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ORCIDTakeshi Fukaya
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Journal Articles
- 2023
[j14]Kengo Suzuki
, Takeshi Fukaya, Takeshi Iwashita:
A novel ILU preconditioning method with a block structure suitable for SIMD vectorization. J. Comput. Appl. Math. 419: 114687 (2023)- [j13]Yingqi Zhao, Takeshi Fukaya, Takeshi Iwashita:
Numerical Behavior of Mixed Precision Iterative Refinement Using the BiCGSTAB Method. J. Inf. Process. 31: 860-874 (2023) - [j12]Hirotoshi Tamori, Takeshi Fukaya, Takeshi Iwashita:
Subspace Correction Preconditioning for Solving a Sequence of Asymmetric Linear Systems Using the Bi-CGSTAB Method. J. Inf. Process. 31: 875-884 (2023) - [j11]Takeshi Iwashita, Kota Ikehara, Takeshi Fukaya, Takeshi Mifune:
Convergence acceleration of preconditioned conjugate gradient solver based on error vector sampling for a sequence of linear systems. Numer. Linear Algebra Appl. 30(6) (2023) - 2022
- [j10]Yingqi Zhao, Takeshi Fukaya, Linjie Zhang, Takeshi Iwashita:
Numerical Investigation into the Mixed Precision GMRES(m) Method Using FP64 and FP32. J. Inf. Process. 30: 525-537 (2022) - [j9]Kengo Suzuki, Takeshi Fukaya, Takeshi Iwashita:
A New AINV Preconditioner for the CG Method in Hybrid CPU-GPU Computing Environment. J. Inf. Process. 30: 755-765 (2022) - [j8]Hisashi Kohashi, Harumichi Iwamoto, Takeshi Fukaya, Yusaku Yamamoto, Takeo Hoshi:
Performance prediction of massively parallel computation by Bayesian inference. JSIAM Lett. 14: 13-16 (2022) - 2020
- [j7]Takeshi Iwashita, Senxi Li, Takeshi Fukaya:
Hierarchical block multi-color ordering: a new parallel ordering method for vectorization and parallelization of the sparse triangular solver in the ICCG method. CCF Trans. High Perform. Comput. 2(2): 84-97 (2020) - [j6]Takeshi Fukaya, Ramaseshan Kannan, Yuji Nakatsukasa, Yusaku Yamamoto, Yuka Yanagisawa:
Shifted Cholesky QR for Computing the QR Factorization of Ill-Conditioned Matrices. SIAM J. Sci. Comput. 42(1): A477-A503 (2020) - 2019
- [j5]Senxi Li, Takeshi Iwashita, Takeshi Fukaya:
Enhancement of Algebraic Block Multi-Color Ordering for ILU Preconditioning and Its Performance Evaluation in Preconditioned GMRES Solver. J. Inf. Process. 27: 201-210 (2019) - 2016
- [j4]Yusaku Yamamoto, Yuji Nakatsukasa, Yuka Yanagisawa, Takeshi Fukaya:
Roundoff error analysis of the CholeskyQR2 algorithm in an oblique inner product. JSIAM Lett. 8: 5-8 (2016) - 2011
- [j3]Jun-ichi Muramatsu, Takeshi Fukaya, Shao-Liang Zhang, Kinji Kimura, Yusaku Yamamoto:
Acceleration of Hessenberg Reduction for Nonsymmetric Eigenvalue Problems in a Hybrid CPU-GPU Computing Environment. Int. J. Netw. Comput. 1(2): 132-143 (2011) - 2010
- [j2]Yusaku Yamamoto, Takeshi Fukaya:
Differential qd algorithm for totally nonnegative Hessenberg matrices: introduction of origin shifts and relationship with the discrete hungry Lotka-Volterra system. JSIAM Lett. 2: 69-72 (2010) - 2009
- [j1]Yusaku Yamamoto, Takeshi Fukaya:
Differential qd algorithm for totally nonnegative band matrices: convergence properties and error analysis. JSIAM Lett. 1: 56-59 (2009)
Conference and Workshop Papers
- 2024
- [c15]Takeshi Fukaya, Yuji Nakatsukasa, Yusaku Yamamoto:
A Cholesky QR type algorithm for computing tall-skinny QR factorization with column pivoting. IPDPS 2024: 63-75 - 2022
- [c14]Takeshi Fukaya:
Distributed Parallel Tall-Skinny QR Factorization: Performance Evaluation of Various Algorithms on Various Systems. PDCAT 2022: 275-287 - 2020
- [c13]Rise Ooi, Takeshi Iwashita, Takeshi Fukaya, Akihiro Ida, Rio Yokota:
Effect of Mixed Precision Computing on H-Matrix Vector Multiplication in BEM Analysis. HPC Asia 2020: 92-101 - [c12]Takeshi Iwashita, Kengo Suzuki, Takeshi Fukaya:
An Integer Arithmetic-Based Sparse Linear Solver Using a GMRES Method and Iterative Refinement. ScalA@SC 2020: 1-8 - 2019
- [c11]Takeshi Fukaya:
An investigation into the impact of the structured QR kernel on the overall performance of the TSQR algorithm. HPC Asia 2019: 81-90 - 2018
- [c10]Takeshi Fukaya, Takeshi Iwashita:
