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(予定)
  • 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141回
日時: 2018年7月20日(金)、15:30 – 16:30
場所: R-CCS 6階講堂

・講演題目:Computing Matrix Functions on the K Computer
・講演者:William Dawson (量子系分子科学研究チーム)
※発表・スライド共に英語

講演要旨:

In this talk, I will present a new library for computing matrix functions, and will do my best to present it in a way that should be of interest to researchers from R-CCS’s many disciplines. Matrix functions have a number of applications including materials science, the solution of partial differential equations, and the study of real world networks. In this talk, I will review the theory of matrix functions, and discuss several methods of computation. Then I will present the parallelization strategy of our library, including the use of communication avoiding algorithms and task based parallelization. I will discuss usability considerations, including how to build a library in Fortran which can be called from many different programming languages. I will finish by presenting a number of different applications of this library, including quantum chemistry, social network analysis, search engine optimization, and the calculation of the eigendecomposition of a matrix.

第140回
日時: 2018年7月6日(金)
場所: R-CCS 6階講堂

・講演題目:TBD
・講演者:TBD

講演要旨:

TBD

第139回 第二部
日時: 2018年5月31日(木)、14:30 – 16:00
場所: R-CCS 6階講堂

・講演題目:上手な聴き方・接し方
・講演者: 戸田 臨床心理士
※発表・スライド共に日本語

講演要旨: 詳細を見る

「部下が何を考えているのかわからない・・。」、「上司に分かってもらえない・・・。」上手な聴き方(傾聴法)は、悩みの種の上司・部下・同僚のコミュニケーションの緩和に効果的です。 ストレス予防で大切なことは、1番目:セルフケア(自分でストレスに気づいて対処する事)2番目:ラインケア(周囲における気づきと対策)と言われています。周囲の方の“しんどいな”という気持ちに気づく研修です。皆さん是非ご参加ください。

第139回 第一部
日時: 2018年5月31日(木)、13:00 – 14:00
(14:00 – 14:30    Cafe Time)
場所: R-CCS 6階講堂

・講演題目:Reproducibility and accuracy of BLAS routines and their application
・講演者: Roman Iakymchuk(Postdoctoral researcher, KTH, Sweden)
※発表・スライド共に英語

講演要旨: 詳細を見る

On modern multi-core, many-core, and heterogeneous architectures, floating-point computations, especially reductions, may become non-deterministic and, therefore, non-reproducible mainly due to the non-associativity of floating-point operations and the dynamic scheduling. We address the problem of reproducibility in the context of fundamental linear algebra operations -- like the ones included in the Basic Linear Algebra Subprograms (BLAS) library -- and propose algorithms that yields both reproducible and accurate results. We extend this approach to the higher level linear algebra algorithms, e.g. the LU factorization, that are built on top of these BLAS kernels. We present these reproducible and accurate algorithms for the BLAS routines and the LU factorization as well as their implementations in parallel environments such as Intel server CPUs, Intel Xeon Phi, and both NVIDIA and AMD GPUs. We show that the performance of our implementations is comparable to the standard ones.

第138回
日時: 2018年5月30日(水)、14:00 – 15:00
場所: R-CCS 6階講堂

・講演題目:研究員会議の活動紹介(とカーボンナノチューブ光量子デバイス)
・講演者: 加藤 雄一郎(開拓研究本部 加藤ナノ量子フォトニクス研究室 主任研究員 兼 光量子工学研究センター 量子オプトエレクトロニクス研究チーム チームリーダー)
※発表は日本語、スライドは英語

講演要旨: 詳細を見る

理化学研究所研究員会議について知っていますか?60年近く前に発足し、研究所の発展と研究の推進を目的として様々な活動に取り組んできた組織です。現在は異分野交流のためのイベントを実施しているほか、奨励研究課題の審査も担当しています。また、現場の研究員の声を事務に届けるなど、事務部門との連携も進めています。本講演では研究員会議の活動内容をご紹介するとともに、運営の仕組みについても説明いたします。また、私の研究内容についてもご紹介させて頂きます。主にカーボンナノチューブを一本だけ使った光デバイスについて、できるだけ分かりやすくお話ししようと思います。

第137回(R-CCS Cafeとしては第1回)
日時: 2018年4月24日(火)、10:00 – 15:00
場所: R-CCS 1階セミナー室

・講演者:
Thomas Schulthess (Swiss CSCS/ETH Director)
Scott Klasky (Senior Scientist ORNL)
※発表・スライド共に英語

