トップページ    イベント・広報    R-CCS Cafe    R-CCS Cafe 第260回(2024年1月19日)

詳細
開催日 2024年1月19日(金)
開催時間 14:00 - 15:45(14:00 - 15:45 講演者3名による講演、15:45 - 自由討論(参加自由))
開催都市 兵庫県神戸市/オンライン
場所

計算科学研究センター(R-CCS)6階講堂/Zoomによる遠隔セミナー

使用言語 発表・スライド共に英語
登壇者

前島 康光

データ同化研究チーム
特別研究員

Serge G. Petiton

University of Lille ; CNRS, France

Nahid Emad

University of Paris Saclay/Versailles, Maison de la Simulation ; LI-PaRAD, France

講演題目・要旨

1st Speaker: Yasumitsu Maejima


Title:
Observing system simulation experiments of a rich phased array weather radar network covering Kyushu for the July 2020 heavy rainfall event
Abstract:
In early summer, a monsoon front called the "Baiu front" yields a rainy season in Japan, and it occasionally causes catastrophic disasters. On July 4, 2020, southern Kumamoto encountered extreme heavy rainfalls associated with the Baiu front and caused catastrophic flooding of River Kuma in southern Kumamoto. This study investigates a potential impact of a rich phased array weather radar (PAWR) network covering Kyushu, Japan on numerical weather prediction (NWP) of this historic heavy rainfall. Perfect-model, identical-twin observing system simulation experiments (OSSEs) with 17 PAWRs are performed by 30-second-upate the local ensemble transform Kalman filter (LETKF) with a regional NWP model known as the Scalable Computing for Advanced Library and Environment-Regional Model (SCALE-RM) at 1-km resolution. Assimilating every 30-second PAWR data significantly improves the heavy rainfall prediction mainly up to 1-hour lead time compared with the no data assimilation experiment.

2nd Speaker: Serge G. Petiton


Title:
Sequences of irregular sparse large matrix computation for iterative methods on Fugaku
Abstract:
Exascale machines are now available, based on several different arithmetic (from 64-bit to 16-32 bit arithmetics, including mixed versions and some that are no longer IEEE compliant) and using different architectures. Recent brain-scale applications, from machine learning and AI for example, manipulate huge graphs or meshes that lead to very sparse nonsymmetrical linear algebra problems. Those applications generate irregularly structured data (graphs, meshes or directly sparse matrices) which allow to distribute along supercomputer nodes a few compressed rows or columns of extreme scale and very sparse matrices but which don’t allow to store any dense vector of the same order on each node, as we usually expected to compute distributed matrix-vector products (even using MapReduce).
In this talk, after a short description of recent evolutions having important impacts on our results, in particular about parallel and distributed iterative methods, I present some results obtained on Fugaku, with Japanese and French colleagues, based on sequences of sparse non-symmetrical matrix products optimized for very irregular sparse and very large matrices. I discuss the performance with respect to the sparsity and the size of the matrices, to some formats to compress the sparse matrices, to the number of process and nodes, and to two different interconnecting network topologies. I also analyze the impact having networks on chip to interconnected some subsets of cores, which don’t share memories, with respect to the sparse irregular patterns of the matrices. I conclude proposing some research perspectives and potential collaborations.

3rd speaker: Nahid Emad


Title:
Parallel Numerical Computation and AI
Abstract:
Many machine learning techniques are strongly linked to linear algebra methods such as those solving the eigenvalue problem or more generally the singular value problem. Sparse computation is, moreover, a subject common to these two fields. We show how to take advantage of these interactions and commonalities to propose new approaches to problem solving in either domain. An innovative machine learning approach based on Unite and Conquer methods, used in linear algebra, will be presented. In addition to its efficiency from an accuracy point of view, the important characteristics of this inherently parallel and scalable technique make it well suited to multi-level and heterogeneous parallel and/or distributed architectures. Experimental results, partly on the Fugaku supercomputer, demonstrating the interest of the approach for efficient data analysis in the case of applications such as clustering, anomaly detection, etc. will be presented.

注意事項

  • 参加の際はPCマイクの音声・ビデオをオフにされるようお願いいたします。
  • 当日の会場環境や通信状態により、やむなく配信を中止・中断する場合がございます。
  • プログラムの内容、時間は予告なく変更される場合があります。
  • ご使用の機器やネットワークの環境によっては、ご視聴いただけない場合がございます。
  • インターネット中継に関する著作権は、主催者及び発表者に帰属します。なお、配信された映像及び音声、若しくはその内容を、理化学研究所の許可無くほかのウェブサイトや著作物等への転載、複製、改変等を行うことを禁じます。

(2023年12月28日)