RIKEN Center for Computational Science High Performance Artificial Intelligence Systems Research Team
Team Leader: Satoshi Matsuoka (Ph.D.)
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:
- 1.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.
- 2.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.
- 3.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.
- 4.Acceleration of HPC algorithms using machine learning:
Accelerate HPC algorithms and applications using empirical models based on machine learning.
Main Research Fields
Related Research Fields
- High Performance Computing
- High Performance Computing
- Computer Architecture
- 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
Papers with an asterisk(*) are based on research conducted outside of RIKEN.
- 1.*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.
- 2.*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.
- 3.*Shweta Salaria, Aleksandr Drozd, Artur Podobas, and Satoshi Matsuoka.:
"Learning Neural Representations for Predicting GPU Performance"
ISC High Performance 2019 (ISC'19), to appear.
- 4.*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.
- 5.*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.
- 6.*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.
- Satoshi Matsuoka
- Team Leader
- Aleksandr Drozd
- Research Scientist
- Shweta Salaria
- Postdoctoral Researcher
RIKEN Center for Computational Science (R-CCS)
Email: matsu [at] acm.org