Optimizing GPU Cache Policies for MI Workloads∗

Johnathan Alsop, Matthew D. Sinclair, Srikant Bharadwaj, Alexandru Dutu, Anthony Gutierrez, Onur Kayiran, Michael Lebeane, Brandon Potter, Sooraj Puthoor, Xianwei Zhang, Tsung Tai Yeh, Bradford M. Beckmann

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

摘要

In recent years, machine intelligence (MI) applications have emerged as a major driver for the computing industry. Optimizing these workloads is important, but complicated. As memory demands grow and data movement overheads increasingly limit performance, determining the best GPU caching policy to use for a diverse range of MI workloads represents one important challenge. To study this, we evaluate 17 MI applications and characterize their behavior using a range of GPU caching strategies. In our evaluations, we find that the choice of caching policy in GPU caches involves multiple performance trade-offs and interactions, and there is no one-size-fits-all GPU caching policy for MI workloads. Based on detailed simulation results, we motivate and evaluate a set of cache optimizations that consistently match the performance of the best static GPU caching policies.

原文English
主出版物標題Proceedings of the 2019 IEEE International Symposium on Workload Characterization, IISWC 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面243-248
頁數6
ISBN(電子)9781728140452
DOIs
出版狀態Published - 11月 2019
事件15th IEEE International Symposium on Workload Characterization, IISWC 2019 - Orlando, United States
持續時間: 3 11月 20195 11月 2019

出版系列

名字Proceedings of the 2019 IEEE International Symposium on Workload Characterization, IISWC 2019

Conference

Conference15th IEEE International Symposium on Workload Characterization, IISWC 2019
國家/地區United States
城市Orlando
期間3/11/195/11/19

指紋

深入研究「Optimizing GPU Cache Policies for MI Workloads∗」主題。共同形成了獨特的指紋。

引用此