| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
In this research, we aimed for improving algorithms of tensor decomposition, which is one of building blocks in data science applications. In addition to the traditional mathematical viewpoint, we investigated efficient algorithms based on the HPC viewpoint, in which the characteristics of recent computers are considered. Through this research, we found that a dominant computation in a typical tensor decomposition can be accelerated by appropriately selecting kernels depending on the conditions such as the size of a tensor. We also presented a new efficient algorithm for a matrix computation deeply related to tensor decomposition.
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