Custom High-Performance Vector Code Generation for Data-Specific Sparse Computations
Marcos Horro, Louis-Noël Pouchet, Gabriel Rodríguez, Juan Touriño · 2022
Sparse computations, such as sparse matrix-dense vector multiplication, are notoriously hard to optimize due to their irregularity and memory-boundedness. Solutions to improve the performance of sparse computations have been proposed, ranging from hardware-based such as gather-scatter instructions, to software ones such as generalized and dedicated sparse formats, used together with specialized executor programs for different hardware targets. These sparse computations are often performed on read-only sparse structures: while the data themselves are variable, the sparsity structure itself does not change. Indeed, sparse formats such as CSR have a typically high cost to insert/remove nonzero elements in the representation. The typical use case is to not modify the sparsity during possibly repeated computations on the same sparse structure.