Research On CPU/GPU Synergetic Algorithm For Monte Carlo Deep Penetration Particle Transport

Over the last decade, the performance and programmability of GPU has been improved greatly. Due to general-purpose GPU computing with advantages such as cost-effective, it is paid more and more attention. Many researchers apply GPU in their field of study, so GPU have evolved from specialty hardware to massively parallel general computation devices. Simulation of particle transport plays an important role in national economical construction and large-scale computing in science and engineering. Monte Carlo (MC) simulation of particle transport owns great advantage over the determined methods to solve some complex types of particle transport, however, the computational complexity of MC method is very huge. CPU/GPU hybrid system has brought great many opportunities and challenges for the solution of that problem.On the basis of existing algorithm of MC particle transport, this thesis presents an algorithm based on large-scale hybrid system for MC deep penetration particle transport, which is designed to fit in with the peculiarity of the hybrid system and is well integrated with MCNP. The following is the main work:1) A GPU based MCNP pseudo random number generator is proposed, and in the generator LCG method is used with the same parameters of MCNP pseudo random number generator. First, the generator quickly generates the random number seeds of every thread through jump method, and then parallel generates several random number subsequences on GPU threads. Compared with MCNP pseudo random number generator on Intel X5670 6 cores CPU, the GPU based Pseudo random number generator proposed in this paper achieve 11 fold speedup on NVIDIA M2050 GPU.2) A GPU based algorithm for deep penetration particle transport MC simulation is proposed, and on the basis of MCNP algorithm for particle transport, a particle number based task decomposition method, high efficiency parallel data structure and reduction method are proposed in the algorithm. This thesis presents some methods to eliminate branch and optimize the usage of GPU memory, which effectively improve the performance of algorithm. Compared MCNP running on X5670, the MCNP-GPU which is MCNP integrated with GPU based algorithm for deep penetration particle transport MC simulation achieves up to 3.4-fold speedup on M2050.3) A hybrid system based CPU/GPU synergic algorithm for deep penetration particle transport MC simulation is proposed. In the algorithm a heuristic task decomposition method for CPU and GPU in a hybrid node is proposed, on the basis of which a multi-level task decomposition design to fit in with the peculiarity of the hybrid system is presented, then a multi-level pseudo random number generator and reduction method which are adapt to the multi-level task decomposition. Using MPI and CUDA, the multi-level pseudo random number generator and hybrid system based CPU/GPU synergic algorithm for deep penetration particle transport MC simulation can be integrated with MCNP to form MCNP-Hybrid, and the performance on subsystem of TianHe-1A proves that the synergic algorithm has good performance and scalability.