2024/12/23 by Tomonori Kouya, Kouya, Tomonori
Engineering · Mathematics · #Advanced Wireless Communication Techniques #FOS: Computer and information sciences #FOS: Mathematics #Numerical Analysis (math.NA) #Performance (cs.PF) #Sparse and Compressive Sensing Techniques #Tensor decomposition and applications
paper · pdf · doi:10.48550/arxiv.2412.17510
openalex publication_date 2024/12/23 · openalex created_date 2024/12/25 · openalex updated_date 2026/07/28
Sparse matrices have recently played a significant and impactful role in scientific computing, including artificial intelligence-related fields. According to historical studies on sparse matrix--vector multiplication (SpMV), Krylov subspace methods are particularly sensitive to the effects of round-off errors when using floating-point arithmetic. By employing multiple-precision linear computation, convergence can be stabilized by reducing these round-off errors. In this paper, we present the performance of our accelerated SpMV using SIMD instructions, demonstrating its effectiveness through various examples, including Krylov subspace methods.