Runtime Profiling of OpenCL Workloads Using LLVM-based Code Instrumentation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yu, Yongseung. | - |
dc.contributor.author | Kang, Seokwon | - |
dc.contributor.author | Park, Yongjun | - |
dc.date.accessioned | 2022-07-09T06:40:05Z | - |
dc.date.available | 2022-07-09T06:40:05Z | - |
dc.date.created | 2021-05-11 | - |
dc.date.issued | 2019-10 | - |
dc.identifier.issn | 2159-3442 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147091 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Runtime Profiling of OpenCL Workloads Using LLVM-based Code Instrumentation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Yongjun | - |
dc.identifier.doi | 10.1109/TENCON.2018.8650390 | - |
dc.identifier.scopusid | 2-s2.0-85063193546 | - |
dc.identifier.wosid | 000465799100291 | - |
dc.identifier.bibliographicCitation | IEEE Region 10 Annual International Conference, Proceedings/TENCON, v.2018-October, pp.1520 - 1524 | - |
dc.relation.isPartOf | IEEE Region 10 Annual International Conference, Proceedings/TENCON | - |
dc.citation.title | IEEE Region 10 Annual International Conference, Proceedings/TENCON | - |
dc.citation.volume | 2018-October | - |
dc.citation.startPage | 1520 | - |
dc.citation.endPage | 1524 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | Graphics processing unit | - |
dc.subject.keywordPlus | Program compilers | - |
dc.subject.keywordPlus | Code instrumentation | - |
dc.subject.keywordPlus | Dynamic Profiling | - |
dc.subject.keywordPlus | Heterogeneous computing | - |
dc.subject.keywordPlus | High-performance hardware | - |
dc.subject.keywordPlus | Intermediate representations | - |
dc.subject.keywordPlus | Language environment | - |
dc.subject.keywordPlus | LLVM | - |
dc.subject.keywordPlus | OpenCL | - |
dc.subject.keywordPlus | Open source software | - |
dc.subject.keywordAuthor | Dynamic Profiling | - |
dc.subject.keywordAuthor | GPU | - |
dc.subject.keywordAuthor | LLVM | - |
dc.subject.keywordAuthor | OpenCL | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/8650390 | - |
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