Adaptive Cooperation of Prefetching and Warp Scheduling on GPUs

This paper proposes a new architecture, called Adaptive PREfetching and Scheduling (APRES), which improves cache efficiency of GPUs. APRES relies on the observation that GPU loads tend to have either high locality or strided access patterns across warps. APRES schedules warps so that as many cache h...

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Bibliographic Details
Published in:IEEE transactions on computers 2019-04, Vol.68 (4), p.609-616
Main Authors: Oh, Yunho, Kim, Keunsoo, Yoon, Myung Kuk, Park, Jong Hyun, Park, Yongjun, Annavaram, Murali, Ro, Won Woo
Format: Article
Language:English
Subjects:
cache
Cache storage
data prefetching
GPU
Graphics processing units
Prefetching
Schedules
Scheduling
Static loads
Storage management
warp scheduling
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Summary:This paper proposes a new architecture, called Adaptive PREfetching and Scheduling (APRES), which improves cache efficiency of GPUs. APRES relies on the observation that GPU loads tend to have either high locality or strided access patterns across warps. APRES schedules warps so that as many cache hits are generated as possible before the generation of any cache miss. Without directly predicting future cache hits/misses for each warp, APRES creates a warp group that will execute the same static load shortly and prioritizes the grouped warps. If the first executed warp in the group hits the cache, grouped warps are likely to access the same cache lines. Unless, APRES considers the load as a strided type and generates prefetch requests for the grouped warps. In addition, APRES includes a new dynamic L1 prefetch and data cache partitioning to reduce contentions between demand-fetched and prefetched lines. In our evaluation, APRES achieves 27.8 percent performance improvement.
ISSN:0018-9340
1557-9956
DOI:10.1109/TC.2018.2878671