Research in our group focuses on the development of software infrastructure and algorithms for next generation supercomputers. In particular, we focus on hierarchical low-rank approximation methods such as the fast multipole method (FMM) and H-matrices. Application areas range from classical molecular dynamics to machine learning.

About Us.

Tokyo Tech, GSIC, Advanced Computing Research Division, Advanced Applications of High-Performance Computing Group

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Hierarchical Low-Rank Approximation

Dense matrices appear in many computational applications such as boundary integral methods for solving homogeneous partial differential .....

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Application to Deep Learning

Deep learning does not require very high precision, and this fact is exploited by the recent low-precision hardware from NVIDIA and Google...

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