous scheduling results in an out-of-order execution. The performance results of the data-driven FMM execution outperform the previous strategy and show linear speedup on a quad-socket quad-core Intel Xeon system.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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