基于upc++ DepSpawn的数据流计算软件缓存自动调优策略

IF 0.9 Q3 MATHEMATICS, APPLIED Computational and Mathematical Methods Pub Date : 2021-01-16 DOI:10.1002/cmm4.1148
Basilio B. Fraguela, Diego Andrade
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引用次数: 1

摘要

数据流计算允许在满足所有依赖项后立即开始计算。这在具有不规则或复杂依赖模式的应用程序中特别有用,否则这些应用程序要么涉及会降低性能的粗粒度同步,要么涉及高编程成本。最近提出了一个在混合共享/分布式内存系统中开发高性能数据流算法的建议是upc++ DepSpawn。在提供良好性能的许多技术中,软件缓存可以最大限度地减少所涉及的进程之间的通信。在本文中,我们提供了该缓存的实现和操作的详细信息,并提出了一种自动调优策略,该策略通过使用户不必估计该缓存的适当大小来简化其使用。相反,运行时现在能够定义合理大小的缓存,提供接近最佳的行为。
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A software cache autotuning strategy for dataflow computing with UPC++ DepSpawn

Dataflow computing allows to start computations as soon as all their dependencies are satisfied. This is particularly useful in applications with irregular or complex patterns of dependencies which would otherwise involve either coarse grain synchronizations which would degrade performance, or high programming costs. A recent proposal for the easy development of performant dataflow algorithms in hybrid shared/distributed memory systems is UPC++ DepSpawn. Among the many techniques it applies to provide good performance is a software cache that minimizes the communications among the processes involved. In this article we provide the details of the implementation and operation of this cache and we present an autotuning strategy that simplifies its usage by freeing the user from having to estimate an adequate size for this cache. Rather, the runtime is now able to define reasonably sized caches that provide near optimal behavior.

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