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R-CCS Cafe

R-CCS Cafe is a place where R-CCS researchers can informally discuss their research beyond the boundary of their discipline to facilitate integration of different disciplines. R-CCS Cafe is held twice a month. All who are interested are welcome to attend.

  • Purpose: To provide a forum for researchers to exchange ideas and information, with the goal to facilitate interdisciplinary collaboration and develop new research fields.
  • Place: Lecture Hall (6th floor) or Seminar Room (1st floor) at R-CCS
  • Language: Presentations will be in Japanese or English. Slides will be in English.

Please make your presentation understandable to researchers in other fields. Questions and active discussion are encouraged.

The 152nd R-CCS Cafe-part I
Date and Time: Thu. Nov. 8, 2018, 13:00 - 14:00
Place: Lecture Hall (6th floor) at R-CCS

Title: Development of computational tools to characterize structure and dynamics of biomolecular systems from single molecule experiments
Speaker: Florence Tama (Team Leader of Computational Structural Biology Research Team)

Presentation Language: English
Presentation Material: English

Abstract: Detail

Biological molecules such as proteins and nucleic acids are responsible for all life activities in the cells, and dysfunction of these molecules can cause severe diseases. These are complex systems consisting of as many as millions of atoms and performing biological functions through dynamical interactions between molecules. Information on the structures of these biological molecules and their dynamics is essential to understand the mechanism of their functions, which can have a huge impact in medicinal applications, particularly in design of new drugs.
The structures and dynamics of large biological complexes can be determined using a variety of experimental techniques, each provides information at different resolution. X-ray crystallography has been providing a large amount of structural information at detailed atomic levels. With recent progress in experimental techniques, Cryo Electron Microscopy (cryo-EM) may be used to obtain 3D structural models near atomic-resolution. In addition, raw data from cryo-EM may now comprise millions of two-dimensional (2D) images of single molecules, which may represent distinct conformations of the molecule. Therefore, dynamics information could also be extracted from the 2D data.
X-ray free electron laser (XFEL) is another exciting new technology that could significantly extend our structural knowledge of biological systems. The first XFEL began operation in 2009 at the SLAC National Accelerator followed by SACLA at RIKEN in 2011. Strong laser light from XFEL enables the measurement of single molecular complexes, without necessity of crystallization. However, for biological systems, due to their low diffraction power, signal to noise ratio is extremely low and interpretation of the data remains challenging.
Given progresses in experimental techniques such as Cryo-EM and XFEL, new computational methods are also now needed to process and interpret data (millions of 2D images) obtained from these single particle experiments. We will discuss the development of hybrid computational methods that combine molecular mechanics and image data processing algorithms to derive structural and dynamical information from cryo-EM and XFEL data.

The 152nd R-CCS Cafe-part Ⅱ
Date and Time: Thu. Nov. 8, 2018, 14:00 - 15:00
Place: Lecture Hall (6th floor) at R-CCS

Title: Bridging the gap between IT users and computer scientists
Speaker: Hiroya Matsuba (Team Leader of HPC Usability Research Team)

Presentation Language: English
Presentation Material: English

Abstract: Detail

HPC Usability Research Team is aiming to realize easy parallel programming so that industry engineers can make parallel simulations. It is important because emerging innovative businesses, such as failure prediction service of production facilities, rely on simulation technologies. This talk covers a new programming framework that enables engineers to write parallel simulation programs with less programming effort than they do with MPI and OpenMP. After introducing our new concept: implementation-agnostic data type (IADT), this talk mention how this concept realizes easy parallel programming. This talk also covers the modeling effort of the cooling facility of the K computer, which a joint project of Operations and Computer Technologies Division and HPC Usability Research Team.

