Introduction of Parallel GPGPU Acceleration Algorithms for the Solution of Radiative Transfer, Part B

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.