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We propose a generalized method for adapting and optimizing algorithms for efficient execution on modern graphics processing units (GPU). The method consists of several steps. First, build a control flow graph (CFG) of the algorithm. Next, transform the CFG into a tree of loops and merge non-parallelizable loops into parallelizable ones. Finally, map the resulting loops tree to the tree of GPU computational units, unrolling the algorithm’s loops as necessary for the match. The method provides a convenient and robust mental framework and strategy for GPU code optimization. We demonstrate the method by adapting a backtracking search algorithm to the GPU platform and building an optimized implementation of the ResNeXt-50 neural network.
Vadim G. Bulavintsev andDmitry D. Zhdanov
"Adaptation of algorithms for efficient execution on GPUs", Proc. SPIE 11895, Optical Design and Testing XI, 118950T (9 October 2021); https://doi.org/10.1117/12.2601619
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Vadim G. Bulavintsev, Dmitry D. Zhdanov, "Adaptation of algorithms for efficient execution on GPUs," Proc. SPIE 11895, Optical Design and Testing XI, 118950T (9 October 2021); https://doi.org/10.1117/12.2601619