Parallel computation of Entropic Lattice Boltzmann method on hybrid CPU–GPU accelerated system

Nowadays, an increasing number of researchers have demonstrated a tendency to choose hybrid CPU–GPU hybrid computing as a high performance computing alternative. Entropic Lattice Boltzmann method (ELBM) parallelization, like many parallel algorithms in the field of rapid scientific and engineering computing, has given rise to much attention for applications of computational fluid dynamics. This study aims to present an efficient implementation of ELBM flow simulation for the D3Q19 model in a hybrid CPU–GPU computing environment, which consists of AMD multi-core CPUs with NVIDIA Graphics Processing Units (GPUs). To overcome the GPU memory size limitation and communication overhead, we propose a set of techniques for the development of an efficient ELBM algorithm for hybrid CPU–GPU computation. Considering the contribution of computational capacity for both the CPU and GPU, an efficient load balancing model is built. The efficiency and accuracy of the proposed approach and established model are tested on a hybrid CPU–GPU accelerated system, where the intensive parts of the computation are dealt with the software framework OpenMP and CUDA. Finally, we show the comparison of resulting computational performance using a hybrid CPU–GPU approach against both a single CPU core and a single GPU device.