clSPARSE: A Vendor-Optimized Open-Source Sparse BLAS Library

Joseph L. Greathouse, Kent Knox, Jakub Pola, Kiran Varaganti, Mayank Daga
Advanced Micro Devices, Inc.
International Workshop on OpenCL (IWOCL’16), 2016


   title={clSPARSE: A Vendor-Optimized Open-Source Sparse BLAS Library},

   author={Greathouse, Joseph L and Knox, Kent and Po{l}a, Jakub and Varaganti, Kiran and Daga, Mayank},

   booktitle={Proceedings of the 4th International Workshop on OpenCL},





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 naive implementations. Optimized sparse linear algebra libraries are thus needed so that users can build applications without enormous effort. Hardware vendors release proprietary libraries that are highly optimized for their devices, but they limit interoperability and promote vendor lock-in. Open libraries often work across multiple devices and can quickly take advantage of new innovations, but they may not reach peak performance. The goal of this work is to provide a sparse linear algebra library that offers both of these advantages. We thus describe clSPARSE, a permissively licensed open-source sparse linear algebra library that offers state-of-theart optimized algorithms implemented in OpenCLTM. We test clSPARSE on GPUs from AMD and Nvidia and show performance benefits over both the proprietary cuSPARSE library and the open-source ViennaCL library.
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