6 citations · 27 across the 10 of their papers we have counts for
10 papers
Factorized Krylov subspace methods for solving large Sylvester equations
Yuki Satake, Takeshi Fukaya, Tomohiro Sogabe +1
Krylov subspace methods, such as the Conjugate Gradient (CG) and BiCGSTAB methods, are widely used in scientific computing for solving linear systems. In this study, we propose a n…
Convergence Acceleration of Preconditioned CG Solver Based on Error Vector Sampling for a Sequence of Linear Systems
Takeshi Iwashita, Kota Ikehara, Takeshi Fukaya +1
In this paper, we focus on solving a sequence of linear systems with an identical (or similar) coefficient matrix. For this type of problems, we investigate the subspace correction…
Performance prediction of massively parallel computation by Bayesian inference
Hisashi Kohashi, Harumichi Iwamoto, Takeshi Fukaya +2
A performance prediction method for massively parallel computation is proposed. The method is based on performance modeling and Bayesian inference to predict elapsed time T as a fu…
Accelerating the SpMV kernel on standard CPUs by exploiting the partially diagonal structures
Takeshi Fukaya, Koki Ishida, Akie Miura +2
Sparse Matrix Vector multiplication (SpMV) is one of basic building blocks in scientific computing, and acceleration of SpMV has been continuously required. In this research, we ai…
An Integer Arithmetic-Based Sparse Linear Solver Using a GMRES Method and Iterative Refinement
Takeshi Iwashita, Kengo Suzuki, Takeshi Fukaya
In this paper, we develop a (preconditioned) GMRES solver based on integer arithmetic, and introduce an iterative refinement framework for the solver. We describe the data format f…
White Paper from Workshop on Large-scale Parallel Numerical Computing Technology (LSPANC 2020): HPC and Computer Arithmetic toward Minimal-Precision Computing
Roman Iakymchuk, Daichi Mukunoki, Artur Podobas +15
In numerical computations, precision of floating-point computations is a key factor to determine the performance (speed and energy-efficiency) as well as the reliability (accuracy…
Effect of Mixed Precision Computing on H-Matrix Vector Multiplication in BEM Analysis
Rise Ooi, Takeshi Iwashita, Takeshi Fukaya +2
Hierarchical Matrix (H-matrix) is an approximation technique which splits a target dense matrix into multiple submatrices, and where a selected portion of submatrices are low-rank…
Hierarchical Block Multi-Color Ordering: A New Parallel Ordering Method for Vectorization and Parallelization of the Sparse Triangular Solver in the ICCG Method
Takeshi Iwashita, Senxi Li, Takeshi Fukaya
In this paper, we propose a new parallel ordering method to vectorize and parallelize the sparse triangular solver, which is called hierarchical block multi-color ordering. In this…
Shifted CholeskyQR for computing the QR factorization of ill-conditioned matrices
Takeshi Fukaya, Ramaseshan Kannan, Yuji Nakatsukasa +2
The Cholesky QR algorithm is an efficient communication-minimizing algorithm for computing the QR factorization of a tall-skinny matrix. Unfortunately it has the inherent numerical…
EigenKernel - A middleware for parallel generalized eigenvalue solvers to attain high scalability and usability
Kazuyuki Tanaka, Hiroto Imachi, Tomoya Fukumoto +5
An open-source middleware EigenKernel was developed for use with parallel generalized eigenvalue solvers or large-scale electronic state calculation to attain high scalability and…