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R-CCS Cafe
The 247th R-CCS Cafe (Jul 14, 2023)
The 247th R-CCS Cafe (Jul 14, 2023)
Japanese| Date | Fri, Jul 14, 2023 |
|---|---|
| Time | 3:00 pm - 4:30 pm (3 pm - 4 pm Talks, 4 pm - Free discussion and coffee break) |
| City | Kobe, Japan/Online |
| Place | Lecture Hall (6th floor) at R-CCS, Online seminar on Zoom
|
| Language | Presentation Language: English Presentation Material: English |
| Speakers |
Peter Ohm Complex Phenomena Unified Simulation Research Team  Yugo Shimizu Medicinal Chemistry Applied AI Unit  Zhengyang Bai High Performance Artificial Intelligence Systems Research Team  |
Talk Titles and Abstracts
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.
Important Notes
- Please turn off your video and microphone when you join the meeting.
- The broadcasting may be interrupted or terminated depending on the network condition or any other unexpected event.
- The program schedule and contents may be modified without prior notice.
- Depending on the utilized device and network environment, it may not be able to watch the session.
- All rights concerning the broadcasted material will belong to the organizer and the presenters, and it is prohibited to copy, modify, or redistribute the total or a part of the broadcasted material without the previous permission of RIKEN.
（Jul 5, 2023）