Efficient dense matrix‐vector multiplication on GPU
Guixia He, Jiaquan Gao, Jun Wang · Concurrency and Computation Practice and Experience · 2018
Summary Given that the dense matrix‐vector multiplication (Ax or ATx) is of great importance in scientific computations, how to accelerate it is investigated on the graphics processing unit (GPU) in this paper. We present a warp‐based implementation of Ax on the GPU, called GEMV‐Adaptive, and a thread‐based implementation of ATx on the GPU, called GEMV‐T‐Adaptive. For our proposed GEMV‐Adaptive and GEMV‐T‐Adaptive, there are the following novelties: (1) an adaptive warp allocation strategy for GEMV‐Adaptive is proposed to assign the optimal warp number for each matrix row, (2) an adaptive thread allocation strategy for GEMV‐T‐Adaptive is designed to assign the optimal thread number to each matrix row, and (3) several optimization schemes are formulated. Experimental results show that the proposed GEMV‐Adaptive and GEMV‐T‐Adaptive mitigate the performance fluctuations of the implementations in the CUBLAS library, always have high performance, and outperform the most recently proposed GEMV and GEMV‐T kernels by Gao et al, respectively, for all test matrices.