Optimizing Algorithm of Sparse Linear Systems on GPU

Linear equations with large spare coefficient matrices arise in many practical scientific and engineering problems. Previous sparse matrix algorithms for solving linear equations based on single-core CPU are highly complex and time-consuming. To solve such problems, aiming at Jacobi iteration algorithm, in this paper we firstly implement a sparse matrix parallel iteration algorithm on a hybrid multi-core parallel system consisting of CPU and GPU, then an optimization scheme is proposed to carry out performance improvement in two ways, i.e., the multi-level storage structure and the memory access mode of CUDA. Experimental results show that the parallel algorithm on hybrid multi-core system can gain higher performance than the original linear Jacobi iteration algorithm on CPU. In addition, the optimization scheme is effective and feasible.