clSPARSE
Joseph L. Greathouse, Kent Knox, Jakub Poła, Kiran Varaganti, Mayank Daga · 2016
Sparse linear algebra is a cornerstone of modern computational science. These algorithms ignore the zero-valued entries found in many domains in order to work on much larger problems at much faster rates than dense algorithms. Nonetheless, optimizing these algorithms is not straightforward. Highly optimized algorithms for multiplying a sparse matrix by a dense vector, for instance, are the subject of a vast corpus of research and can be hundreds of times longer than naïve implementations. Optimized sparse linear algebra libraries are thus needed so that users can build applications without enormous effort.