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Performance Modeling of OpenMP Program Based on LLVM Compilation Platform

  • Chen Yang
  • , Xiangzhan Yu*
  • , Yue Zhao
  • *Corresponding author for this work
  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the rapid development of computers and their technologies, high- performance computers (HPC) have gradually become one of the indispensable research methods in the field of basic science. However, with the development of HPC, its scale and complexity have also increased by orders of magnitude, which lead to problems such as high execution efficiency and energy consumption of parallel applications in the HPC system. So it brings many challenges to parallel programming. In order to solve the above situation, the mechanism needs to be proposed, which can predict the performance characteristics of parallel programs before they are executed. Therefore, parallel program performance prediction is the key to solving the above problems. OpenMP program is the most common shared memory parallel program, so it is an important part of the research of parallel program performance evaluation. This article uses the LLVM compilation platform to convert the OpenMP source code into LLVM intermediate code (IR) through Clang, statically and dynamically analyzes the IR, and then obtains the OpenMP performance model to predict the OpenMP execution time and optimal execution time. Based on the modeling of OpenMP parallel applications, we can get the performance model of the OpenMP program. Based on this model, we can predict the performance and scalability of OpenMP-related applications in HPC systems, find program and system bottlenecks, and guide program performance optimization.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings
EditorsXingming Sun, Xiaorui Zhang, Zhihua Xia, Elisa Bertino
PublisherSpringer Science and Business Media Deutschland GmbH
Pages17-30
Number of pages14
ISBN (Print)9783030786205
DOIs
StatePublished - 2021
Externally publishedYes
Event7th International Conference on Artificial Intelligence and Security, ICAIS 2021 - Dublin, Ireland
Duration: 19 Jul 202123 Jul 2021

Publication series

NameCommunications in Computer and Information Science
Volume1424
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference7th International Conference on Artificial Intelligence and Security, ICAIS 2021
Country/TerritoryIreland
CityDublin
Period19/07/2123/07/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Dynamic analysis
  • HPC
  • LLVM compilation platform
  • OpenMP parallel program
  • Performance modeling
  • Scalability prediction
  • Static analysis

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