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Cache-conscious run-time decomposition of data parallel computations

机译:数据并行计算的具有缓存意识的运行时分解

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摘要

Multi-core architectures feature an intricate hierarchy of cache memories, with multiple levels and sizes. To adequately decompose an application according to the traits of a particular memory hierarchy is a cumbersome task that may be rewarded with significant performance gains. The current state-of-the-art in memory hierarchy-aware parallel computing delegates this endeavour on the programmer, demanding from him deep knowledge of both parallel programming and computer architecture. In this paper, we propose the shifting of these memory hierarchy-related concerns to the run-time system, which then takes on the responsibility of distributing the computation's data across the target memory hierarchy. We evaluate our approach from a performance perspective, comparing it against the common cache-neglectful data decomposition strategy.
机译:多核体系结构具有复杂的高速缓存存储器层次结构,具有多个级别和大小。要根据特定内存层次结构的特征来充分分解应用程序是一项繁琐的任务,可以通过获得显着的性能收益获得回报。当前了解内存层次结构的并行计算的最新技术将这项工作委托给程序员,要求程序员对并行编程和计算机体系结构有深入的了解。在本文中,我们建议将这些与内存层次结构相关的问题转移到运行时系统,然后由运行时系统负责在目标内存层次结构中分配计算数据。我们从性能的角度评估我们的方法,并将其与常见的忽略缓存的数据分解策略进行比较。

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