RIKEN Center for Computational Science High Performance Artificial Intelligence Systems Research Team
Team Leader: Mohamed Wahib (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 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.Acceleration of HPC algorithms using machine learning:
Accelerate HPC algorithms and applications using empirical models based on machine learning.
- 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
Related Research Fields
- High Performance Computing
- Parallel Distributed Processing
- Computer Architecture
- 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
Papers with an asterisk(*) are based on research conducted outside of RIKEN.
*Jintao Meng, Chen Zhuang, Peng Chen, Mohamed Wahib, Bertil Schmidt, Xiao Wang, Haidong Lan, Dou Wu, Minwen Deng, Yanjie Wei, Shenzhong Feng.:
"Automatic Generation of High-Performance Convolution Kernels on ARM CPUs for Deep Learning"
IEEE Transactions on Parallel & Distributed Systems, vol. 34, April 2022.
*Jintao Meng, Peng Chen, Mingjun Yang, Mohamed Wahib, Yanjie Wei, Shengzhong Feng, Wei Liu, Junzhou Huang.:
"Boosting the Predictive Performance with Aqueous Solubility Dataset Curation”
Nature Scientific Data, March 2022.
*Truong Thao Nguyen, Francois Trahay, Jens Domke, Aleksandr Drozd, Emil Vatai, Jianwei Liao, Mohamed Wahib, Balazs Gerofi.:
"Why Globally Re-shuffle? Revisiting Data Shuffling in Large Scale Deep Learning"
36th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2022).
*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).
*Peng Chen, Mohamed Wahib, Xiao Wang, Takahiro Hirofuchi, Hirotaka Ogawa, Ander Biguri, Richard Boardman, Thomas Blumensath, Satoshi Matsuoka.:
"Scalable FBP Decomposition for Cone-Beam CT Reconstruction"
International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2021).
Fareed Mohammad Qararyah, Mohamed Wahib, Doga Dikbayır, Mehmet Esat Belviranl, Didem Unat.:
"A computational-graph Partitioning Method for Training Memory-constrained DNNs”
Elsevier Parallel Computing, Volume 104 pp. 102-117, July 2021.
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).
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).
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).
- Mohamed Wahib
- Team Leader
- Jun Igarashi
- Senior Scientist
- Balazs Gerofi
- Senior Scientist
- Aleksandr Drozd
- Research Scientist
- Emil Vatai
- Research Scientist
|Seeking a Research Scientist or Postdoctoral Researcher (R-CCS2202)||Open until filled|
Nihonbashi 1-chome Mitsui Building, 15th floor,
Email: mohamed.attia [at] riken.jp