JUNE 18–22, 2017

Rio Yokota

GSIC, Tokyo Institute of Technology

Rio Yokota is an Associate Professor at GSIC, Tokyo Institute of Technology. He was a Research Scientist at ECRC, KAUST from September 2011 to March 2015, where he worked on fast multipole methods (FMM) and their application to sparse matrix preconditioners, and also their implementation on large-scale heterogeneous architectures. During his PhD in Mechanical Engineering at Keio University, he worked on the implementation of fast multipole methods on special purpose machines such as MDGRAPE-3, and then on GPUs after CUDA was released. During a post-doc at the University of Bristol, he continued to work on FMM, and was part of the team that won the Gordon Bell prize for price/performance in 2009 using 760 GPUs. During a postdoc with Lorena Barba at Boston University he developed an open source parallel FMM code -- ExaFMM. He is now working on hierarchical low-rank approximation methods and their application to deep learning. Rio is a member of ACM and SIAM.

Speaker at: Algorithms for Extreme Scale in Practice
Wednesday, June 21, 2017, 08:30 am - 10:00 am
  Hierarchical Low-Rank Approximations at Extreme Scale
Wednesday, June 21, 2017, 09:00 am - 09:30 am