Effective Implementation of GPU-based Revised Simplex algorithm applying new memory management and cycle avoidance strategies
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Graphics Processing Units (GPUs) with high computational capabilities used as modern parallel platforms to deal with complex computational problems. We use this platform to solve large-scale linear programing problems by revised simplex algorithm. To implement this algorithm, we propose some new memory management strategies. In addition, to avoid cycling because of degeneracy conditions, we use a tabu rule for entering variable selection in the revised simplex algorithm. To evaluate this algorithm, we consider two sets of benchmark problems and compare the speedup factors for these problems. The comparisons demonstrate that the proposed method is highly effective and solve the problems with the maximum speedup factors 165.2 and 65.46 with respect to the sequential version and Matlab Linprog solver respectively.
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