High Performance Artificial Intelligence Systems Research Team

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 Fugaku. 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

Jens Domke, Emil Vatai, Aleksandr Drozd, Peng Chen, Yosuke Oyama, Lingqi Zhang, Shweta Salaria, Daichi Mukunoki, Artur Podobas, Mohamed Wahib, Satoshi Matsuoka:
"Matrix Engines for HPC: A Performance Study from the Applications Perspective",
35th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2021)
Albert Khaira, Truong Thao Nguyen, Leonardo Bautista Gomez, Ryousei Takano, Rosa Badia, Mohamed Wahib:
"An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks",
30th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2021)
Peng Chen, Mohamed Wahib, Xiao Wang, shinichiro takizawa, Takahiro Hirofuchi, Ogawa Hirotaka, Satoshi Matsuoka:
"Performance Portable Back-projection Algorithms on CPUs: Agnostic Data Locality and Vectorization Optimizations",
35th ACM International Conference on Supercomputing (ICS 2021)
Jun Li, Minjun Li, Zhigang Cai, Francois Trahay, Mohamed Wahib, Balazs Gerofi, Zhiming Liu, Jianwei Liao:
"Intra-page Cache Update in SLC Mode with Partial Programming in High Density SSDs",
50th International Conference on Parallel Processing (ICPP 2021)
Mohamed Wahib, Haoyu Zhang, Truong Thao Nguyen, Aleksandr Drozd, Jens Domke, Lingqi Zhang, Ryousei Takano, Satoshi Matsuoka:
"Scaling Deep Learning Workloads Beyond Memory Capacity",
International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2020)
Lingqi Zhang, Wahib Mohamed, Haoyu Zhang, Matsuoka Satoshi:
"A Study of Single and Multi-device Synchronization Methods in Nvidia GPUs",
34th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2020)
Jens Domke, Satoshi Matsuoka, Ivan R. Ivanov, Yuki Tsushima, Tomoya Yuki, Akihiro Nomura, Shinichi Miura, Nic McDonald, Dennis L. Floyd, Nicolas Dube:
"The First Supercomputer with HyperX Topology: A Viable Alternative to Fat-Trees?",
International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2019)
Chen Peng,Wahib Mohamed,Takizawa Shinichiro,Matsuoka Satoshi:
"A Versatile Software Systolic Execution Model for GPU Memory Bound Kernels",
International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2019)
Jens Domke, Kazuaki Matsumura, Mohamed Wahib, Haoyu Zhang, Keita Yashima,Toshiki Tsuchikawa, Yohei Tsuji, Artur Podobas, Satoshi Matsuoka:
"Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?",
33th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2019)
Shweta Salaria, Aleksandr Drozd, Artur Podobas, Satoshi Matsuoka:
"Learning Neural Representations for Predicting GPU Performance",
ISC High Performance 2019 (ISC 2019)

Team Leader Satoshi Matsuoka

Keyword