Effective use of data management techniques for analysis and visualization of massive scientific data is a crucial ingredient for the success of any supercomputing center and cyberinfrastructure for data-intensive scientific investigation. In the progress towards exascale computing, the data movement challenges have fostered innovation leading to complex streaming workflows that take advantage of any data processing opportunity arising while the data is in motion. This technology finds practical use in a number of industry applications including precision agriculture and tele-medicine. In this talk I will present a number of techniques developed at the Center for Extreme Data Management Analysis and Visualization (CEDMAV) that allow to build a scalable data movement infrastructure for fast I/O while organizing the data in a way that makes it immediately accessible for analytics and visualization. In addition, I will present a topological analytics framework that allows processing data in-situ and achieve massive data reductions while maintaining the ability to explore the full parameter space for feature selection. Overall, this leads to a flexible data streaming workflow that allows working with massive simulation models without compromising the interactive nature of the exploratory process that is characteristic of the most effective data analytics and visualization environment.
※ストリーミング型大規模データ可視化ツールOpenViSUSのチュートリアルを3月20日に行います。
詳細はOpenViSUS ストリーミング型大規模データ可視化チュートリアル（2019年3月20日）をご覧下さい。

科学的研究とそうでないものを分かつものはなんだろうか。この問いへの一つの答えは、経験科学は実験や観察に依拠して理論構築するというものである。では、その見方によれば計算機シミュレーションを用いた「実験」はどう扱われるだろうか。計算機シミュレーションによる研究は科学と言えないのだろうか。計算機シミュレーションは近年は科学哲学においても注目されており、この問いに関わるような考察も行われている。今回の講演ではそうした科学哲学の近年の動きを踏まえながら、計算機シミュレーションの科学における位置づけについて考えていきたい。

Understanding the chiral symmetry and its spontaneous breakdown is essential to theoretically reveal the nature of QCD (Quantum Chromo Dynamics) in the Standard Model of particle physics. To this end non-perturbative approaches to QCD dynamics are indispensable and numerical computation based on lattice QCD is only one available method to pursue this for targeted precision in the state-of-the-art application. Ever since the first trial in this approach started in 1980, tremendous effort to improve the algorithms has been made. With that and the increased computer capability we (lattice community) now achieved percent level precision computation for not all, but, some important physical quantities. On the other hand, if one's focus is at the boundary of chiral symmetric - broken "phases", then a delicate treatment of the chiral symmetry is required to even predict the qualitative nature of the system. Such a treatment has become possible only recently in large-scale numerical simulations, which have led to some hints of novel features of QCD. Taking the latter examples, this talk describes such features of QCD, which appears in two different contexts: 1) when the numbers of quarks are increased and 2) in the finite temperature phase transition of two-flavor QCD. The implication to the physics beyond the Standard Model 1); and also the impact to the real world 2) are discussed with the results obtained from large scale numerical simulations. Finally problems which needs to be solved in the next generation supercomputers are also discussed.

The High Performance Big Data Research Team is investigating and developing system software to facilitate extreme-scale big data processing, machine learning and deep learning for the K computer, post-K computer and beyond. The computational power in high performance computing (HPC) systems has been dramatically increasing, driven in particular by advanced multi/many-core architectures and new memory technologies such as high bandwidth memory and hybrid memory cubes. Although these HPC systems are keeping pace with required computational and memory performance for running scientific applications, they are inadequate with respect to I/O performance required by data-intensive applications. In this talk, we briefly introduce various approaches to resolve these I/O issues and future research directions for the next generation HPC systems.

The continuous improvement in processing speed in high-performance computer systems has been enabled by transistor scaling known as Moore's law. However, this trend is predicted to end in the near future. It is vital to research and develop new, more efficient high performance architectures to continue realizing high performance computing systems. One of the missions of our team is to research and develop a next-generation high-performance computer architecture together with strategies to improve the power efficiency of exascale supercomputer systems. Our research focus includes non-von Neumann architectures, integrating next generation non-volatile memories and/or various types of accelerators into a general-purpose processor, acceleration of machine learning computations, and hybrid computing architectures that combine new and classical computing models. In this talk, we briefly introduce our recent research efforts on next generation high-performance architectures. We also present a power-aware resource management framework which have been developed by our JST CREST project. The developed framework controls power allocation among co-scheduled jobs to optimize total system throughput and power-efficiency within a given power constraint. We have tested this framework on a large-scale HPC cluster system with about 1000 compute-nodes and showed that it can successfully manage the system's power consumption.

This presentation will briefly introduce the ideas behind quantum mechanics and its possible application in quantum sensing, communication and computing. We’ll then discuss the ideas and principles that have enabled the world’s first quantum computers. We’ll briefly review the technologies and architectures, then dive a little more deeply into how the D-Wave quantum annealing computer works. We’ll survey the “proto-applications” that customers have been developing in areas of optimization, machine learning and material science that point the way to production use of quantum computers in the next few years. Finally, we’ll discuss some of the future directions of quantum computing.

The family of MAX phases — with chemical formula of Mn+1AXn, where n = 1, 2, or 3, “M” is an early transition metal (Sc, Ti, Zr, Hf, V, Nb, Ta, Cr, Mo), "A" is A group elements (Al, Si, P, S, Ga, Ge, As, In, Sn) and "X" is carbon and/or nitrogen — are a large family of layered ceramics with structural applications. Recently, MAX phases have been exfoliated into 2D single and/or multi Mn+1Xn layers by using appropriate acid solutions. The resulting 2D-Mn+1Xn transitional metal carbides and nitrides have been named as MXenes. Considering a large number of compositional possibilities of MAX phase compounds, a large number of MXenes with unprecedented properties could also be obtained in the future. Owing to their large surface area, hydrophilicity, adsorption ability, and high surface reactivity, 2D MXenes have experimentally attracted attention for many potential applications, e.g., catalysts, ion batteries, gas storage media, and sensors. Given the fast progress of MXene-based science and technology, in this presentation, I would like to update your knowledge of electronic properties and some of the possible applications of MXenes.

In this talk, we review our achievement in regional-scale data assimilation with Himawari-8 satellite radiances. In July 2015, the Japan Meteorological Agency (JMA) started full operations of their new geostationary satellite “Himawari-8”, the first of a series of the third-generation geostationary meteorological satellites. Himawari-8 can produce high-resolution observations with 16 frequency bands every 10 minutes for full disk. To assimilate Himawari-8 radiances, we implemented a radiative transfer model into a regional-scale data assimilation system known as SCALE-LETKF, consists of the Scalable Computing for Advanced Library and Environment-Regional Model (SCALE-RM) and the Local Ensemble Transform Kalman Filter (LETKF). We assimilated all-sky every-10-minute infrared (IR) radiances from Himawari-8. The results showed that assimilating the every-10-minute Himawari-8 IR radiances improves the analyzed tropical cyclone (TC) structure and intensity forecasts. In another case in September 2015, the heavy precipitation forecasts are greatly improved by assimilating the Himawari-8 IR observations. We ran a rainfall-runoff model using the improved precipitation forecasts and found that assimilating the Himawari-8 observations frequently may give longer lead times in terms of the flood risk. We also show other case studies on a different TC case and the extremely-heavy precipitation event in July 2018.