| Project/Area Number |
19H04119
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 60090:High performance computing-related
|
|---|
| Research Institution | Institute of Physical and Chemical Research |
|---|
Principal Investigator |
ドンケ イェンス 国立研究開発法人理化学研究所, 計算科学研究センター, チームリーダー (70815480)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
遠藤 敏夫 東京工業大学, 学術国際情報センター, 教授 (80396788)
|
|---|
| Project Period (FY) |
2019-04-01 – 2024-03-31
|
| Project Status |
Completed (Fiscal Year 2023)
|
| Budget Amount *help |
¥17,160,000 (Direct Cost: ¥13,200,000、Indirect Cost: ¥3,960,000)
Fiscal Year 2023: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2022: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2021: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2020: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2019: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
|
|---|
| Keywords | HPC interconnects / routing / hierarchical / supercomputing |
|---|
| Outline of Research at the Start |
The research objective is the invention and development of a novel type of
algorithms, which calculate the communication paths within supercomputer
networks. These novel algorithms will be hierarchical to overcome scalability
challenges of existing algorithms, which are insufficient for future system.
|
| Outline of Annual Research Achievements |
In FY23, we continued with MocCUDA approach to speedup the large scale execution of deep learning frameworks on Fugaku. Additonally, we hosted an intern from ETH Zurich, with whom we published in the high-ranked NSDI'24 conference: "A High-Performance Design, Implementation, Deployment, and Evaluation of The Slim Fly Network", a continuation of our routing work specifically tailored for SimFly networks. Furthermore, we established a collaboration with Univ. of Illinois at Chicago, and our work on co-designing a better Alltoallv for GraphNN and FFT is under review at ICPP. Lastly, together with ETH we designed a holistic toolchain, called EvalNet, which can simulate large-scale networks. The tool performs topology generation, routing, path analysis, and metric evaluation at various scales.
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| Research Progress Status |
令和5年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和5年度が最終年度であるため、記入しない。
|
