An analytical study of GPU computation for solving QAPs by parallel evolutionary computation with independent run
Shigeyoshi Tsutsui, Noriyuki Fujimoto
Abstract
Shigeyoshi Tsutsui, Noriyuki Fujimoto
Abstract
This paper proposes an evolutionary algorithm for solving QAPs with parallel independent run using GPU computation and gives a statistical analysis on how speedup can be attained with this model. With the proposed model, we achieve a GPU computation performance that is nearly proportional to the number of equipped multi-processors (MPs) in the GPUs. We explain these computational results by performing statistical analysis. Regarding performance comparison to CPU computations, GPU computation shows a speedup of x4.4 and x7.9 on average using a single GPU and two GPUs, respectively.
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This paper proposes an evolutionary algorithm for solving QAPs with parallel independent run using GPU computation and gives a statistical analysis on how speedup can be attained with this model. With the proposed model, we achieve a GPU computation performance that is nearly proportional to the number of equipped multi-processors (MPs) in the GPUs. We explain these computational results by performing statistical analysis. Regarding performance comparison to CPU computations, GPU computation shows a speedup of x4.4 and x7.9 on average using a single GPU and two GPUs, respectively.
Key concepts: Speedup, Computation, Parallel computing, Computer science, CUDA, Computational science, Evolutionary computation, Algorithm