Abstract
We propose a variety of batched routines for concurrently processing a large collection of small-size, independent sparse matrixvector products (SpMV) on graphics processing units (GPUs). These batched SpMV kernels are designed to be flexible in order to handle a batch of matrices which differ in size, nonzero count, and nonzero distribution. Furthermore, they support three most commonly used sparse storage formats: CSR, COO and ELL. Our experimental results on a state-of-the-art GPU reveal performance improvements of up to 25 compared to non-batched SpMV routines.
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CITATION STYLE
Anzt, H., Collins, G., Dongarra, J., Flegar, G., & Quintana-Ort, E. S. (2017). Flexible batched sparse matrix-vector product on GPUs. In Proceedings of ScalA 2017: 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems - Held in conjunction with SC 2017: The International Conference for High Performance Computing, Networking, Storage and Analysis. Association for Computing Machinery, Inc. https://doi.org/10.1145/3148226.3148230
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