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）
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).
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）
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.
日時: 2019年11月11日（月）、14:20 - 15:00
場所: R-CCS 6階講堂
・講演題目：Toward a human-scale whole-brain simulation on the Fugaku computer
・講演者：五十嵐 潤（情報システム本部 計算工学応用開発ユニット）
The human brain is estimated to include about 100 billion neurons and 100 trillion connections. A whole-brain simulation helps us to investigate all interactions between neurons for elucidating brain function and disease. However, a human-scale whole-brain simulation has not been realized due to the lack of sufficient computational resources in the current supercomputers and experimental data for modeling.
In the project of Exploratory Challenge on post-K computer #4-1, to realize human-scale whole-brain simulation on the Fugaku computer, we have been constructing a mammalian whole-brain model consisting of the cerebral cortex, thalamus, cerebellum, and basal ganglia using NEST simulator, and developing an in-house simulator specialized for the whole brain-simulation on the next-generation supercomputers.
In the first part of the presentation, we are going to introduce simulation studies on modulation of a network state by a hierarchical inhibitory circuitry in the primary motor cortex, and neural responses to different spatial size signals in the primary somatosensory cortex. In the second part, we are going to present studies on one-third of human-scale cortical simulation and a full of human-scale cerebellar simulation on the K computer. Finally, we are going to present our future perspective of large-scale brain simulations on Fugaku computer based on the current result in our project.
日時: 2019年10月7日（月）、13:00 - 13:55
場所: R-CCS 6階講堂
・講演題目： Data Processing for Digital Ensemble and Topographical Aspect of Simulation of Disaster
・講演者： 大石 哲（総合防災・減災研究チーム チームリーダー）
Simulation of disaster on HPC requires well scaled program and urban structure data. A very small number of people make well scaled program as an academic achievement, whereas a very large number of people are necessary for applying it into real world through making urban structure data. Urban structure data consists of variety of sources like government, commercial and satellite data. It means that urban structure model is ill-structured. And due to its frequent changes, it is necessary for us to make a sophisticated way of data processing for introducing urban structure alive in numerical simulation. The first part of the talk will deal with the data processing and its applications.
On the other hand, people are anxious about the predictability of disaster simulation because cities are devastated by disasters every year. In the second part of the talk, we will discuss about the predictability of damage of disasters. We are living in concave system, then, a highly accurate simulation result can be obtained when we deal with disaster damage.
日時: 2019年10月7日（月）、13:55 - 14:50
場所: R-CCS 6階講堂
・講演題目： Toward Fugaku: Symmetry to be Unfolded
・講演者： 青木 保道（連続系場の理論研究チーム チームリーダー）
Symmetry is one of the most important notions to understand the fundamental lows of nature. Using a finite degree of freedom, which is unavoidable for numerical simulations, leaves many symmetry broken. In the main application of the field theory research team, which is QCD (Quantum Chromo Dynamics), chiral symmetry is the most important one. This symmetry is not only crucial to understand the QCD dynamics and then the history of the universe, but also important to control the systematic error associated with the discretization. Using a chiral symmetric formulation, which is made possible even with the finite degree of freedom, some important, unsolved problems are expected to be solved on Fugaku. It is especially so for the study of the high temperature phase transition of QCD, for which the chiral symmetric analysis has not been done in complete manner. I will explain the current status and the future plan of the team toward such large scale simulation on Fugaku and discuss what it would bring to us.
日時: 2019年10月7日（月）、15:05 - 16:00
場所: R-CCS 6階講堂
・講演題目： Earth & Planetary Materials (to be) Explored by K and Fugaku Computers & Quantum Beamlines
・講演者： 飯高 敏晃（情報システム本部 計算工学応用開発ユニット）
日時: 2019年9月17日（火）、15:30 - 16:10
場所: R-CCS 6階講堂
・講演題目： Application Specific Multi-Threading for Heterogeneous Systems using High-Level-Synthesis from C code
・講演者： Jens Huthmann（アーキテクチャ研究チーム 特別研究員）
The performance improvement of conventional processor has begun to stagnate in recent years. Because of this, researchers are looking for new possibilities to improve the performance of computing systems. Heterogeneous systems turned out to be a powerful possibility. In the context of this talk, a heterogeneous system consists of a software-programmable processor and a FPGA based configurable hardware accelerator.
Due to their increased complexity, it is more complicated to develop applications for heterogeneous systems than for conventional systems based on a software-programmable processor. For programming the software and hardware parts, different languages have to be used and additional specialised hardware-knowledge is required. Both factors increase the development cost.
This work presents the compiler framework Nymble which allows to program a heterogeneous system with only a single high-level language. In the high-level language the developer only has to select which parts of the application should be executed in hardware. Nymble then generates a program for the software-processor, the configuration of the hardware, and all interfaces between software and hardware.
