| Project/Area Number |
26730062
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
High performance computing
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Igarashi Ryo 東京大学, 情報基盤センター, 特任講師 (10548895)
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| Project Period (FY) |
2014-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2017: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2016: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 高性能計算 / 量子格子模型 / テンソルネットワーク / 数値線形代数 / 数値計算手法 / アルゴリズム / 対角化 / 特異値分解 / MPS |
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| Outline of Final Research Achievements |
When developing a simulation program for the Matrix Product State (MPS) method, very large number of matrix operations are required. We need not only mere parallelization, but using the property of the matrix to evaluate.
We evaluated Randomized SVD, which approximates the matrix, and presented at the conference that there was no problem with the calculation accuracy due to the introduction of the approximation, and the paper that applied this Randomized SVD to the tensor renormalization group method was published. In addition, the paper of ALPS project , which we co-develop worldwide which contains diagonalization method and MPS programs, was also published.
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| Academic Significance and Societal Importance of the Research Achievements |
近年の高並列なスーパーコンピューター上では、並列計算を効率よく行うために計算方法に近似を入れることがある。その近似が量子格子模型のシミュレーションの場合に精度の問題がないことを確認し、計算速度もこれまでの手法の2倍以上になることがわかった。これは、量子格子模型のシミュレーション手法であるテンソルネットワーク法に広くつかうことのできる手法である。
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