Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박영준 | - |
dc.date.accessioned | 2019-11-24T16:35:30Z | - |
dc.date.available | 2019-11-24T16:35:30Z | - |
dc.date.issued | 2017-04 | - |
dc.identifier.citation | IEICE ELECTRONICS EXPRESS, v. 14, no. 7, Article no. 20161158 | en_US |
dc.identifier.issn | 1349-2543 | - |
dc.identifier.uri | https://www.jstage.jst.go.jp/article/elex/14/7/14_14.20161158/_article | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/113738 | - |
dc.description.abstract | GPU spatial multitasking has been proven to be quite effective at executing different applications concurrently using SM partitioning. However, while it maximizes total throughput, latency-critical applications often cannot meet their deadlines due to the increased execution time. Furthermore, SM partitioning cannot allocate the appropriate L1 cache size per kernel. To solve these problems, this paper proposes a new application-aware resource allocation framework called GPU Fine-Tuner, for assigning appropriate resources to GPU kernels. To minimize the execution time of latency-constrained applications, it assigns them more SMs when performance is not affected. It also increases the cache size of SMs for cache-sensitive kernels using resource borrowing from neighbors for cache-insensitive kernels. Experimental results show that the Fine-Tuner outperforms GPU spatial multitasking with up to 15% less average latency without performance degradation. | en_US |
dc.description.sponsorship | This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2015R1C1A1A01053844 and No. NRF-2015K2A1A2070541) and the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korean government (Motie: Ministry of Trade, Industry & Energy, HRD Program for Software-SoC convergence) (No. N0001883). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG | en_US |
dc.subject | GPGPU | en_US |
dc.subject | multitasking | en_US |
dc.subject | energy | en_US |
dc.subject | resource sharing | en_US |
dc.subject | workload balancing | en_US |
dc.title | Efficient GPU multitasking with latency minimization and cache boosting | en_US |
dc.type | Article | en_US |
dc.relation.no | 7 | - |
dc.relation.volume | 14 | - |
dc.identifier.doi | 10.1587/elex.14.20161158 | - |
dc.relation.page | 1-12 | - |
dc.relation.journal | IEICE ELECTRONICS EXPRESS | - |
dc.contributor.googleauthor | Kim, Jiho | - |
dc.contributor.googleauthor | Chu, Minsung | - |
dc.contributor.googleauthor | Park, Yongjun | - |
dc.relation.code | 2017010142 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF COMPUTER SCIENCE | - |
dc.identifier.pid | yongjunpark | - |
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