Large scale iterative solvers by combining FMM and H-matrices
| Project/Area Number |
16H05859
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| Research Category |
Grant-in-Aid for Young Scientists (A)
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| Allocation Type | Single-year Grants |
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| Research Field |
High performance computing
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
Yokota Rio 東京工業大学, 学術国際情報センター, 准教授 (20760573)
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| Research Collaborator |
Li Xiaoye S. Lawrence Berkeley National Laboratory
Keyes David E. King Abdullah University of Science and Technology
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| Project Period (FY) |
2016-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥6,630,000 (Direct Cost: ¥5,100,000、Indirect Cost: ¥1,530,000)
Fiscal Year 2017: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2016: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
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| Keywords | H行列 / FMM / GPU / LU分解 / ハイパフォーマンス・コンピューティング / アルゴリズム / 数値解法 / 反復法 / 前処理 |
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| Outline of Final Research Achievements |
In FY2016, we extended the FMM to H-matrices and developed a LU decomposition code using H-matrices. The dual tree traversal of exaFMM was used to determine the block cluster tree for arbitrary admissibility conditions, which allowed tasked based parallelization of the compression part of the H-matrix code. In FY2017, we further optimized inner kernels of the H-matrix code and compared H-matrices with multigrid for real applications. The use of batched MAGMA enabled us to maximize the performance of GPUs even for small matrices. The advantage of H-matrices over multigrid depends on the condition number of the matrix, while the H-matrix becomes advantageous as the degree of parallelism increases.
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Report
(3 results)
Research Products
(28 results)
