high performance computing on graphics processing units: hgpu.org

Sparse matrix-vector multiplication (SpMV) is an important kernel in many scientific applications and is known to be memory bandwidth limited. On modern processors with wide SIMD and large numbers of cores, we identify and address several bottlenecks which may limit performance even before memory bandwidth: (a) low SIMD efficiency due to sparsity, (b) overhead due to irregular memory accesses, and (c) load-imbalance due to non-uniform matrix structures. We describe an efficient implementation of SpMV on the Intel Xeon Phi Coprocessor, codenamed Knights Corner (KNC), that addresses the above challenges. Our implementation exploits the salient architectural features of KNC, such as large caches and hardware support for irregular memory accesses. By using a specialized data structure with careful load balancing, we attain performance on average close to 90% of KNC’s achievable memory bandwidth on a diverse set of sparse matrices. Furthermore, we demonstrate that our implementation is 3.52x and 1.32x faster, respectively, than the best available implementations on dual Intel Xeon Processor E5-2680 and the NVIDIA Tesla K20X architecture.