Abstract
Warp scheduling policy for GPUs has significant impact on performance since the order of executed warps determines the degree of data cache locality. Greedy warp scheduling policy such as GTO shows better performance than fair scheduling policy for numerous applications. However, cache locality by multiple warps is underutilized when the GTO is adopted, resulting in overall performance degradation. In this paper, we propose a dynamic selective warp scheduling exploiting data locality of workload. Inter-warp locality and intra-warp locality are determined based on the access history information of the L1 data cache. By adjusting scheduling policy dynamically, the performance and cache efficiency are improved compared LRR and GTO significantly. According to our experimental results, the proposed technique provides IPC improvement by 19% and 3.8% over LRR and GTO, respectively.
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This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF2018R1A2B6005740).
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Kim, G.B., Kim, J.M., Kim, C.H. (2019). Dynamic Selective Warp Scheduling for GPUs Using L1 Data Cache Locality Information. In: Park, J., Shen, H., Sung, Y., Tian, H. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2018. Communications in Computer and Information Science, vol 931. Springer, Singapore. https://doi.org/10.1007/978-981-13-5907-1_24
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DOI: https://doi.org/10.1007/978-981-13-5907-1_24
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