Sparse computations on GPGPUs

Sparse matrix computations are ubiquitous in scientific computing; General-Purpose computing on Graphics Processing Units (GPGPU) is fast becoming a key component of high performance computing systems. It is therefore natural that a substantial amount of effort has been devoted to implementing sparse matrix computations on GPUs. In this paper, we discuss our work in this field, starting with the data structures we have employed to implement common operations, together with the software architecture we have devised to allow interoperability with existing software packages. To test the effectiveness of our approach we have run experiments with it on two platforms; the experimental results show that our data structures allow us to achieve very good performance results, significantly better than what can be obtained with the most recent version of the CUSPARSE library.