プログラム: 詳細を見る

10:00~12:00 チュートリアル(Part I)
題目:Enhancing Scientific Data Management for Exascale
講演者:Scott Klasky (Senior Scientist ORNL)
講演要旨: As we continue toward exascale, scientific data volume is continuing to scale and becoming more burdensome to manage. In this talk, we lay out opportunities to enhance state of the art data management techniques. We emphasize well principled data compression, and using it to achieve progressive refinement. This can both accelerate I/O and afford the user increased flexibility when she interacts with the data. The formulation naturally maps onto enabling partitioning of the progressively improving-quality representations of a data quantity into different media-type destinations, to keep the highest priority information as close as possible to the computation, and take advantage of deepening memory/storage hierarchies in ways not previously possible. Careful monitoring is requisite to our vision, not only to verify that compression has not eliminated salient features in the data, but also to better understand the performance of massively parallel scientific applications. Increased mathematical rigor would be ideal, to help bring compression on a better-understood theoretical footing, closer to the relevant scientific theory, more aware of constraints imposed by the science, and more tightly error-controlled. Throughout, we highlight pathfinding research we have begun exploring these related topics, and comment toward future work that will be needed.

13:00~13:50 チュートリアル(Part Ⅱ)
題目:Enhancing Scientific Data Management for Exascale (Part Ⅰの続き)
講演者:Scott Klasky (Senior Scientist ORNL)

14:00~15:00 講演
題目:Creating ADIOS-2 for scientific exascale data
講演者:Scott Klasky (Senior Scientist ORNL)
講演要旨: What is Scientific Exascale data? For some it is really big data from scientific experiments and simulations. We use it in Data Intensive Science, which is an acknowledgement that as simulations and experiments continue to generate larger amounts of data, we must turn our attention on how to move, store, manage, analyze and visualize this data in a timely fashion. We are already seeing Petascale simulations produce close to 100 PB per simulation, and we are hearing simulations for Exascale computing trying to approach 100 EB of data per week. Clearly the cost of “write once read never” is becoming too expensive and we must start to create software eco-systems to help us cope with this flood of data from scientific instruments and calculations. We have built the idea of I/O staging in the Adaptable I/O system (ADIOS) to ingest, reduce, and move data on HPC systems and over the WAN to other computational resources, and my talk focuses on creating a software ecosystem which employs these techniques to cope with the extreme amounts of data being produced in the DOE. Furthermore, Exascale data must be re-purposed in time in order to validate the results against physics experiments, such as the ITER fusion tokamak. This creates new challenges which must be explored and developed into an overarching infrastructure for scientific data. Our goal is to create an I/O framework that addresses most of the use-cases arising from both the Exascale challenges and the new scientific instruments coming on-line in the next 10 years.

15:00~16:00 講演
題目:Reflecting on the goal and baseline for exascale computing
講演者:Thomas Schulthess (Swiss CSCS/ETH Director)
講演要旨: Application performance is given much emphasis in discussions of exascale computing. A 50-fold increase in sustained performance over today’s applications running on multi-petaflops supercomputing platforms should be the expected target for exascale systems deployed early next decade. In the present talk, we will reflect on what this means in practice and how much these exascale systems will advance the state of the art. Experience with today’s platforms show that there can be an order of magnitude difference in performance within a given class of numerical methods, depending only on choice of architecture and implementation. This bears the questions on what our baseline is, over which the performance improvements of exascale systems will be measured. Furthermore, how close will these exascale systems bring us to deliver on scientific goals, such as convection resolving global climate simulations or throughput oriented computations for meteorology?

第136回(特別版 第4回)
日時: 2018年3月20日(火)、16:00 – 17:00
場所: AICS 6階講堂

・講演題目:Simulating quantum mechanics on a classical computer
・講演者: Garnet Kin-Lic Chan(カリフォルニア工科大学、教授)
※発表・スライド共に英語

講演要旨: 詳細を見る

Quantum mechanics is the fundamental theory underlying all of chemistry, materials science, and the biological world, yet solving the equations appears to be an exponentially hard problem. Is there hope to simulate the quantum world using classical computers? I will discuss why simulating quantum mechanics is not usually as hard as it first appears, and give some examples of how modern day quantum mechanical calculations are changing our understanding of practical chemistry and materials science.

