%0 Conference Paper %B 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA '17) %D 2017 %T Flexible Batched Sparse Matrix-Vector Product on GPUs %A Hartwig Anzt %A Gary Collins %A Jack Dongarra %A Goran Flegar %A Enrique S. Quintana-Orti %X We propose a variety of batched routines for concurrently processing a large collection of small-size, independent sparse matrix-vector 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 25X compared to non-batched SpMV routines. %B 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA '17) %I ACM Press %C Denver, CO %8 2017-11 %G eng %R http://dx.doi.org/10.1145/3148226.3148230