One of the hot topics in graphic processing unit (GPU) research is workload scheduling. For parallel workloads with a large scale, the scheduling strategy can affect seriously system performance. To address this, the ...
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One of the hot topics in graphic processing unit (GPU) research is workload scheduling. For parallel workloads with a large scale, the scheduling strategy can affect seriously system performance. To address this, the authors carry out scheduling of data transfer before workload execution scheduling, and propose an optimal scheduling algorithm for GPU workload. By hiding data transfer in workload execution to the maximum extent, the algorithm can reduce wait time, thus achieving a small timespan. They attribute the problem of hiding data transfer in workload execution to the 0-1 knapsack problem, and propose the pseudo-polynomialtimealgorithm based on the Dyer-Zemel algorithm. The authors then deduce the fullypolynomial-timealgorithmscheme for PPTA. By testing on cloud platform equipped with Nvidia Geforce GTX 750, they show that their scheduling algorithm estimates the optimal schedule sequence effectively.
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