DQN-based OpenCL workload partition for performance optimization.

Park, Sanghyun; Suh, Taeweon

This paper proposes a deep Q network (DQN)-based method for the workload partition problem in OpenCL. The DQN, a reinforcement learning algorithm, optimizes the workload partition for each processing unit by the self-training, based on the accumulated performance data on the computing environment. Our experiments reveal that the DQN-based partition provides the performance improvement by up to 62.2% and 6.9% in JPEG decoding, compared to the LuxMark-based and target-based partitions, respectively. The DQN is able to capture the low-level contention in slave devices such as caches and memory, and the communication bottleneck between devices, and reflect it to the workload partition ratio.

CACHE memory; REINFORCEMENT learning; MACHINE learning; COMPUTER storage devices; JPEG (Image coding standard)

Journal of Supercomputing, 2019, Vol 75, Issue 8, p4875

0920-8542

Academic Journal

10.1007/s11227-019-02766-0