Apex-Map: A Global Data Access Benchmark to Analyze HPC Systems and Parallel Programming Paradigms
Apex-Map: A Global Data Access Benchmark to Analyze HPC Systems and Parallel Programming Paradigms
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Apex-Map：分析 HPC 系统和并行编程范式的全球数据访问基准
DOI：
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发表时间：
2005
期刊：
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通讯作者：
H. Shan
中科院分区：
文献类型：
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作者：
Erich Strohmaier;H. Shan
记忆墙和全球数据运动已成为许多科学应用的主要性能瓶颈。因此，需要对数据访问流和相关基准测量其性能的新特征来有效地比较HPC系统，软件和编程范式。在本文中，我们介绍了一种新颖的全球数据访问基准，Apex-Map。它是一个参数化的合成性能探针，并将时间和空间位置的概念整合到其设计中。我们测量了几个高级处理器和并行计算平台上的整个时间和空间位置的APEX-MAP性能，并使用生成的性能表面进行表现比较，并研究这些不同架构的特征。我们证明了Apex-Map的结果清楚地反映了使用系统的许多特定特征。我们还展示了Apex-MAP的实用性，用于分析三个领先的并行编程模型的性能效应，并证明其相对优点。
The memory wall and global data movement have become the dominant performance bottleneck for many scientific applications. New characterizations of data access streams and related benchmarks to measure their performances are therefore needed to compare HPC systems, software, and programming paradigms effectively. In this paper, we introduce a novel global data access benchmark, Apex-Map. It is a parameterized synthetic performance probe and integrates concepts for temporal and spatial locality into its design. We measured Apex-Map performance for a whole range of temporal and spatial localities on several advanced processors and parallel computing platforms and use the generated performance surfaces forperformance comparisons and to study the characteristics of these different architectures. We demonstrate that the results of Apex-Map clearly reflect many specific characteristics of the used systems. We also show the utility of Apex-Map for analyzing the performance effects of three leading parallel programming models and demonstrate their relative merits.