Understanding Object-level Memory Access Patterns Across the Spectrum

Xu Ji, Chao Wang, Nosayba El-Sayed, Xiaosong Ma, Youngjae Kim, Sudharshan S. Vazhkudai, W. Xue, Daniel Sánchez
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引用次数: 17

Abstract

Memory accesses limit the performance and scalability of countless applications. Many design and optimization efforts will benefit from an in-depth understanding of memory access behavior, which is not offered by extant access tracing and profiling methods.In this paper, we adopt a holistic memory access profiling approach to enable a better understanding of program-system memory interactions. We have developed a two-pass tool adopting fast online and slow offline profiling, with which we have profiled, at the variable/object level, a collection of 38 representative applications spanning major domains (HPC, personal computing, data analytics, AI, graph processing, and datacenter workloads), at varying problem sizes. We have performed detailed result analysis and code examination. Our findings provide new insights into application memory behavior, including insights on per-object access patterns, adoption of data structures, and memory-access changes at different problem sizes. We find that scientific computation applications exhibit distinct behaviors compared to datacenter workloads, motivating separate memory system design/optimizations.
理解对象级内存访问模式
内存访问限制了无数应用程序的性能和可伸缩性。许多设计和优化工作将受益于对内存访问行为的深入理解,这是现有访问跟踪和分析方法所不能提供的。在本文中,我们采用整体内存访问分析方法来更好地理解程序-系统内存交互。我们开发了一个采用快速在线和缓慢离线分析的两步工具,我们在变量/对象级别分析了38个代表性应用程序的集合,涵盖了主要领域(HPC,个人计算,数据分析,人工智能,图形处理和数据中心工作负载),不同的问题规模。我们进行了详细的结果分析和代码检查。我们的发现为应用程序内存行为提供了新的见解,包括对每个对象访问模式、数据结构的采用以及不同问题规模下的内存访问变化的见解。我们发现,与数据中心工作负载相比,科学计算应用程序表现出不同的行为,从而激发了单独的内存系统设计/优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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