Fast Monte-Carlo Photon Transport Employing GPU-Based Parallel Computation

Abstract

Monte Carlo (MC) is known to be the most accurate dose calculation method. However, MC suffers from high computational cost as a large number of particles have to be simulated to achieve the desired statistical uncertainty. Enhancing computational power by parallelizing the simulation with multiple GPU threads reduces the time required to reach the desired uncertainty in MC simulation. In this article, we present DOSXYZgpu, a GPU implementation of EGSnrc code which is written in CUDA Fortran as an algorithm. This article relies on a well validated and popular code among medical physicists, EGSnrc/DOSXYZnrc. In order to transport particles between two consecutive interactions, we developed an algorithm to handle several thousands of histories per warp. DOSXYZgpu implementation is evaluated with the original sequential EGSnrc/DOSXYZnrc. Maximum speedup of 205 times is achieved while the statistical uncertainty of the simulation is preserved. The $t$ -test statistical analysis indicates that for more than 95% of the voxels there is no significant difference between the results obtained from the GPU and the CPU.