Introduction of Parallel GPGPU Acceleration Algorithms for the Solution of Radiative Transfer

Abstract

General-purpose computing on graphics processing units (GPGPU) is a recent technique that allows the parallel graphics processing unit (GPU) to accelerate calculations performed sequentially by the central processing unit (CPU). To introduce GPGPU to radiative transfer, the Gauss-Seidel solution of the well-known expressions for 1-D and 3-D homogeneous, isotropic media is selected as a test case. Different algorithms are introduced to balance memory and GPU-CPU communication, critical aspects of GPGPU. Results show that speed-ups of one to two orders of magnitude are obtained when compared to sequential solutions. The underlying value of GPGPU is its potential extension in radiative solvers (e.g., Monte Carlo, discrete ordinates) at a minimal learning curve.

This research was supported by an appointment to the NASA Postdoctoral Program (NPP) at the Langley Research Center, administered by Oak Ridge Associated Universities (ORAU). The authors would like to acknowledge the Center of Computational Research (CCR) of SUNY at Buffalo for the use of their facilities to run the CPU calculations.