research-article

Open access

PEAK: a Light-Weight Profiler for HPC Systems

Authors:

SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis

Pages 677 - 680

https://doi.org/10.1145/3624062.3624143

Published: 12 November 2023 Publication History

All formats PDF

Abstract

In the context of the expanding landscape of contemporary High-Performance Computing (HPC) applications from petascale to exascale, the pursuit of performance optimization emerges as a significant impediment within software development endeavors. In the meantime, the escalating intricacies inherent in parallel architectures and systems serve to compound the challenges associated with performance enhancement. Here, we introduce PEAK (Performance Evaluation and Analysis Kit), a light-weight profiling tool developed with a specific focus on large-scale HPC applications. Using Dynamic Binary Instrumentation, PEAK is able to profile large-scale multi-threaded, multi-process applications with low overhead and high accuracy. We analyzed the overhead and accuracy of PEAK using synthetic benchmarks and real applications and compared it against the other widely used HPC profiling tools available. Our demonstration underscores that PEAK exhibits comparable overhead and accuracy to alternative profiling tools, while preserving its inherent simplicity.

Supplemental Material

MP4 File

Recording of "PEAK: a Light-Weight Profiler for HPC Systems" presentation at HUST-23.

Download
83.86 MB

References

[1]

L. Adhianto, S. Banerjee, M. Fagan, M. Krentel, G. Marin, J. Mellor-Crummey, and N. R. Tallent. 2010. HPCTOOLKIT: tools for performance analysis of optimized parallel programs. Concurrency and Computation: Practice and Experience 22, 6 (2010), 685–701. https://doi.org/10.1002/cpe.1553 _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.1553.

Crossref

Google Scholar

[2]

Andrew R. Bernat and Barton P. Miller. 2011. Anywhere, any-time binary instrumentation. In Proceedings of the 10th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools (2011-09-05). ACM, Szeged Hungary, 9–16. https://doi.org/10.1145/2024569.2024572

Digital Library

Google Scholar

[3]

Derek Bruening, Timothy Garnett, and Saman Amarasinghe. 2003. An infrastructure for adaptive dynamic optimization. In Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization (2003-03-23) (CGO ’03). IEEE Computer Society, USA, 265–275.

Crossref

Google Scholar

[4]

Galen Hunt and Doug Brubacher. 1999. Detours: Binary Interception of Win32 Functions. In Proceedings of the 3rd Conference on USENIX Windows NT Symposium - Volume 3 (Seattle, Washington) (WINSYM’99). USENIX Association, USA, 14.

Digital Library

Google Scholar

[5]

Christopher January, Jonathan Byrd, Xavier Oró, and Mark O’Connor. 2015. Allinea MAP: Adding Energy and OpenMP Profiling Without Increasing Overhead. In Tools for High Performance Computing 2014, Christoph Niethammer, José Gracia, Andreas Knüpfer, Michael M. Resch, and Wolfgang E. Nagel (Eds.). Springer International Publishing, Cham, 25–35. https://doi.org/10.1007/978-3-319-16012-2_2

Crossref

Google Scholar

[6]

Chi-Keung Luk, Robert Cohn, Robert Muth, Harish Patil, Artur Klauser, Geoff Lowney, Steven Wallace, Vijay Janapa Reddi, and Kim Hazelwood. 2005. Pin: building customized program analysis tools with dynamic instrumentation. ACM SIGPLAN Notices 40, 6 (2005), 190–200. https://doi.org/10.1145/1064978.1065034

Digital Library

Google Scholar

[7]

Dieter An Mey, Scott Biersdorf, Christian Bischof, Kai Diethelm, Dominic Eschweiler, Michael Gerndt, Andreas Knüpfer, Daniel Lorenz, Allen Malony, Wolfgang E. Nagel, Yury Oleynik, Christian Rössel, Pavel Saviankou, Dirk Schmidl, Sameer Shende, Michael Wagner, Bert Wesarg, and Felix Wolf. 2011. Score-P: A Unified Performance Measurement System for Petascale Applications. In Competence in High Performance Computing 2010, Christian Bischof, Heinz-Gerd Hegering, Wolfgang E. Nagel, and Gabriel Wittum (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 85–97. https://doi.org/10.1007/978-3-642-24025-6_8

Crossref

Google Scholar

[8]

Ole André V Ravnås. 2019. Frida: Dynamic instrumentation toolkit for developers, reverse-engineers, and security researchers.

