Tongxiang Gu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Tongxiang Gu has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Stef Graillat
Sorbonne Universite, CNRS, LIP6, Paris, Paris, France
2 shared publications - Xiaowen Xu
Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, China
2 shared publications Hengbin An
1 shared publication- Jing Meng
School of Mathematics and Statistics, Taishan University, China
1 shared publication - Ting-Zhu Huang
School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China
1 shared publication Xiaojun Lei
1 shared publication
Latest Publications
- Open Access
- 27 August 2025
- Published by Association for Computing Machinery (ACM)
- p. 63-68
A General Framework for Reproducible Parallel Preconditioned Krylov Methods Using Three BLAS Variants
- 27 August 2025
- Published by Association for Computing Machinery (ACM)
- p. 63-68
Open AccessA General Framework for Reproducible Parallel Preconditioned Krylov Methods Using Three BLAS Variants
- 10 March 2025
- Published by Elsevier in Computer Physics Communications
- Vol. 312
2D 3T PINNs for solving 2-D 3-T heat conduction equations based on physics-informed neural networks
- 10 March 2025
- Published by Elsevier in Computer Physics Communications
- Vol. 312
2D 3T PINNs for solving 2-D 3-T heat conduction equations based on physics-informed neural networks
- Open Access
- 27 February 2023
- Published by Association for Computing Machinery (ACM)
- p. 46-54
Comparison of Reproducible Parallel Preconditioned BiCGSTAB Algorithm Based on ExBLAS and ReproBLAS
Krylov subspace algorithms are important methods for solving linear systems. In order to efficiently solve large-scale linear systems, parallelism techniques are often applied. However, parallelism often enlarge the non-associativity of floating-point operations, which can lead to non-reproducibility of the computations. This paper compares the performance of the parallel preconditioned BiCGSTAB algorithm implemented with two different libraries (ExBLAS and ReproBLAS) that can ensure the reproducibility of computations. To address the effect of the compiler, we explicitly utilize the FMA instructions. Finally, numerical experiments show that based on two BLAS implementations, the BiCGSTAB algorithms are reproducible. By contrast, the BiCGSTAB algorithm based on ExBLAS is more accurate but more time-consuming than the one based on ReproBLAS.
- 27 February 2023
- Published by Association for Computing Machinery (ACM)
- p. 46-54
Open AccessComparison of Reproducible Parallel Preconditioned BiCGSTAB Algorithm Based on ExBLAS and ReproBLAS
Krylov subspace algorithms are important methods for solving linear systems. In order to efficiently solve large-scale linear systems, parallelism techniques are often applied. However, parallelism often enlarge the non-associativity of floating-point operations, which can lead to non-reproducibility of the computations. This paper compares the performance of the parallel preconditioned BiCGSTAB algorithm implemented with two different libraries (ExBLAS and ReproBLAS) that can ensure the reproducibility of computations. To address the effect of the compiler, we explicitly utilize the FMA instructions. Finally, numerical experiments show that based on two BLAS implementations, the BiCGSTAB algorithms are reproducible. By contrast, the BiCGSTAB algorithm based on ExBLAS is more accurate but more time-consuming than the one based on ReproBLAS.
- 1 June 2007
- Published by Elsevier in Applied Mathematics and Computation
- Vol. 189 (2) , 1737-1748
A class of preconditioners based on the -type preconditioning matrices for solving linear systems
- 1 June 2007
- Published by Elsevier in Applied Mathematics and Computation
- Vol. 189 (2) , 1737-1748
A class of preconditioners based on the -type preconditioning matrices for solving linear systems