The 152nd R-CCS Cafe-part Ⅲ
Date and Time: Thu. Nov. 8, 2018, 15:15 - 16:15
Place: Lecture Hall (6th floor) at R-CCS

Title: Quantum many-body physics in strongly correlated materials
Speaker: Seiji Yunoki (Team Leader of Computational Materials Science Research Team)

Presentation Language: English
Presentation Material: English

Abstract: Detail

One of the most wealth fields in condensed matter physics is a kind of strongly correlated quantum systems where many-body interactions dominate determining fundamental physical properties. These systems include Hubbard-like models and frustrated quantum spin models, which are relevant, for example, to high-Tc cuprate superconductors and quantum spin liquids. The widely accepted consensus is that there is no ultimate numerical method at the present to solve a reasonably wide range of strongly correlated quantum systems in spatial dimensions higher than one dimension. In this talk, I will first explain what strongly correlated materials are and why we do have to care, and then introduce some of our team research activities, mostly focusing on quantum Monte Carlo and density matrix renormalization group simulations.

The 151st R-CCS Cafe
Date and Time: Thu. Oct. 25, 2018, 15:30 - 16:30
Place: Lecture Hall (6th floor) at R-CCS

Title: Variational and Adiabatically Navigated Quantum Eigensolver
Speaker: Shunji Matsuura(1QBit)

Presentation Language: Japanese
Presentation Material: English

Abstract: Detail

最近の量子コンピューター技術の発展により、近い将来量子コンピューターを用いてどのような計算が可能になるかという研究が活発に行われるようになってきました。現在の量子コンピューターに共通する問題点は実質的なコヒーレンス時間が短い事、そして誤り訂正を行うことができないことです。このため、短時間で計算を終わらせるためのアルゴリズムの開発が非常に重要になってきます。その一つとして注目を集めているのが、量子、古典両方のコンピューターを用いたハイブリッド型のアルゴリズムです。 今回の講演では前半に量子計算に関する現状のレビューを行い、後半で断熱量子計算におけるハイブリッドアルゴリズム(VanQver: Variational and Adiabatically Navigated Quantum Eigensolver)の紹介を行います。特に量子化学への応用に関しての計算結果について説明する予定です。

The 150th R-CCS Cafe-part I
Date and Time: Fri. Oct. 19, 2018, 13:30 - 14:30
Place: Lecture Hall (6th floor) at R-CCS

Title: The Long and Winding Road to Exascale
Speaker: Henry Tufo (Univ. of Colorado at Boulder)

Presentation Language: English
Presentation Material: English

Abstract: Detail

We present a brief history of high-performance computing (HPC) through the lens of the tera-, peta-, and pre-exascale milestones. Key challenges and (potential) solutions are discussed and trends identified. An overview of the current HPC landscape is provided and, when coupled with technology roadmaps and evolving workloads, several possible avenues to practical exascale computing are identified. But many challenges remain and we discuss them in the context of several real world examples.

The 150th R-CCS Cafe-part Ⅱ
Date and Time: Fri. Oct. 19, 2018, 14:30 - 15:00
Place: Lecture Hall (6th floor) at R-CCS

Title: The Evaluation of the Chebyshev smoother and the MSDO-CG of enlarged Krylov subspace method
Speaker: Ryo Yoda (Kogakuin University)

Presentation Language: English
Presentation Material: English

Abstract: Detail

The first topic is the Chebyshev smoother utilized in the multigrid method. The Chebyshev smoother is derived from the Chebyshev polynomials. This smoother is the nonstationary method, unlike Jacobi and Gauss-Seidel method. It has as high parallelism as Jacobi method and has high convergence like Gauss-Seidel method. We made use of the Jacobi, Gauss-Seidel, or Chebyshev iteration as the smoother of the multigrid method, and compared the number of iterations of the multigrid preconditioned conjugate gradient method and the improvement of the eigenvalue distribution. The second topic is the enlarged Krylov subspace method. The enlarged Krylov subspace method divides the solution vector and the search direction set into spatially distinct sets. It enlarges DOF of the search direction space compared to the Krylov subspace method. On the other hand, Multiple Search Direction Conjugate Gradient method (MSD-CG), which is one of the conventional versions of the enlarged Krylov subspace CG method, does not satisfy A-orthogonality in the search direction vectors. The Multiple Search Direction Conjugate Gradient method with Orthogonalization (MSDO-CG) is adapted to A-orthogonalization processes to guarantee faster convergence. We implemented MSDO-CG, and evaluated the convergence behavior.

