Improving Structured Grid-Based Sparse Matrix-Vector Multiplication and Gauss–Seidel Iteration on GPDSP

On improving the performance of sparse matrix-vector multiplication

We analyze single-node performance of sparse matrix-vector multiplication by investigating issues of data locality and ne-grained parallelism. We examine the data-locality characteristics of the compressed-sparse-row representation and consider improvements in locality through matrix permutation. Motivated by potential improvements in ne-grained parallelism, we evaluate modiied sparse-matrix re...

Reconfigurable Sparse Matrix-Vector Multiplication on FPGAs

executing memory-intensive simulations, such as those required for sparse matrix-vector multiplication. This effect is due to the memory bottleneck that is encountered with large arrays that must be stored in dynamic RAM. An FPGA core designed for a target performance that does not unnecessarily exceed the memory imposed bottleneck can be distributed, along with multiple memory interfaces, into...

Sparse Matrix-Vector Multiplication on a GPU

Optimizing Sparse Matrix Vector Multiplication on SMPs

We describe optimizations of sparse matrix-vector multiplication on uniprocessors and SMPs. The optimization techniques include register blocking, cache blocking, and matrix reordering. We focus on optimizations that improve performance on SMPs, in particular, matrix reordering implemented using two diierent graph algorithms. We present a performance study of this algorithmic kernel, showing ho...

Sparse Matrix-Vector Multiplication on FPGAs

Floating-point Sparse Matrix-Vector Multiplication (SpMXV) is a key computational kernel in scientic and engineering applications. The poor data locality of sparse matrices signicantly reduces the performance of SpMXV on general-purpose processors, which rely heavily on the cache hierarchy to achieve high performance. The abundant hardware resources on current FPGAs provide new opportunities to...