Runtime Profiling of OpenCL Workloads Using LLVM-based Code Instrumentation

Yongseung Yu, Seokwon Kang, Yongjun Park

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

Abstract

GPUs, which are widely used high-performance hardware accelerators in heterogeneous computing, and programming models for architectures such as OpenCL and CUDA, have recently been developed to achieve high productivity. LLVM is an open-source compiler infrastructure that enables low-level optimization through LLVM intermediate representation (LLVM IR) in various programming language environments. In this paper, we propose a fully-automatic Dynamic Profiling framework which performs instruction-level analysis through IR-level code instrumentation for typical OpenCL workload kernels.

Original languageEnglish
Title of host publicationProceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1520-1524
Number of pages5
ISBN (Electronic)9781538654576
DOIs
Publication statusPublished - 2019 Feb 22
Event2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of
Duration: 2018 Oct 282018 Oct 31

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2018-October
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2018 IEEE Region 10 Conference, TENCON 2018
Country/TerritoryKorea, Republic of
CityJeju
Period18/10/2818/10/31

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP)(No. NRF-2015R1C1A1A01053844), ICT R&D program of MSIP/IITP (No. 2017-0-00142), and the R&D program of MOTIE/KEIT (No. 10077609).

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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