Runtime Profiling of OpenCL Workloads Using LLVM-based Code Instrumentation
- Authors
- Yu, Yongseung.; Kang, Seokwon; Park, Yongjun
- Issue Date
- Oct-2019
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Dynamic Profiling; GPU; LLVM; OpenCL
- Citation
- IEEE Region 10 Annual International Conference, Proceedings/TENCON, v.2018-October, pp.1520 - 1524
- Indexed
- SCOPUS
- Journal Title
- IEEE Region 10 Annual International Conference, Proceedings/TENCON
- Volume
- 2018-October
- Start Page
- 1520
- End Page
- 1524
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147091
- DOI
- 10.1109/TENCON.2018.8650390
- ISSN
- 2159-3442
- 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.
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