理化学研究所 計算科学研究センター

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R-CCS Cafe

R-CCS Cafe は、異分野融合のための足掛かりとして、計算科学研究センター(R-CCS)に集う研究者が井戸端会議的にざっくばらんに議論する場として、毎月2回程度予定しております。興味をお持ちの方は原則どなたでも参加可能です。

  • 目 的: 異分野間の壁を超えた研究協力を促進し、新しい学問分野の開拓を目指すため、 研究者間の情報交換・相互理解の場を提供し、研究協力のきっかけを作る。
  • 会 場:R-CCS 6階講堂もしくは1階セミナー室
  • 言 語:講演は日本語/英語、スライドは英語
  • その他:講演者は他分野の方にも理解できる発表を心掛け、参加者は積極的に質問しましょう。

第187回 第1部
日時: 2019年12月16日(月)、15:30 - 16:10
場所: R-CCS 6階講堂

・講演題目:Scalable fixed-mesh method for simulations of multi-material vehicle structures
・講演者:西口 浩司(複雑現象統一的解法研究チーム)
※発表・スライド共に英語

講演要旨:

In recent years, in the automotive industry, weight reductions are indispensable for complying with carbon dioxide emission regulations. Although automotive companies have been mainly using steel sheets, they want to employ multi-material structures including extrusions, castings, or 3D printings of aluminum alloy or resin to achieve weight reductions. However, the structural design will be more complex because multi-material structures have a higher degree of geometric freedom than sheet metal structures. Therefore, numerical simulations need to play a more critical role in designing optimal vehicle structures.
For the last several decades, a Lagrangian finite element method (FEM) using mainly shell formulation has been the de facto standard in the automotive industry. However, shell formulation cannot numerically model the multi-material structures mentioned above because they do not have a constant thickness. Thus, the continuum formulation has to be applied, but this approach poses two computational problems.
The first problem is that an enormous number of finite elements using continuum formulation is required to discretize the multi-material structures spatially. A scalable method in a massively parallel environment is indispensable for this simulation. Secondly, we need to spend more than a month to generate the finite element mesh of a car body. Therefore, it is challenging to investigate many patterns of vehicle structures.
Considering the background as mentioned above, we focus on a Eulerian finite volume method (FVM) [1] based on continuum formulation [2] using a scalable hierarchical Cartesian mesh method [1]. This Eulerian FVM [1] has the following three advantages. The first one is good scalability [1] in a massively parallel computing environment. Secondly, we can easily generate the computational mesh of a car body only within 10 minutes. We will demonstrate the stiffness analysis of a body-in-white structure, which is spatially discretized by approximately 200 million cells and was computed using 104,520 cores on the K computer. Thirdly, the proposed Eulerian method is easy to couple a conventional finite volume fluid solver.
In future work, we plan to conduct car crash simulations using many patterns of multi-material vehicle structures to study ultralight vehicle structures.
[1] K. Nishiguchi 2019 https://doi.org/10.1002/nme.5954 [2] K. Nishiguchi 2018 https://doi.org/10.1002/nme.5790

第187回 第2部
日時: 2019年12月16日(月)、16:10 - 16:50
場所: R-CCS 6階講堂

・講演題目:System Software for Emerging Hardware Technologies in Computing Systems
・講演者:小柴 篤史(プロセッサ研究チーム)
※発表・スライド共に英語

講演要旨:

In this talk, I will introduce some of my previous work including my Ph.D. thesis, new OS features and middleware to make use of emerging hardware technologies. Emerging hardware devices/features (e.g., FPGA/ASICs, non-volatile memories, Intel SGX) have been widely studied due to strong demands on performance improvement, energy efficiency, and data protection of computer systems. However, due to a lack of system software support, existing computer systems cannot fully utilize these new devices. I have proposed new operating system functions and middleware, which are useful to improve the performance/usability of the devices or analyze an application behavior with them.

