Architecture- and workload- aware heterogeneous algorithms for sparse matrix vector multiplication
Sivaramakrishna Bharadwaj Indarapu, Manoj Maramreddy, Kishore Kothapalli · 2014
Multiplying a sparse matrix with a vector, denoted spmv, is a fundamental operation in linear algebra with several applications. Hence, efficient and scalable implementation of spmv has been a topic of immense research. Recent efforts are aimed at implementations on GPUs, multicore architectures, and such emerging computational platforms. Owing to the highly irregular nature of spmv, it is observed that GPUs and CPUs can offer comparable performance.