Many-Core Branch-and-Bound for GPU Accelerators and MIC Coprocessors

Abstract

Coprocessors are increasingly becoming key building blocks of High Performance Computing platforms. These many-core energy-efficient devices boost the performance of traditional processors. On the other hand, Branch-and-Bound (B&B) algorithms are tree-based exact methods for solving to optimality combinatorial optimization problems (COPs). Solving large COPs results in the generation of a very large pool of subproblems and the evaluation of their associated lower bounds. Generating and evaluating those subproblems on coprocessors raises several issues including processor-coprocessor data transfer optimization, vectorization, thread divergence, and so on. In this paper, we investigate the offload-based parallel design and implementation of B&B algorithms for coprocessors addressing these issues. Two major many-core architectures are considered and compared: Nvidia GPU and Intel MIC. The proposed approaches have been experimented using the Flow-Shop scheduling problem and two hardware configurations equivalent in terms of energy consumption: Nvidia Tesla K40 and Intel Xeon Phi 5110P. The reported results show that the GPU-accelerated approach outperforms the MIC offload-based one even in its vectorized version. Moreover, vectorization improves the efficiency of the MIC offload-based approach with a factor of two.