Optimizing the Linear Fascicle Evaluation Algorithm for Multi-core and Many-core Systems

摘要

Sparse matrix-vector multiplication (SpMV) operations are commonly used in various scientific and engineering applications. The performance of the SpMV operation often depends on exploiting regularity patterns in the matrix. Various representations and optimization techniques have been proposed to minimize the memory bandwidth bottleneck arising from the irregular memory access pattern involved. Among recent representation techniques, tensor decomposition is a popular one used for very large but sparse matrices. Post sparse-tensor decomposition, the new representation involves indirect accesses, making it challenging to optimize for multi-cores and even more demanding for the massively parallel architectures, such as on GPUs.