Google Scholar

[9]

James Reinders. 2005. VTune performance analyzer essentials: measurement and tuning techniques for software developers (1. print ed.). Intel Press, Hillsboro, Or.

Google Scholar

[10]

Sameer S. Shende and Allen D. Malony. 2006. The Tau Parallel Performance System. The International Journal of High Performance Computing Applications 20, 2 (May 2006), 287–311. https://doi.org/10.1177/1094342006064482 Publisher: SAGE Publications Ltd STM.

Digital Library

Google Scholar

[11]

Dan Stanzione, John West, R. Todd Evans, Tommy Minyard, Omar Ghattas, and Dhabaleswar K. Panda. 2020. Frontera: The Evolution of Leadership Computing at the National Science Foundation. In Practice and Experience in Advanced Research Computing (Portland, OR, USA) (PEARC ’20). Association for Computing Machinery, New York, NY, USA, 106–111. https://doi.org/10.1145/3311790.3396656

Digital Library

Google Scholar

[12]

Jeffrey Vetter and Chris Chambreau. 2005. mpiP: Lightweight, Scalable MPI Profiling.

Google Scholar

Cited By

View all

Li JWang YLiang XLiu H(2024)Automatic BLAS Offloading on Unified Memory Architecture: A Study on NVIDIA Grace-HopperPractice and Experience in Advanced Research Computing 2024: Human Powered Computing10.1145/3626203.3670561(1-5)Online publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1145/3626203.3670561

Index Terms

PEAK: a Light-Weight Profiler for HPC Systems
1. General and reference
  1. Cross-computing tools and techniques
    1. Performance
2. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software performance

Recommendations

Non-intrusive dynamic application profiler for detailed loop execution characterization
CASES '08: Proceedings of the 2008 international conference on Compilers, architectures and synthesis for embedded systems

Application profiling - the process of monitoring an application to determine the frequency of execution within specific regions - is an essential step within the design process for many software and hardware systems. In this paper, we present an ...
Profiling OpenCL Kernels Using Wavefront Occupancy with Radeon GPU Profiler
IWOCL '19: Proceedings of the International Workshop on OpenCL

Profiling OpenCL [3] applications on modern GPUs is usually limited to gathering timestamps from the host side or gathering performance counter data for a complete GPU kernel. In this presentation, we will show the limitations of existing performance ...
Smart Clustering of HPC Applications Using Similar Job Detection Methods
Parallel Processing and Applied Mathematics
Abstract
In order for supercomputer resources to be effectively used, it is necessary to constantly analyze various aspects of the operation of modern HPC systems. One of the most significant aspects is the efficiency of execution of parallel applications ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis

November 2023

2180 pages

ISBN:9798400707858

DOI:10.1145/3624062

This work is licensed under a Creative Commons Attribution International 4.0 License.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 November 2023

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

NSF (National Science Foundation)

Conference

SC-W 2023

SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis

November 12 - 17, 2023

CO, Denver, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
415
Total Downloads

Downloads (Last 12 months)368
Downloads (Last 6 weeks)36

Reflects downloads up to 16 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Li JWang YLiang XLiu H(2024)Automatic BLAS Offloading on Unified Memory Architecture: A Study on NVIDIA Grace-HopperPractice and Experience in Advanced Research Computing 2024: Human Powered Computing10.1145/3626203.3670561(1-5)Online publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1145/3626203.3670561

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

Supplemental Material

References

Cited By

Index Terms

Recommendations

Non-intrusive dynamic application profiler for detailed loop execution characterization

Profiling OpenCL Kernels Using Wavefront Occupancy with Radeon GPU Profiler

Smart Clustering of HPC Applications Using Similar Job Detection Methods

Comments

Information

Published In

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

HTML Format

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations