Improving GPU Multitasking Efficiency Using Dynamic Resource Sharing
- Authors
- Kim, Jiho; Cha, Jehee; Park, Jason Jong Kyu; Jeon, Dongsuk; Park, Yongjun
- Issue Date
- Jan-2019
- Publisher
- IEEE COMPUTER SOC
- Keywords
- Computer architecture; GPUs; multi-programmed; resource sharing; spatial multitasking
- Citation
- IEEE COMPUTER ARCHITECTURE LETTERS, v.18, no.1, pp.1 - 5
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE COMPUTER ARCHITECTURE LETTERS
- Volume
- 18
- Number
- 1
- Start Page
- 1
- End Page
- 5
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/148493
- DOI
- 10.1109/LCA.2018.2889042
- ISSN
- 1556-6056
- Abstract
- As GPUs have become essential components for embedded computing systems, a shared GPU with multiple CPU cores needs to efficiently support concurrent execution of multiple different applications. Spatial multitasking, which assigns a different amount of streaming multiprocessors (SMs) to multiple applications, is one of the most common solutions for this. However, this is not a panacea for maximizing total resource utilization. It is because an SM consists of many different sub-resources such as caches, execution units and scheduling units, and the requirements of the sub-resources per kernel are not well matched to their fixed sizes inside an SM. To solve the resource requirement mismatch problem, this paper proposes a GPU Weaver, a dynamic sub-resource management system of multitasking GPUs. GPU Weaver can maximize sub-resource utilization through a shared resource controller (SRC) that is added between neighboring SMs. The SRC dynamically identifies idle sub-resources of an SM and allows them to be used by the neighboring SM when possible. Experiments show that the combination of multiple sub-resource borrowing techniques enhances the total throughput by up to 26 and 9.5 percent on average over the baseline spatial multitasking GPU.
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