DEMCMC-GPU: An Efficient Multi-Objective Optimization Method with GPU Acceleration on the Fermi Architecture


In this paper, we present an efficient method implemented on Graphics Processing Unit (GPU), DEMCMC-CPU, for multi-objective continuous optimization problems. The DEMCMC-GPU kernel is the DEMCMC algorithm, which combines the attractive features of Differential Evolution (DE) and Markov Chain Monte Carlo (MCMC) to evolve a population of Markov chains toward a diversified set of solutions at the Pareto optimal front in the multi-objective search space. With parallel evolution of a population of Markov chains, the DEMCMC algorithm is a. natural fit for the CPU architecture. The implementation of DEMCMC-CPU on the pre-Fermi architecture can lead to a (similar to)25 speedup on a set of multi-objective benchmark function problems, compare to the CPU-only implementation of DEMONIC. By taking advantage of new cache mechanism in the emerging NVIDIA Fermi CPU architecture, efficient sorting algorithm on CPU, and efficient parallel pseudorandom number generators; the speedup of DEMCMC-GPU can be aggressively improved to (similar to)100.