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In recent years, machine intelligence (MI) applications have emerged as amajor driver for the computing industry. Optimizing these workloads isimportant but complicated. As memory demands grow and data movement overheadsincreasingly limit performance, determining the best GPU caching policy to usefor a diverse range of MI workloads represents one important challenge. Tostudy this, we evaluate 17 MI applications and characterize their behaviorsusing a range of GPU caching strategies. In our evaluations, we find that thechoice of caching policy in GPU caches involves multiple performance trade-offsand interactions, and there is no one-size-fits-all GPU caching policy for MIworkloads. Based on detailed simulation results, we motivate and evaluate a setof cache optimizations that consistently match the performance of the beststatic GPU caching policies.

Optimizing GPU Cache Policies for MI Workloads