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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:

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
日時: 2018年3月6日(火)、14:30 – 16:30
場所: AICS 6階講堂

・講演者:
Niclas Jansson (KTH Stockholm)
Philipp Schlatter (KTH Stockholm)
※発表・スライド共に英語

講演プログラム: 詳細を見る

【1】講演題目:Efficient Gather-Scatter Operations in NEK5000 using PGAS
講演者:Niclas Jansson (KTH Stockholm)
講演要旨: Gather-scatter operations are one of the key communication kernels used in the computation fluid dynamics (CFD) application Nek5000 for fetching data dependencies (gather), and spreading results to other nodes (scatter). The current implementation used in Nek5000 is the Gather-Scatter library, GS, which utilises different communication strategies: nearest neighbour exchange, message aggregation, and collectives, to efficiently perform communication on a given platform. GS is implemented using non-blocking, two-sided message passing via MPI and the library has proven to scale well to hundreds of thousands of cores. However, the necessity to match sending and receiving messages in the two-sided communication abstraction can quickly increase latency and synchronisation costs for very fine grained parallelism, in particular for the unstructured communication patterns created by unstructured CFD problems. ExaGS is are-implementation of the Gather-Scatter library, with the intent to use the best available programming model for a given architecture. We present our current implementation of ExaGS, based on the one-sided programming model provided by the Partitioned Global Address Space (PGAS) abstraction, using Unified Parallel C (UPC). Using a lock-free design with efficient point-to-point synchronisation primitives, ExaGS is able to reduce communication latency compared to the current two-sided MPI implementation. A detailed description of the library and implemented algorithms are given, together with a performance study of ExaGS when used together with Nek5000, and its co-design benchmarking application Nekbone.

【2】講演題目:Enabling Exascale Fluid Dynamics: Adaptive Mesh Refinement
講演者:Philipp Schlatter (KTH Stockholm)
講演要旨: 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.

第132回
日時: 2018年3月1日(木)、15:30 – 16:30
場所: AICS 6階講堂

・講演題目:The system and application of Sunway TaihuLight
・講演者:Zhao LIU (National Super Computing Center in Wuxi)
※発表・スライド共に英語

講演要旨: 詳細を見る

The Sunway TaihuLight is a Chinese supercomputer which, as of November 2017, is ranked number one in the TOP500 list as the fastest supercomputer in the world with a LINPACK benchmark rating of 93 petaflops. The Sunway TaihuLight uses a total of 40,960 Chinese-designed SW26010 manycore 64-bit RISC processors based on the Sunway architecture for a total of 10,649,600 CPU cores across the entire system. In this talk, I will give a brief introduction on the architecture of the supercomputer and SW26010 processor, as well as the acheivements we have made based on this supercomputer.

第131回
日時: 2018年2月14日(水)、15:30 – 16:30
場所: AICS 6階講堂

・講演題目:Beyond the Gold-Standard in Quantum Chemistry: An Efficient Single-reference Coupled Cluster Theory, with Multi-reference Correlation Effects, for Molecular Energetics and Spectra
・講演者:Rahul Maitra(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: 詳細を見る

Coupled Cluster (CC) Theory is the most accurate many-body ab-initio Quantum Mechanical Theory for predicting molecular energetics and spectra. Although many variants of CC have been developed, all of these methods have their own pros and cons in terms of computational efficiency and structural simplicity and thus their general usability. The single reference coupled cluster (SRCC) theory is conceptually simple and SRCC with singles, doubles and perturbative inclusion of triples excitation --the CCSD(T) method-- has established itself as the 'Gold Standard' in Quantum Chemistry. While there are more rigorous and robust methods, like multi-reference coupled cluster (MRCC) theory, the CCSD(T) remains a popular choice owing to its simplicity in structure. However, due to the steep scaling of the CCSD(T) method, achieving similar accuracy to MRCC with both structural simplicity and a lower scaling behavior remains an important area of research to handle large systems. Introducing the key concepts of SRCC and MRCC, I shall demonstrate how our recently developed methodology, which is termed as "iterative n-body excitation inclusive CCSD", bridges this gap. It accomplishes this by incorporating the essence of MR formalism into a simplified SR method. In my presentation, interesting application to molecules and dispersion bound complexes such as Hydrazine, Pyridine, Peptide-dimers etc. shall also be presented, which will clearly demonstrate the superiority of our current low scaling method against the 'Gold-Standard', while bypassing the difficulty of its high scaling and the complexity of MR structures. Finally, I shall conclude with a roadmap to handle strongly correlated molecular systems.

第130回(特別版 第2回
日時: 2018年2月1日(木)、14:30 – 15:30
場所: AICS 6階講堂

・講演題目:記憶と言語
・講演者:内田 樹(神戸女学院大学 名誉教授)
※発表は日本語。スライドはありません。

講演要旨: 詳細を見る

記憶は蓄積されるものではなく、編集されるものである。また時間は空間的には表象できず、文字の発生によって人間の時間意識が劇的 に変容した。

第129回(特別版 第1回
日時: 2018年1月16日(火)、15:30 – 16:30
場所: AICS 6階講堂

・講演題目:The strong interaction, the mass of the universe and supercomputers
・講演者:Prof.Zoltan Fodor(University of Wuppertal,Germany)
※発表・スライド共に英語

講演要旨: 詳細を見る

In particle physics, the strong interaction is the mechanism responsible for the strong nuclear force. It is described by a quantum field theory and due to its strength the underlying equations are strongly coupled. The only systematic way to solve them is by using leading edge supercomputers. Interestingly enough, the solutions to these equations answer many fundamental questions. They explain the mass of the visible universe. They tell us how the tiny mass difference between neutrons and protons are generated, which is the reason for the ignition of stars. These solutions might even help to answer the question about the origin of dark matter. The synergy between supercomputers and physics will be discussed in detail.

第128回
日時: 2018年1月10日(水)、15:15 – 16:15
場所: AICS 6階講堂

・講演題目:「悩みぐせを解消!・怒りぐせを解消!」気分も見た目も素敵な印象へ
・講演者:坂本弥生 (コミュニケーションサポート・ローズウッド)
※発表・スライド共に日本語

講演要旨: 詳細を見る

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