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High Performance Artificial Intelligence Systems Research Team
High Performance Artificial Intelligence Systems Research Team
Japanese
Team Leader Satoshi Matsuoka
matsu[at]acm.org (Lab location: Tokyo)
- Please change [at] to @
- 2019
- Team Leader, High Performance Artificial Intelligence Systems Research Team, R-CCS, RIKEN (-present)
- 2018
- Director, R-CCS, RIKEN (-present)
- Specially Appointed Professor, Tokyo Tech (-present)
- 2017
- Director, Real World Big Data Computing Open Innovation Laboratory (RWBC-OIL), AIST and Tokyo Tech
- 2000
- Full Professor, Global Scientific Information and Computing Center (GSIC), the Tokyo Institute of Technology
- 1993
- Ph. D. from the University of Tokyo
Keyword
- High Performance Artificial Intelligence Systems
- Scalable Deep Learning
- Performance Modeling of AI Systems e.g. Deep Learning
- Acceleration of Advanced Deep Learning Algorithms
- Convergence of AI and Simulation
Research summary
The High Performance Artificial Intelligence Systems Research Team is an R-CCS laboratory focusing on convergence of HPC and AI, namely high performance systems, software, and algorithms research for artificial intelligence/machine learning. In collaboration with other research institutes in HPC and AI-related research in Japan as well as globally, it seeks to develop next-generation AI technology that will utilize state-of-the-art high-performance computation facilities, including the post-K. Specifically, we conduct research on next-generation AI systems by focusing on the following topics:
- Extreme speedup and scalability of deep learning: Achieve extreme scalability of deep learning in large-scale supercomputing environments including the post-K, extending the latest algorithms and frameworks for deep learning.
- Performance analysis of deep learning: Accelerate computational kernels for AI over the state-of-the-art hardware architectures by analyzing algorithms for deep learning and other machine learning/AI, measuring their performance and constructing their performance models.
- Acceleration of modern AI algorithms: Accelerate advanced AI algorithms, such as ultra-deep neural networks and high-resolution GAN over images, those that require massive computational resources, using extreme-scale deep learning systems.
- Acceleration of HPC algorithms using machine learning: Accelerate HPC algorithms and applications using empirical models based on machine learning.
Representative papers
- Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, and Satoshi Matsuoka.:
"Large-scale Distributed Second-order Optimization Using Kronecker-factored Approximate Curvature for Deep Convolutional Neural Networks"
2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019), to appear. - Yusuke Nagasaka, Akira Nukada, Ryosuke Kojima, and Satoshi Matsuoka.:
"Batched Sparse Matrix Multiplication for Accelerating Graph Convolutional Networks"
The 19th Annual IEEE/ACM International Symposium in Cluster, Cloud, and Grid Computing (CCGrid 2019), to appear. - Shweta Salaria, Aleksandr Drozd, Artur Podobas, and Satoshi Matsuoka.:
"Learning Neural Representations for Predicting GPU Performance"
ISC High Performance 2019 (ISC'19), to appear. - Pak Markthub, and Mehmet E. Belviranli, Seyong Lee, Jeffrey Vetter, and Satoshi Matsuoka.:
"DRAGON: Breaking GPU Memory Capacity Limits with Direct NVM Access"
Proceedings of the ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'18), pp 32:1--32:13. - Yosuke Oyama, Tal Ben-Nun, Torsten Hoefler, and Satoshi Matsuoka.:
"Accelerating Deep Learning Frameworks with Micro-batches"
2018 IEEE International Conference on Cluster Computing (CLUSTER 2018) pp 402-412. - Yosuke Oyama, Akihiro Nomura, Ikuro Sato, Hiroki Nishimura, Yukimasa Tamatsu, and Satoshi Matsuoka.:
"Predicting Statistics of Asynchronous SGD Parameters for a Large-Scale Distributed Deep Learning System on GPU Supercomputers"
2016 IEEE International Conference on Big Data (Big Data 2016), pp. 66-75.