2025/05/27 by Kengo Suzuki, Suzuki, Kengo, Takeshi Iwashita +1
Computer Science · Physics and Astronomy · #FOS: Mathematics #G.1.3 #Matrix Theory and Algorithms #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Numerical Methods and Algorithms
paper · pdf · doi:10.48550/arxiv.2505.20719
openalex publication_date 2025/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Low-precision computing is essential for efficiently utilizing memory bandwidth and computing cores. While many mixed-precision algorithms have been developed for iterative sparse linear solvers, effectively leveraging half-precision (fp16) arithmetic remains challenging. This study introduces a novel nested Krylov approach that integrates the flexible GMRES and Richardson methods in a deeply nested structure, progressively reducing precision from double-precision to fp16 toward the innermost solver. To avoid meaningless computations beyond precision limits, the low-precision inner solvers perform only a few iterations per invocation, while the nested structure ensures their frequent execution. Numerical experiments show that using fp16 in the approach directly enhances solver performance without compromising convergence, achieving speedups of up to 1.65x and 2.42x over double-precision and double-single mixed-precision implementations, respectively. Moreover, the proposed method outperforms or matches other standard Krylov solvers, including restarted GMRES, CG, and BiCGStab methods.