Taking advantage of GPU/CPU architectures for sparse Conjugate Gradient solver computation

Solving large sparse linear systems is a time and energy consuming process. This paper presents an efficient exploitation of graphic processing units (GPUs) for accelerating Conjugate Gradient iterative solver (CG). We use the high-level software library PARALUTION for sparse linear algebra on multi/many-core systems, which supports GPU (with CUDA and OpenCL) and Multi-CPU implementations of CG method using different storage formats. We discuss and compare performance using three platforms.