Runtime Profiling of OpenCL Workloads Using LLVM-based Code Instrumentation

Yongseung Yu; Seokwon Kang; Yongjun Park

doi:10.1109/TENCON.2018.8650390

Runtime Profiling of OpenCL Workloads Using LLVM-based Code Instrumentation

Yongseung Yu, Seokwon Kang, Yongjun Park

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1520-1524
Number of pages	5
ISBN (Electronic)	9781538654576
DOIs	https://doi.org/10.1109/TENCON.2018.8650390
Publication status	Published - 2018 Jul 2
Event	2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of Duration: 2018 Oct 28 → 2018 Oct 31

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume	2018-October
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2018 IEEE Region 10 Conference, TENCON 2018
Country/Territory	Korea, Republic of
City	Jeju
Period	18/10/28 → 18/10/31

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TENCON.2018.8650390

Cite this

Yu, Y., Kang, S., & Park, Y. (2018). Runtime Profiling of OpenCL Workloads Using LLVM-based Code Instrumentation. In Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference (pp. 1520-1524). Article 8650390 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2018-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2018.8650390

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title = "Runtime Profiling of OpenCL Workloads Using LLVM-based Code Instrumentation",

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.",

author = "Yongseung Yu and Seokwon Kang and Yongjun Park",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Region 10 Conference, TENCON 2018 ; Conference date: 28-10-2018 Through 31-10-2018",

year = "2018",

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doi = "10.1109/TENCON.2018.8650390",

language = "English",

series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

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Yu, Y, Kang, S & Park, Y 2018, Runtime Profiling of OpenCL Workloads Using LLVM-based Code Instrumentation. in Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference., 8650390, IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2018-October, Institute of Electrical and Electronics Engineers Inc., pp. 1520-1524, 2018 IEEE Region 10 Conference, TENCON 2018, Jeju, Korea, Republic of, 18/10/28. https://doi.org/10.1109/TENCON.2018.8650390

Runtime Profiling of OpenCL Workloads Using LLVM-based Code Instrumentation. / Yu, Yongseung; Kang, Seokwon; Park, Yongjun.
Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1520-1524 8650390 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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.

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BT - Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference

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Runtime Profiling of OpenCL Workloads Using LLVM-based Code Instrumentation

Abstract

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