Sparse Matrix-Vector Multiplication

Sparse Matrix-Vector Multiplication (SpMV) is a crucial operation in scientific computing. The need to accelerate this operation comes from its application in Krylov methods on large sparse matrices, whose SpMVs are performed iteratively. We focus on finding a new format for SpMV on the GPU because existing formats do not achieve good performance on large and unstructured matrices such as those coming from Number Field Sieve factorizations.