Context-Aware Memory Profiling for Speculative Parallelism

Changsu Kim, Juhyun Kim, Juwon Kang, Jae W. Lee, Hanjun Kim

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

Abstract

To expose hidden parallelism from programs with complex dependences, modern compilers employ memory profilers to augment imprecise static analyses. Since dynamic dependence patterns among instructions can vary widely depending on the context, such as function call site stack and loop nest level, context-aware memory profiling is of great value for precise memory profiling. However, recording memory dependences with full context information causes huge overheads in terms of CPU cycles and memory space. Existing profilers mitigate this problem by compromising precision, coverage, or both. This paper proposes a new precise Context-Aware Memory Profiling (CAMP) framework that efficiently traces all the memory dependences with full context information. CAMP statically analyzes a context tree of a program that illustrates all the possible dynamic contexts, and simplifies context management during profiling. For 14 programs from SPEC CINT2000 and CINT2006 benchmark suites, CAMP increases speculative parallelism opportunities by 12.6% on average and by up to 63.0% compared to the baseline context-oblivious, loop-aware memory profiler.

Original languageEnglish
Title of host publicationProceedings - 24th IEEE International Conference on High Performance Computing, HiPC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages328-337
Number of pages10
ISBN (Electronic)9781538622933
DOIs
Publication statusPublished - 2018 Feb 7
Event24th IEEE International Conference on High Performance Computing, HiPC 2017 - Jaipur, India
Duration: 2017 Dec 182017 Dec 21

Publication series

NameProceedings - 24th IEEE International Conference on High Performance Computing, HiPC 2017
Volume2017-December

Other

Other24th IEEE International Conference on High Performance Computing, HiPC 2017
CountryIndia
CityJaipur
Period17/12/1817/12/21

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Modelling and Simulation

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  • Cite this

    Kim, C., Kim, J., Kang, J., Lee, J. W., & Kim, H. (2018). Context-Aware Memory Profiling for Speculative Parallelism. In Proceedings - 24th IEEE International Conference on High Performance Computing, HiPC 2017 (pp. 328-337). (Proceedings - 24th IEEE International Conference on High Performance Computing, HiPC 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HiPC.2017.00045