Optimizing GPU Cache Policies for MI Workloads
| Autor: | Bradford M. Beckmann, Tsung Tai Yeh, Xianwei Zhang, Alexandru Dutu, Anthony Gutierrez, Onur Kayiran, Srikant Bharadwaj, Matthew D. Sinclair, Michael LeBeane, Sooraj Puthoor, Johnathan Alsop, Brandon Potter |
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| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Set (abstract data type) Hardware_MEMORYSTRUCTURES Computer science Distributed computing 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 02 engineering and technology Cache 01 natural sciences 020202 computer hardware & architecture |
| Zdroj: | IISWC |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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