Time-space tiling with tile-level parallelism for the 3D FDTD method. HPC Asia 2018: 116-126 - [c9]Takeshi Fukaya, Toshiyuki Imamura, Yusaku Yamamoto:
A Case Study on Modeling the Performance of Dense Matrix Computation: Tridiagonalization in the EigenExa Eigensolver on the K Computer. IPDPS Workshops 2018: 1113-1122 - 2016
- [c8]Seiji Nagashima, Takeshi Fukaya, Yusaku Yamamoto:
On Constructing Cost Models for Online Automatic Tuning Using ATMathCoreLib: Case Studies through the SVD Computation on a Multicore Processor. MCSoC 2016: 345-352 - 2015
- [c7]Takeshi Fukaya, Toshiyuki Imamura:
Performance Evaluation of the Eigen Exa Eigensolver on Oakleaf-FX: Tridiagonalization Versus Pentadiagonalization. IPDPS Workshops 2015: 960-969 - [c6]Toshiyuki Imamura, Takeshi Fukaya, Yusuke Hirota, Susumu Yamada, Masahiko Machida:
CAHTR: Communication-Avoiding Householder TRidiagonalization. PARCO 2015: 381-390 - [c5]Yosuke Kumagai, Akihiro Fujii, Teruo Tanaka, Yusuke Hirota, Takeshi Fukaya, Toshiyuki Imamura, Reiji Suda:
Performance Analysis of the Chebyshev Basis Conjugate Gradient Method on the K Computer. PPAM (1) 2015: 74-85 - 2014
- [c4]Takeshi Fukaya, Yuji Nakatsukasa, Yuka Yanagisawa, Yusaku Yamamoto:
CholeskyQR2: a simple and communication-avoiding algorithm for computing a tall-skinny QR factorization on a large-scale parallel system. ScalA@SC 2014: 31-38 - [c3]Takeshi Fukaya, Toshiyuki Imamura, Yusaku Yamamoto:
Performance Analysis of the Householder-Type Parallel Tall-Skinny QR Factorizations Toward Automatic Algorithm Selection. VECPAR 2014: 269-283 - 2008
- [c2]Takeshi Fukaya, Yusaku Yamamoto, Shao-Liang Zhang:
A dynamic programming approach to optimizing the blocking strategy for the Householder QR decomposition. CLUSTER 2008: 402-410 - 2007
- [c1]Yusaku Yamamoto, Takeshi Fukaya, Takashi Uneyama, Masami Takata, Kinji Kimura, Masashi Iwasaki, Yoshimasa Nakamura:
Accelerating the Singular Value Decomposition of Rectangular Matrices with the CSX600 and the Integrable SVD. PaCT 2007: 340-345
Parts in Books or Collections
- 2010
- [p1]Yusaku Yamamoto, Takeshi Fukaya:
Dynamic Programming Approaches to Optimizing the Blocking Strategy for Basic Matrix Decompositions. Software Automatic Tuning, From Concepts to State-of-the-Art Results 2010: 69-85
Informal and Other Publications
- 2022
- [i7]Takeshi Iwashita, Kota Ikehara, Takeshi Fukaya, Takeshi Mifune:
Convergence Acceleration of Preconditioned CG Solver Based on Error Vector Sampling for a Sequence of Linear Systems. CoRR abs/2203.08498 (2022) - 2021
- [i6]Takeshi Fukaya, Koki Ishida, Akie Miura, Takeshi Iwashita, Hiroshi Nakashima:
Accelerating the SpMV kernel on standard CPUs by exploiting the partially diagonal structures. CoRR abs/2105.04937 (2021) - [i5]Hisashi Kohashi, Harumichi Iwamoto, Takeshi Fukaya, Yusaku Yamamoto, Takeo Hoshi:
Performance prediction of massively parallel computation by Bayesian inference. CoRR abs/2110.14545 (2021) - 2020
- [i4]Roman Iakymchuk, Daichi Mukunoki, Artur Podobas, Fabienne Jézéquel, Toshiyuki Imamura, Norihisa Fujita, Jens Huthmann, Shuhei Kudo, Yiyu Tan, Jens Domke, Kai Torben Ohlhus, Takeshi Fukaya, Takeo Hoshi, Yuki Murakami, Maho Nakata, Takeshi Ogita, Kentaro Sano, Taisuke Boku:
White Paper from Workshop on Large-scale Parallel Numerical Computing Technology (LSPANC 2020): HPC and Computer Arithmetic toward Minimal-Precision Computing. CoRR abs/2004.04628 (2020) - [i3]Takeshi Iwashita, Kengo Suzuki, Takeshi Fukaya:
An Integer Arithmetic-Based Sparse Linear Solver Using a GMRES Method and Iterative Refinement. CoRR abs/2009.07495 (2020) - 2019
- [i2]Takeshi Iwashita, Senxi Li, Takeshi Fukaya:
Hierarchical Block Multi-Color Ordering: A New Parallel Ordering Method for Vectorization and Parallelization of the Sparse Triangular Solver in the ICCG Method. CoRR abs/1908.00741 (2019) - [i1]Rise Ooi, Takeshi Iwashita, Takeshi Fukaya, Akihiro Ida, Rio Yokota:
Effect of Mixed Precision Computing on H-Matrix Vector Multiplication in BEM Analysis. CoRR abs/1911.00093 (2019)
Coauthor Index
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