RIKEN Center for Computational Science High Performance Artificial Intelligence Systems Research Team

Team Principal: Mohamed Wahib (Ph.D.)

Japanese Page

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:

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.Science Enabled by AI:
Extend the capability of AI to qualitatively and quantitatively advance scientific and engineering.
5.Intelligent programming systems:
Use AI to auto-generate programs that can adapt to and withstand the complexity and divergence of hardware design.

Main Research Fields

Informatics

Related Research Fields

High Performance Computing
Parallel Distributed Processing
Computer Architecture

Keywords

High Performance Artificial Intelligence Systems
Intelligent Programming Systems
Performance Modeling of AI Systems e.g. Deep Learning
Scalable Deep Learning
Convergence of AI and Simulation

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.

1. *Zhengyang Bai, Peng Chen, Mohamed Wahib,
"RT-RkNN: Reverse k Nearest Neighbor Queries as a Graphics Ray Casting Problem",
International Conference on Very Large Data Bases, Vol. 19, No. 9, (VLDB 2026)
2. *Chen Zhuang, Lingqi Zhang, Benjamin Brock, Du Wu, Peng Chen, Toshio Endo, Satoshi Matsuoka, Mohamed Wahib,
"SHIRO: Near-Optimal Communication Strategies for Distributed Sparse Matrix Multiplication",
ACM International Conference on Supercomputing 2026 (ICS’26)
3. *Wengang Li, Lingqi Zhang, Toshio Endo, Mohamed Wahib:
"Understanding Routing Mechanism in Mixture-of-Experts Language Models."
International Conference on Learning Representations (ICLR 2026)
4. *Lingqi Zhang, Tengfei Wang, Jiajun Huang, Chen Zhuang, Ivan R. Ivanov, Peng Chen, Toshio Endo, Mohamed Wahib,
"FRUGAL: Pushing GPU Applications Beyond Memory Limits",
Proceedings of the 24th ACM/IEEE International Symposium on Code Generation and Optimization 2026, pp 199-211(CGO'26)
5. *Enzhi Zhang, Peng Chen, Jun Igarashi, Isaac Lyngaas, Rui Zhong, Du Wu, Xiao Wang, Masaharu Munetomo, Mohamed Wahib,
"SHFP: Symmetrical Hierarchical Forest Patching with Pretrained Vision Transformer Encoder for High-Resolution Medical Segmentation",
Advances in Neural Information Processing Systems (NeurIPS 2025)
6. *Mohamed Wahib, Muhammet Abdullah Soytürk, Didem Unat,
"Balanced and Elastic End-to-end Training of Dynamic LLMs",
Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC’25)
7. Xiao Wang, Jong-Youl Choi, Takuya Kurihaya, Isaac Lyngaas, Hong-Jun Yoon, Nasik Muhammad Nafi, Aristeidis Tsaris, Ashwin M. Aji, Maliha Hossain, Ming Fan, Mohamed Wahib, Dali Wang, Peter Thornton, Moetasim Ashfaq, Prasanna Balaprakash, Dan Lu,
"ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling",
Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC’25) Gordon Bell Finalist, Best Paper Award
8. Faveo Hoerold, Ivan Radanov Ivanov, Akash Dhruv, William S. Moses, Anshu Dubey, Mohamed Wahib, Jens Domke,
"RAPTOR: Numerical Profiling of Scientific Applications,Proceedings of the International Conference on High Performance Computing",
Networking, Storage and Analysis (SC’25) Best Reproducibility Advancement Award
9. Enzhi Zhang, Isaac Lyngaas, Peng Chen, Xiao Wang, Jun Igarashi, Yuankai Huo, Masaharu Munetomo, Mohamed Wahib
"Adaptive Patching for High-resolution Image Segmentation with Transformers"
International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2024)
10. Thao Nguyen Truong, Balazs Gerofi, Edgar Josafat Martinez-Noriega, Francois Trahay, Mohamed Wahib
"KAKURENBO:"Adaptively Hiding Samples in Deep Neural Network Training"
Advances in Neural Information Processing Systems 2023 (NeurIPS 2023)

Lab Members

Principal investigator

Mohamed Wahib: Team Principal

Core members

Jun Igarashi: Senior Research Scientist
Emil Vatai: Research Scientist
Zhengyang Bai: Postdoctoral Researcher
Lingqi Zhang: Postdoctoral Researcher
Joao Eduardo Batista: Postdoctoral Researcher
Du Wu: Junior Research Associate
Chen Zhuang: Junior Research Associate
Cong Ma: Junior Research Associate
Balazs Gerofi: Visiting Scientist

Contact Information

Nihonbashi 1-chome Mitsui Building, 15th floor,
1-4-1 Nihonbashi,
Chuo-ku, Tokyo
103-0027, Japan
Email: mohamed.attia@riken.jp