Understanding object-level memory access patterns across the spectrum

Xu Ji, Chao Wang, Nosayba El-Sayed, Xiaosong Ma, Youngjae Kim, Sudharshan S. Vazhkudai, Wei Xue, Daniel Sanchez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450351140
DOIs
Publication statusPublished - 12 Nov 2017
Externally publishedYes
EventInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017 - Denver, United States
Duration: 12 Nov 201717 Nov 2017

Other

OtherInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017
CountryUnited States
CityDenver
Period12/11/1717/11/17

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Keywords

  • Data types and structures
  • Memory profiling
  • Object access patterns
  • Tracing
  • Workload characterization

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this

Ji, X., Wang, C., El-Sayed, N., Ma, X., Kim, Y., Vazhkudai, S. S., Xue, W., & Sanchez, D. (2017). Understanding object-level memory access patterns across the spectrum. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017 [25] Association for Computing Machinery, Inc. https://doi.org/10.1145/3126908.3126917