To hide long memory access latencies, this talk presents an execution model which allows the simultaneous execution of multiple threads in a single accelerator. Additionally, the model enables threads to be dynamically reordered at specific points in the common accelerator pipeline. This capability is used to let other (non-waiting) threads overtake a thread which is waiting for a memory access. Thus, these other threads can execute their calculations independently of the waiting thread to bridge the latency of memory accesses.
The presented execution model dynamically spreads multiple threads over the pipeline. This results in a higher utilisation of the resources by using resources more effectively. Furthermore, the simultaneous execution of multiple threads can achieve similar throughput as multiple copies of a single-threaded accelerator running in parallel.
It makes it possible to combine the improved throughput of multiple copies with the increased efficiency of simultaneous threads in a single accelerator. Thread reordering allows the new model to be effectively used with a cached shared-memory.
In comparison, between four copies of a single-threaded accelerator and a multi-thread accelerator with four thread (both created by Nymble), a resource efficiency of up to factor 2.6x can be achieved. At the same time, four simultaneous threads can be up to 4x as fast as four threads executed consecutively on a single accelerator. Compared to other, more optimised compilers, Nymble can still achieve up to 2x faster runtime with 1.5x resource efficiency.
日時: 2019年9月17日（火）、16:10 - 16:50
場所: R-CCS 6階講堂
・講演題目： Using Field-Programmable Gate Arrays to Explore Different Numerical Representation: A Use-Case on POSITs
・講演者： Artur Podobas (プロセッサ研究チーム 特別研究員)
The inevitable end of Moore’s law motivates researchers to re-think many of the historical architectural decisions. Among these decisions we find the representation of floating-point numbers, which has remained unchanged for nearly three decades. Chasing better performance, lower power consumption or improved accuracy, researches today are actively searching for smaller and/or better representations. Today, a multitude of different representations are found in the specialized (e.g. Deep-Learning) applications as well as for general-purpose applications (e.g. POSITs).
However, despite their claimed strengths, alternative representations remain difficult to evaluate empirically. There are software approaches and emulation libraries available, but their sluggishness only allows the smallest of inputs to be evaluated and understood.
POSIT is a new numerical representation, introduced by professor John Gustafson in 2017 as a candidate to replace the traditional IEEE-754 representation. In this talk I will present my experience in designing, building and accelerating the POSIT numerical representation on Field-Programmable Gate Arrays (FPGAs). I will start by briefly introducing the POSIT representation, show its hardware implementation details, reasoning around their trade-offs (with respect to IEEE-754) and conclude the presentation with small use-cases and their measured/obtained performance.
日時: 2019年9月2日（月）、13:00 - 13:55
場所: R-CCS 6階講堂
・講演題目： Current status of FDPS/Processor design from HPC perspective
・講演者： Jun Makino（粒子系シミュレータ研究チーム、チームリーダー）
This talk will consist of two parts. In part I, I'll overview the current status of FDPS (Framework for Developing Particle Simulator). FDPS provide an easy and highly efficient way to develop parallel program for particle-based simulations, through extensive use of metaprogramming. It takes the definitions of particle class and particle-particle interaction function as input, and generates high-performance parallel libraries for domain decomposition, particle exchange and interaction calculation. Using these functions, application programmers can develop their own parallel programs, without spending much time to write and debug parallel code written in MPI. In fact, an application program written using FDPS functions contain no MPI calls and yet run on single-core, multiple cores using OpenMP or multiple nodes using MPI (or hybrid OpenMP-MPI) without the need of the change in the source code. After the initial release of FDPS in 2015, we have added many additional functionalities and performance improvement, which I'll overview in this talk. In the second part, I'll discuss how one can design the processors architecture which is "optimal" in some well-defined meaning.
日時: 2019年9月2日（月）、13:55 - 14:50
場所: R-CCS 6階講堂
・講演題目： Recent achievements and future plans in the computational molecular science research team
・講演者： Takahito Nakajima（量子系分子科学研究チーム、チームリーダー）
We will give a talk on the recent achievements and future plans in the computational molecular science research team. In particular, we will introduce materials design of hole-transporting materials (HTMs) for perovskite solar cells. In this study, the efficient search of optimum HTMs was achieved by applying machine learning techniques. We employed the deep neural network to predict the power conversion efficiency of perovskite solar cells with HTMs by utilizing molecular descriptors as input features. We also employed the Gaussian process regression to evaluate the acquisition function in Bayesian optimization and implement uncertainty and reliability to the prediction model. Discrete particle swarm optimization was applied to tackle the optimization problem in the vast chemical space. In addition, we will introduce the future development of the solar-cell simulator based on the dynamic Monte Carlo approach with the first-principles calculation.