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Branch-and-Bound (B&B) algorithms are time intensive tree-based explorationmethods for solving to optimality combinatorial optimization problems. In thispaper, we investigate the use of GPU computing as a major complementary way tospeed up those methods. The focus is put on the bounding mechanism of B&Balgorithms, which is the most time consuming part of their exploration process.We propose a parallel B&B algorithm based on a GPU-accelerated bounding model.The proposed approach concentrate on optimizing data access management tofurther improve the performance of the bounding mechanism which uses large andintermediate data sets that do not completely fit in GPU memory. Extensiveexperiments of the contribution have been carried out on well known FSPbenchmarks using an Nvidia Tesla C2050 GPU card. We compared the obtainedperformances to a single and a multithreaded CPU-based execution. Accelerationsup to x100 are achieved for large problem instances.

A GPU-accelerated Branch-and-Bound Algorithm for the Flow-Shop Scheduling Problem