第135回
日時: 2018年3月14日(水)、15:30 – 16:30
場所: AICS 6階講堂

・講演題目:Towards a science of high performance design
・講演者:Tze Meng Low(Assistant Research Professor, Carnegie Mellon University, USA)
※発表・スライド共に英語

講演要旨: 詳細を見る

Applications are becoming more varied. Architectures are becoming more complex. Yet software implemented by the expert still achieves higher performance than most automatically generated code despite two decades of research in automatic empirical optimization systems. Can we ever get expert-level performance automatically? In this talk, I will discuss how expert-level performance in the dense linear algebra domain can be systematically attained through the use of formal methods and analytical models. Specifically, I will present our analytical models and their underlying hardware principles. I will demonstrate how these analytical models are resilient against changes. I will also share how analytical models from the dense linear algebra domain have been adapted to design high performance implementations for problems in other domains.

第134回 (特別版 第3回
日時: 2018年3月12日(月)、15:00 – 16:00
場所: AICS 6階講堂

・講演題目:深層学習の応用とそれを支える会社の仕組み
・講演者:比戸 将平(株式会社PFN)
※発表・スライド共に日本語

講演要旨: 詳細を見る

Preferred Networksでは深層学習を中心に様々な産業応用に向けて研究開発を行っています。本講演では深層学習の基礎からPFNの最新研究成果、およびそれを支える社内の体制・仕組みについても紹介します。

第133回 第二部
日時: 2018年3月6日(火)、14:30 – 16:30(第一部開始から第二部終了まで)
場所: AICS 6階講堂

講演題目:Enabling Exascale Fluid Dynamics: Adaptive Mesh Refinement
講演者:Philipp Schlatter (KTH Stockholm)
※発表・スライド共に英語

講演要旨: 詳細を見る

The complex nature of turbulent fluid flows implies that the computational resources needed to accurately model problems of industrial and academic relevance is virtually unbounded. Computational Fluid Dynamics (CFD) is therefore a natural driver for exascale computing and has the potential for substantial societal impact, like reduced energy consumption, alternative sources of energy, improved health care, and improved climate models. Extreme-scale CFD poses several cross disciplinary challenges e.g. algorithmic issues in scalable solver design, handling of extreme sized data with compression and in-situ analysis, resilience and energy awareness in both hardware and algorithm design. The wide range of topics makes exascale CFD relevant to a wider HPC audience, extending outside the traditional fluid dynamics community. This talk will summarise the work within the EU funded Horizon 2020 project ExaFLOW, which brings together leading CFD experts and users from both industry and academica: Partners include KTH Royal Institute of Technology (Sweden), Imperial College (UK), University of Southampton (UK), University of Edinburgh (UK), University of Stuttgart (Germany), and industrial partners McLaren (UK) and ACSC (Germany). Special focus is on adaptive mesh refinement (AMR), which is identified as one of the key aspects of large-scale, exascale simulations in CFD: The solution of such problems is a-priori unknown, such that the mesh structure necessarily needs to be solution-dependent, and adjust during the progress of a simulation. In this talk we focus on AMR implemented in Nek5000, a code aimed at direct numerical simulations, based on the spectral element method (SEM). The two main ingredients for AMR are tools for automatic mesh refinement and error estimators which allow for optimal error control. New capabilities in Nek5000 enable the use of the h-refinement method for mesh adaptation, where selected elements are split via quadtree (2D) or octree (3D) structures. Two methods are considred for estimating the error. The first method is local and based on the spectral properties of the solution on each element. These so-called spectral error indicators come with a low overhead and are easily implemented but they provide only a local measure of the error. The second method, on the other hand, is goal-oriented and takes into account both the local properties of the solution and the global dependence of the error in a functional of interest (such as drag or lift) via the resolution of an adjoint problem. These so-called adjoint error estimators are based on a similar work done within the framework of the finite element method. AMR capabilities are demonstrated in Nek5000 and applied to simple steady and unsteady test cases, such as the flow past a cylinder and the lid-driven cavity, in two and three dimensions. The definition and implementation of both error estimation methods are presented and discussed. The results obtained with both methods are compared and are shown to efficiently reduce the number of degrees of freedom required to reach a given tolerance on the solution compared to conforming refinement. Moreover, the gains in terms of mesh generation, accuracy and computational cost are discussed by analyzing the convergence of some functional of interest and the evolution of the mesh as refinement proceeds.