An Effective Approach for Implementing Sparse Matrix-Vector Multiplication on Graphics Processing Units

Sparse matrix vector multiplication, SpMV, is often a performance bottleneck in iterative solvers. Recently, Graphics Processing Units, GPUs, have been deployed to enhance the performance of this operation. We present a blocked version of the Transposed Jagged Diagonal storage format which is tailored for GPUs, BTJAD. We develop a highly optimized SpMV kernel that takes advantage of the properties of the BTJAD storage format and reuses loaded values of the source vector in the registers of a GPU. Using 62 matrices with different sparsity patterns and executing on an NVIDIA Tesla T10 GPU, we compare the performance of our kernel with that of the SpMV kernels in NVIDIA´s library. Our kernel achieves superior execution throughputs for matrices that are non-uniform in their nonzero row lengths, outperforming the best available kernels by up to 4.67x. When executing on the Fermi class GeForce GTX480 GPU which has a larger register file size, the maximum speedup achieved by our kernel improves to 6.6x.