CPU/GPU computing for long-wave radiation physics on large GPU clusters

Geoscience simulations rely heavily on high performance computing (HPC) systems. To date, many CPU/GPU heterogeneous HPC systems have been established on which many geoscience simulations have been performed. For most of these simulations on GPU clusters, it can be observed that only the GPU's computational capacity has been exploited to accomplish the arithmetic operations while that of the CPU is ignored, which results in an underutilization of the computing resources within the entire HPC system. In this paper, we perform a long-wave radiation simulation by exploiting the computational capacities of both CPUs and GPUs in the Tianhe-1A supercomputer. First, the long-wave radiation process is accelerated with a Tesla M2050 GPU and achieves significant speedup over the baseline performance on a single Intel X5670 CPU core. Second, a workload distribution scheme based on the speedup feedback is proposed and validated with various workloads. Third, a parallel programming model (MPI+OpenMP/CUDA) is presented and utilized when simulating the radiation physics on large GPU clusters. Finally, we address the computational efficiency issue by exploiting the available computing resources within the Tianhe-1A supercomputer. Experimental results demonstrate that the hybrid version can be accomplished within much less time than that of the CPU counterpart; also, they show similar sensitivity to the temporal resolution of the radiation process.

► We accelerate the long wave radiation process with Tesla M2050 GPU.
► We propose a workload distribution scheme based on the speedup feedback.
► We present a parallel programming model (MPI+OpenMP/CUDA) for GPU clusters.
► We investigate the computational efficiency of CPU/GPU version radiation process.
► We discuss the schemes that GPUs are used in accelerating traditional applications.