Generating optimal CUDA sparse matrix-vector product implementations for evolving GPU hardware

The CUDA model for graphics processing units (GPUs) presents the programmer with a plethora of different programming options. These includes different memory types, different memory access methods and different data types. Identifying which options to use and when is a non-trivial exercise. This paper explores the effect of these different options on the performance of a routine that evaluates sparse matrix-vector products (SpMV) across three different generations of NVIDIA GPU hardware. A process for analysing performance and selecting the subset of implementations that perform best is proposed. The potential for mapping sparse matrix attributes to optimal CUDA SpMV implementations is discussed

GPUにおける拡張精度浮動小数点演算を用いた線形計算の研究

筑波大学 (University of Tsukuba)201