Characterizing Fine-Grained Resource Utilization for Multitasking GPGPU in Cloud Systems
- 주제(키워드) Instruction sets , Graphics processing units , Resource management , Cloud computing , Multitasking , Virtual machining , Registers , GPGPU , resource utilization , cloud system , multitasking , thread block scheduler
- 주제(기타) Computer Science, Information Systems
- 주제(기타) Engineering, Electrical & Electronic
- 주제(기타) Telecommunications
- 설명문(일반) [Cho, Kyungwoon; Bahn, Hyokyung] Ewha Womans Univ, Dept Comp Engn, Seoul 120750, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- OA유형 gold
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000191063
- 본문언어 영어
- Published As https://doi.org/10.1109/ACCESS.2021.3132492
초록/요약
Managing GPGPU resources in cloud systems is challenging as workloads with various resource usage patterns coexist. To determine the co-location of workloads, previous studies have shown that run-time performance profiling and dynamic relocation of workloads is necessary due to interference between workloads. However, this makes instant scheduling difficult and also affects the performance of workload executions. In this article, we show that efficient resource sharing in GPGPU is possible without run-time profiling if resource usage characteristics of workloads are analyzed down to a fine-grained unit level. To extract workload characteristics, we do not perform profiling at scheduling time, but separate profiling from scheduling, thereby reducing the run-time complexity of previous approaches. Specifically, we anatomize the characteristics of various GPGPU workloads and present a new scheduling policy that aims at balancing resource utilization by co-locating workloads with complementary resource demands. Simulation experiments under various virtual machine scenarios show that the proposed policy improves the GPGPU throughput by 119.5% on average and up to 191.7%.
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