Leveraging index compression techniques to optimize the use of co-processors

The significant presence that many-core devices like GPUs have these days, and their enormous computational power, motivates the study of sparse matrix operations in this hardware. The essential sparse kernels in scientific computing, such as the sparse matrix-vector multiplication (SpMV), usually have many different high-performance GPU implementations. Sparse matrix problems typically imply memory-bound operations, and this characteristic is particularly limiting in massively parallel processors. This work revisits the main ideas about reducing the volume of data required by sparse storage formats and advances in understanding some compression techniques. In particular, we study the use of index compression combined with sparse matrix reordering techniques in CSR and explore other approaches using a blocked format. The systematic experimental evaluation on a large set of real-world matrices confirms that this approach achieves meaningful data storage reductions. Additionally, we find promising results of the impact of the storage reduction on the execution time when using accelerators to perform the mathematical kernels.