GPU accelerated Monte Carlo simulation of deep penetration neutron transport

Abstract

Over the last decade, with the increasing performance and programmability of Graphics processing unit (GPU), these units have evolved from specialty hardware to massively parallel general computation devices. Simulation of neutron transport plays an important role in national economical construction and large-scale computing in science and engineering. MC (Monte Carlo) simulation of neutron transport owns great advantage over the determined methods to solve some complex types of particle transport. It is the disadvantage that the computational complexity of MC method is very huge. Due to the independence of samples in MC simulation, the algorithm of MC simulation is in principle well-suited to run on highly parallel GPU. However, the complexities of MC simulation of deep penetration particle transport bring serious difficulties in designing a GPU-based algorithm. We present an algorithm based GPU for MC deep penetration particle transport, in which a particle number based task decomposition method and high efficiency parallel data structure are proposed to match with the underlying GPU architecture. Results demonstrate that with the same computational accuracy as MCNP, MCNP-GPU referred to as MCNP integrated with our algorithm on M2050 achieves 3.53-fold and 7.26-fold speedup respectively by compared with MCNP running on X5670 and X5355.