第186回
日時: 2019年12月10日(火)、10:00 - 11:00
場所: R-CCS 1階セミナー室

・講演題目:Next generation optics/photonics broadens system architecture aperture
・講演者:Dube, Nicolas (Dr. / Chief Strategist for High-Performance Computing at Hewlett Packard Enterprise)
※発表・スライド共に英語

講演要旨:

This presentation will introduce new optical devices that enable HPC and AI system architectures to free up from cost-prohibitive active optical cables and scale-limiting copper cables. Developed by parallel teams at HPE, VCSEL based and silicon photonics ring resonator technologies both enable passive optics at scale, and can integrate as mid-board, co-packaged or even 3D-stacked optical devices. These technologies set the course for much more capable interconnects, thanks to a significantly reduced cost structure and an energy profile tracking to sub 10 pJ/bit. Application at the system level will then be outlined, including the enablement of the Hyper-X and other multi-dimensional all to all topologies thanks to new components like fiber-shuffles.

第185回 第1部
日時: 2019年12月2日(月)、13:00 - 13:40
場所: R-CCS 6階講堂

・講演題目:Parallel Multigrid Methods on Manycore Clusters with IHK/McKernel
・講演者:中島 研吾(計算科学研究センター 副センター長)
※発表・スライド共に英語
(BlueJeansによる遠隔セミナーとなります)

講演要旨: 詳細を見る

The parallel multigrid method is expected to play an important role in large-scale scientific computing on exa-scale supercomputer systems. Previously we proposed Hierarchical Coarse Grid Aggregation (hCGA), which dramatically improved the performance of the parallel multigrid solver when the number of MPI processes was O(10^4) or more. Because hCGA can handle only two layers of parallel hierarchical levels, the computation overhead due to coarse grid solver may become significant when the number of MPI processes reaches O(10^5)- O(10^6) or more. In the present work, we propose AM-hCGA (Adaptive Multilevel hCGA) that can take into account multiple layers of three or more levels, and show preliminary results using the Oakforest-PACS (OFP) system by JCAHPC. Additionally, we also examine the impact of a lightweight multi-kernel operating system, called IHK/McKernel, for parallel multigrid solvers running on OFP.

This is a joint work with Balazs Gerofi (RIKEN R-CCS), Yutaka Ishikawa (RIKEN R-CCS), and Masashi Horikoshi (Intel).

第185回 第2部
日時: 2019年12月2日(月)、13:40 - 14:20
場所: R-CCS 6階講堂

・講演題目:Task-Parallelism and Dataflow: Programming models for FPGA accelerated HPC
・講演者:佐藤 三久(計算科学研究センター 副センター長/プログラミング環境研究チーム チームリーダー)
※発表・スライド共に英語

講演要旨: 詳細を見る

Recently, FPGA has been attracting attention as an alternative device to accelerate HPC applications. Data flow computing model is a popular abstraction of computing in both fine-grain and coarse-grain from decades, and this model is used as a programming model for FPGA such as Maxler DFE and SPGen (Stream Processor Generator). While it is a kind of “static” data flow model, it might be interesting to extend models by using “dynamic” data flow models as old dataflow architecture to handle the dynamic behavior of systems. On other hands, global programming models to integrate FPGA computing into parallel computing of host processors are also important. OpenMP task and target directives, which are recently introduced in OpenMP 4.5, can be extended to specify the interface to offloaded computation done by FPGA. And, the optimization for FGPA needs different metric such as hardware resources, which are very different from the optimization of CPU and GPU. In this talk, issues on programming models for FPGA accelerated HPC are presented.

第185回 第3部
日時: 2019年12月2日(月)、14:20 - 15:00
場所: R-CCS 6階講堂

・講演題目:Operation improvement towards new service on Fugaku
・講演者:庄司 文由(計算科学研究センター 運用技術部門長)
※発表・スライド共に英語

講演要旨: 詳細を見る

The K computer shut down in August with many achievements. We are now working on the installation and preparation of the operation of the Fugaku supercomputer. In the spring of next year, an early access program that uses a part of Fugaku installed in R-CCS will start. To improve user services, we are now considering "cloud service," which includes collaboration with commercial service providers. The "cloud service" is expected to contribute to improve the usability of Fugaku and increase the number of users and application areas drastically. To achieve highly energy-efficient, we optimize the Fugaku system and its facility operation based on operation data analysis and have to motivate users to increase energy efficiency by using Power API. To steadily advance these new activities, it is more critical to collaborate with research teams and operation division tightly. In this talk, I give a vision of the operation improvement towards new service on Fugaku.