The 150th R-CCS Cafe-part Ⅲ
Date and Time: Fri. Oct. 19, 2018, 15:30 - 16:30
Place: Lecture Hall (6th floor) at R-CCS

Title: 職域救命説明会
Speaker: Ryoko Kawamura (Health Care Center)

Presentation Language: Japanese
Presentation Material: Japanese

Abstract: Detail

R-CCSでは、年間多くのセミナーが各チーム主催で開催されます。また『京』コンピューターの見学のために年間約1万人の外来者が来場しています。もし、主催するセミナーで心肺停止の方が発生した場合はどのように対応しますか?救急通報を行ってから、8分間何も行わなかった場合は救命の可能性は10%を切ってしまいます。救命の説明と昨年度神戸市市民救命講習を受講された方によるデモンストレーションでR-CCSにおける救命の手順を知るきっかけとしてください。また、今回救急時に使用するために購入したフルフラットになる車いすの取扱い説明も行います。

The 149th R-CCS Cafe-part I
Date and Time: Wed. Oct. 10, 2018, 13:45 - 14:25
Place: Lecture Hall (6th floor) at R-CCS

Title: Filter consists of a few resolvents to solve symmetric definite generalized eigenproblems
Speaker: Hiroshi Murakami (Tokyo Metropolitan University)

Presentation Language: English
Presentation Material: English

Abstract: Detail

By using a filter, those eigenpairs of a symmetric definite generalized eigenproblem "A v = lambda Bv" are solved whose eigenvalues are in a specified real interval "[a,b]". In present study, the filter we use is a polynomial of the real part of a linear combination of a few resolvents. Applications of a few resolvents are given by solving corresponding systems of linear equations. We are to solve these systems of linear equations by some matrix decomposition methods. Since we use a few resolvents, the number of matrix decompositions required is also a few (2 to 4).

The 149th R-CCS Cafe-part II
Date and Time: Wed. Oct. 10, 2018, 14:30 - 15:10
Place: Lecture Hall (6th floor) at R-CCS

Title: 1. The BEAST eigensolver and 2. Recent developments and results for the BEAST eigensolver
Speaker: Martin Galgon (University of Wuppertal), Sarah Huber (University of Wuppertal)

Presentation Language: English
Presentation Material: English

Abstract: Detail

1. We will introduce the BEAST eigensolver, which incorporates both polynomial and contour integral type filter diagonalization schemes for the solution of large sparse eigenproblems. The solver incorporates multiple levels of parallelism as well as a variety of algorithmic advances for fast, accurate, and robust solution. The underlying filter diagonalization schemes and the resulting solver will be introduced here.
2. In this talk we will discuss some of the latest advances of the BEAST eigensolver, including the availability of mixed precision calculations for reduced calculation time while maintaining accuracy. We also show results from recent tests showing the scalability of BEAST to very large problem sizes and numbers of cores.

The 149th R-CCS Cafe-part III
Date and Time: Wed. Oct. 10, 2018, 15:50 - 16:20
Place: Lecture Hall (6th floor) at R-CCS

Title: High-performance implementation of the Jacobi based eigen- and singular value decomposition methods
Speaker: Shuhei Kudo (Large-scale Parallel Numerical Computing Technology Research Team)

Presentation Language: English
Presentation Material: English

Abstract: Detail

In this talk, we explain our progress in developing high-performance implementations of the Jacobi method. The Jacobi method is an old algorithm to compute eigen- and singular value decompositions. Although its large computational cost, it has advantages against the conventional method on recent supercomputers when it is used with the blocking and the parallelizing techniques which improve the arithmetic intensity and reduce the communication cost. Our optimized implementations of the Jacobi method show high strong scalability on recent supercomputers with more than ten thousand nodes and outperform the conventional method.