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R-CCS Cafe 第247回（2023年7月14日）

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詳細
開催日 2023年7月14日(金)
開催時間 15:00 - 16:30（15:00 - 16:00　講演者を交えたフリーディスカッション、16:00 - 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

Peter OHM

複雑現象統一的解法研究チーム
特任研究員

清水 祐吾

創薬化学AIアプリケーションユニット
研究員

Zhengyang BAI

高性能人工知能システム研究チーム
特別研究員

講演題目・要旨

1st Speaker: Peter Ohm


Title:
Scalable Multiphysics Block Preconditioners for Resistive MHD on ARM Architecture
Abstract:
A base-level mathematical basis for the continuum fluid modeling of dissipative plasma system is the resistive magnetohydrodynamic model. This model requires the solution of the governing partial differential equations (PDEs) describing conservation of mass, momentum, and thermal energy, along with various reduced forms of Maxwell’s equations for the electromagnetic fields. The resulting systems are characterized by strong nonlinear and nonsymmetric coupling of fluid and electromagnetic phenomena, as well as the significant range of time- and length-scales that these interactions produce. These characteristics make scalable and efficient iterative solution, of the resulting poorly-conditioned discrete systems, extremely difficult.

In this talk we consider the development of block preconditioners based on an approximate block factorization that isolates important coupled physics interactions allowing for targeted and efficient solvers for these interactions. These block preconditioners are implemented in the Trilinos framework, and we investigate the performance of these methods on ARM architecture.

2nd Speaker: Yugo Shimizu


Title:
AI-driven drug discovery: from modeling using public database to experimental validation
Abstract:
Recent advances in artificial intelligence (AI) technology have been remarkable, and AI is now being used in various aspects of drug discovery. Public databases such as ChEMBL and PubChem are important data acquisition sources for AI drug discovery. However, there is often a large distance in compound chemical space between compounds in public databases and compounds actually used in drug development, which limits the applicability of AI models trained on public data. Protein–protein interactions (PPIs) are attracting attention as new promising targets for drug discovery, but these problems are likely to occur because it is often difficult to use conventionally used small molecule compounds. This talk will introduce the current state of public data models and methods for improving model accuracy by acquiring new experimental data in the search for medium-sized molecule inhibitors for PPI targets.

3rd Speaker: Zhengyang Bai


Title:
Leveraging Ray Casting for Task Splitting over Processing Elements
Abstract:
Task splitting based on task dependency analysis is a critical aspect of task-based runtime systems, as it significantly impacts performance. An effective task splitting algorithm should allocate tasks to processing elements (PEs) with improved data locality and minimizing the overhead caused by data communication. Traditionally, this analysis is performed using a Task Dependency Graph, which is a sparse matrix with complex algorithms, making it difficult to accelerate. However, we propose a novel approach to enhance performance by modeling the task dependency analysis problem as a visibility problem and employing ray casting to extract the dependencies and split the tasks.
In this presentation, we delve into the concept of using ray casting for task dependency analysis and splitting, explaining why it offers a promising alternative to conventional methods and the potential advantages over existing techniques.

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（2023年7月5日）