Publications Details

CONDITIONAL POINT SAMPLING IMPLEMENTATION FOR THE GPU

Kersting, Luke J.; Olson, Aaron J.; Bossler, Kerry B.

Conditional Point Sampling (CoPS) is a recently developed stochastic media transport algorithm that has demonstrated a high degree of accuracy in 1D and 3D simulations implemented for the CPU in Python. However, it is increasingly important that modern, production-level transport codes like CoPS be adapted for use on next-generation computing architectures. In this project, we describe the creation of a fast and accurate variant of CoPS implemented for the GPU in C++. As an initial test, we performed a code-to-code verification using single-history cohorts, which showed that the GPU implementation matched the original CPU implementation to within statistical uncertainty, while improving the speed by over a factor of 4000. We then tested the GPU implementation for cohorts up to size 64 and compared three variants of CoPS based on how the particle histories are grouped into cohorts: successive, simultaneous, and a successive-simultaneous hybrid. We examined the accuracy-efficiency tradeoff of each variant for 9 different benchmarks, measuring the reflectance and transmittance in a cubic geometry with reflecting boundary conditions on the four non-transmissive or reflective faces. Successive cohorts were found to be far more accurate than simultaneous cohorts for both reflectance (4.3 times) and transmittance (5.9 times), although simultaneous cohorts run more than twice as fast as successive cohorts, especially for larger cohorts. The hybrid cohorts demonstrated speed and accuracy behavior most similar to that of simultaneous cohorts. Overall, successive cohorts were found to be more suitable for the GPU due to their greater accuracy and reproducibility, although simultaneous and hybrid cohorts present an enticing prospect for future research.