Work

Performance Evaluation of Accelerated Real and Complex Multiple-Precision Sparse Matrix-Vector Multiplication

Year: 2024

Type: conference-paper

Abstract: 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 er-rors 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.

Source: International Conference on Engineering and Emerging Technologies (ICEET)

DOI: 10.1109/iceet65156.2024.10913822

Author: Tomonori Kouya

Institution: Shizuoka Institute of Science and Technology

Language: English

Topic: Matrix Theory and Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Japan Society for the Promotion of Science

Award: Development of Efficient Solvers for Ill-conditioned Nonlinear Equations Using Mixed- and Multiple-precision Floating-point Arithmetic