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The 266th R-CCS Cafe (Aug 5, 2024)

Japanese
Details
Date Mon, Aug 5, 2024
Time 3:00 pm - 5:00 pm (3:00 pm - 3:40 pm Talks, 3:40 pm - 4:00 Discussions, 4:00 pm - Free discussion and coffee break)
City Kobe, Japan/Online
Place

Lecture Hall (6th floor) at R-CCS, Online seminar on Zoom

  • If you are not affiliated with R-CCS and would like to attend R-CCS Cafe, please email us at r-ccs-cafe[at]ml.riken.jp.
Language Presentation Language: English
Presentation Material: English
Speakers

Kento Sato

High Performance Big Data Research Team
Team Leader

Kengo Nakajima

Deputy Director

photo:Kengo Nakajima

Talk Titles and Abstracts

1st Speaker: Kento Sato

Title:
Introduction of Research Activities at High Performance Big Data Research Team (FY2024 ver.)
Abstract:
The High Performance Big Data Research Team at RIKEN Center for Computational Science (RIKEN R-CCS) has been working on performance analysis and system software R&D for extreme-scale computing towards convergence of AI/Big Data and HPC as well as fundamental R&Ds for HPC. In this talk, Kento Sato will introduce R&D activities at the team.

2nd Speaker: Kengo Nakajima

Title:
Further Investigation for Optimization of Communication-Computation Overlapping in Parallel Multigrid Methods
Abstract:
Preconditioned iterative methods based on the Krylov subspace technique are widely employed in various scientific and technical computing. When utilizing large-scale parallel computing systems, the communication overhead tends to increase with the growth in the number of nodes, making its reduction a crucial challenge. In parallel FEM/FVM, halo communication and computation overlapping (CC-Overlapping) are commonly employed, often in conjunction with the dynamic loop scheduling feature of OpenMP. In the previous work, the author proposes a method to apply CC-Overlapping to the forward and backward substitutions of the IC(0) smoother of the parallel Conjugate Gradient method preconditioned by Multigrid (MGCG). Using up to 4,096 nodes on Wisteria/BDEC-01 (Odyssey) with A64FX, performance improvement of approximately 40+% was achieved compared to the original implementation. In the present work, further investigation for optimization of CC-Overlapping has been conducted.

Important Notes

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（Jul 25, 2024）