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View all- Zhao CGao WNie FZhou H(2022)A Survey of GPU Multitasking Methods Supported by Hardware ArchitectureIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.311563033:6(1451-1463)Online publication date: 1-Jun-2022
SSAGA: SMs Synthesized for Asymmetric GPGPU Applications
Authors: 
Shamik Saha, 
Prabal Basu, Chidhambaranathan Rajamanikkam, Aatreyi Bal, Koushik Chakraborty, Sanghamitra RoyAuthors Info & Claims
Shamik Saha, Prabal Basu, Chidhambaranathan Rajamanikkam, Aatreyi Bal, Koushik Chakraborty, Sanghamitra RoyAuthors Info & ClaimsArticle No.: 49, Pages 1 - 20
Abstract
The emergence of GPGPU applications, bolstered by flexible GPU programming platforms, has created a tremendous challenge in maintaining high energy efficiency in modern GPUs. In this article, we demonstrate that customizing a Streaming Multiprocessor (SM) of a GPU at a lower frequency is significantly more energy efficient compared to employing DVFS on an SM designed for a high-frequency operation. Using a system-level CAD technique, we propose SSAGA—Streaming Multiprocessors Synthesized for Asymmetric GPGPU Applications—an energy-efficient GPU design paradigm. SSAGA creates architecturally identical SM cores, customized for different voltage-frequency domains. Our rigorous cross-layer methodology demonstrates an average of 20% improvement in energy efficiency over a spatially multitasking GPU across a range of GPGPU applications.
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Index Terms
- SSAGA: SMs Synthesized for Asymmetric GPGPU Applications
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Published: 21 April 2017
Accepted: 01 October 2016
Revised: 01 September 2016
Received: 01 May 2016
Published in TODAES Volume 22, Issue 3
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View all- Zhao CGao WNie FZhou H(2022)A Survey of GPU Multitasking Methods Supported by Hardware ArchitectureIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.311563033:6(1451-1463)Online publication date: 1-Jun-2022
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