Sparse matrix-vector multiplication forms the heart of iterative linear solvers used widely in scientific computations (e.g., finite element methods). In such solvers, the matrix-v...
Abstract. We improve the performance of sparse matrix-vector multiplication (SpMV) on modern cache-based superscalar machines when the matrix structure consists of multiple, irregu...
Abstract—The Sparse Matrix-Vector Multiplication kernel exhibits limited potential for taking advantage of modern shared memory architectures due to its large memory bandwidth re...
Kornilios Kourtis, Georgios I. Goumas, Nectarios K...
—Sparse Matrix-Vector multiplication (SpMV) is a very challenging computational kernel, since its performance depends greatly on both the input matrix and the underlying architec...
Vasileios Karakasis, Georgios I. Goumas, Nectarios...
Floating-point Sparse Matrix-Vector Multiplication (SpMXV) is a key computational kernel in scientific and engineering applications. The poor data locality of sparse matrices sig...