Conjugate Gradient Solvers with High Accuracy and Bit-wise Reproducibility between CPU and GPU using Ozaki scheme
Author:
Affiliation:
1. RIKEN Center for Computational Science, Hyogo
2. Shibaura Institute of Technology, Japan
3. Tokyo Woman’s Christian University, Japan.
4. Sorbonne University, France
Funder
Horizon 2020 Framework Programme
Japan Society for the Promotion of Science
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3432261.3432270
Reference21 articles.
1. S. W. D. Chien I. B. Peng and S. Markidis. 2019. Posit NPB: Assessing the Precision Improvement in HPC Scientific Applications. (to appear). S. W. D. Chien I. B. Peng and S. Markidis. 2019. Posit NPB: Assessing the Precision Improvement in HPC Scientific Applications. (to appear).
2. Numerical reproducibility for the parallel reduction on multi- and many-core architectures
3. The university of Florida sparse matrix collection
4. MPFR
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1. Comparison of Reproducible Parallel Preconditioned BiCGSTAB Algorithm Based on ExBLAS and ReproBLAS;Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region;2023-02-27
2. Infinite-Precision Inner Product and Sparse Matrix-Vector Multiplication Using Ozaki Scheme with Dot2 on Manycore Processors;Parallel Processing and Applied Mathematics;2023
3. Compensated summation and dot product algorithms for floating-point vectors on parallel architectures: Error bounds, implementation and application in the Krylov subspace methods;Journal of Computational and Applied Mathematics;2022-11
4. Accurate Matrix Multiplication on Binary128 Format Accelerated by Ozaki Scheme;50th International Conference on Parallel Processing;2021-08-09
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