第184回 第1部
日時: 2019年11月25日(月)、15:30 - 16:10
場所: R-CCS 6階講堂

・講演題目:Linear solvers in LQCD application
・講演者:金森 逸作(連続系場の理論研究チーム)
※発表・スライド共に英語

講演要旨: 詳細を見る

Every proton and neutron are made of elementary particles called quark. The dynamics of quarks is described by Quantum Chromo Dynamics (QCD) and it can be formulated on a structure lattice (Lattice QCD or simply LQCD). The bottle neck of LQCD simulation, which a Markov Chain Monte Carlo (MCMC), is solving large sparse linear equation. It appears in both generating configurations and calculating physical observables using configurations. In this talk, I will discuss linear solvers for LQCD application. I will mainly focus on our recent implementation of multigrid type solvers. I will also discuss usage of simd variables in LQCD.

第184回 第2部
日時: 2019年11月25日(月)、16:10 - 16:40
場所: R-CCS 6階講堂

・講演題目:Atomistic modeling of the alternating access mechanism of the mitochondrial ADP/ATP carrier with molecular simulations
・講演者:田村 康一(粒子系生物物理研究チーム)
※発表・スライド共に英語

講演要旨: 詳細を見る

ADP/ATP Carrier (AAC) is a membrane transport protein embedded in the inner mitochondrial membrane and mediates a 1:1 exchange of ADP and ATP. The transporter is known to alternate between the so-called c-state and the m-state in the course of the transport cycle. The crystallographic structure of the c-state has been known since 2003, while that of the m-state is not known until 2019. In 2015, we developed a molecular dynamics (MD) technique to simulate global conformational changes of protein. The method, linear response path following (LRPF), enables us to sample large conformational changes of protein without the prior knowledge of the target conformation. The method is suitable for the study of AAC, as no structural knowledge of the m-state is available when the project is started. Using LRPF simulations, we successfully predicted a model structure for the m-state structure (Tamura and Hayashi, 2017, PLOS ONE). The predicted structure was later validated by the experimentally determined m-state structure.

第183回 第1部
日時: 2019年11月11日(月)、13:00 - 13:40
場所: R-CCS 6階講堂

・講演題目: Convergence of AI/Big Data and HPC
・講演者:佐藤 賢斗(高性能ビッグデータ研究チーム チームリーダー)
※発表・スライド共に英語

講演要旨: 詳細を見る

The High Performance Big Data Research Team at RIKEN Center for Computational Science (RIKEN R-CCS) has been researching and developing system software to facilitate extreme-scale big data processing, machine learning and deep learning for high performance computing (HPC) systems, i.e., convergence of AI/Big Data and HPC. In this talk, Kento Sato introduce several R&D activities for accelerating AI/Big data applications on HPC systems (HPC for AI/Big data) as well as ones for resolving HPC challenges by using these AI/Big data techniques (AI/Big data for HPC).

第183回 第2部
日時: 2019年11月11日(月)、13:40 - 14:20
場所: R-CCS 6階講堂

・講演題目:Structure and dynamics of protein biological functions via X-ray crystallography and molecular dynamics simulations
・講演者:Florence Tama (計算構造生物学研究チーム チームリーダー)
※発表・スライド共に英語

講演要旨: 詳細を見る

High resolution structures of biological molecules provide insights into their functions. Such structures are often derived from X-ray crystallographic experiments, but the information is not complete. Crystals are first cryo-cooled before collecting X-ray data. It has been suggested that, while backbone structures are usually very similar between room-temperature and cryo-temperature, cryo-cooling may hamper biologically relevant dynamics. In addition, X-ray crystallography requires crystals, which might lead to some artifacts as biomolecules might interact which each other, stabilizing specific conformations which might be different than in solution.
Furthermore, to fully understand function of biomolecules, dynamics need also to be considered. However, such X-ray crystallographic structure only represent one specific snapshot of the overall conformational space that biomolecules can cover. Therefore, to complement experimental data, Molecular Dynamics (MD) simulations can be utilized to connect structure, dynamics and function. We will be illustrating those points with studies we have been conducting on different biological systems.