Parallel Matrix-Matrix Multiplication Based on HPL with a GPU-Accelerated PC Cluster
Qin Wang, Junichi Ohmura, Axida Shan, Takefumi Miyoshi, Hidetsugu Irie, Tsutomu Yoshinaga · 2010
In this paper, we propose an approach for significantly improving the performance of parallel matrix-matrix multiplication using a GPU-accelerated cluster. For one node, we implement a CPUs-GPU parallel double-precision general matrix-matrix multiplication (dgemm) operation and achieve a performance improvement of 32% as compared to the GPU-only case and 56% as compared to the CPUs-only case. For the entire cluster, we use the overlap GPU acceleration solution to high-performance Linpack (HPL), which eliminates the close dependency between the LU decomposition and the dgemm operation, and achieve a performance improvement of 5.72% as compared to the flat GPU acceleration case.