R-CCS Cafe 第218回 第1部

Towards the end of Moore's law, we need to develop new algorithms and applications. We are developing h3-Open-BDEC, which is innovative software for sustainable promotion of scientific discovery by supercomputers in the Exascale Era by combining (Simulation + Data + Learning (S+D+L)), where ideas of data science and machine learning are introduced to computational science. The h3-Open-BDEC is designed for extracting the maximum performance of the supercomputers in the Exascale Era, which is based on hierarchical, hybrid and heterogeneous (h3) architecture for Big Data & Extreme Computing (BDEC), with minimum energy consumption focusing on (1) innovative method for numerical analysis based on the new principle of computing by adaptive precision, and (2) Hierarchical Data Driven Approach (hDDA) based on machine learning. Integration of (S+D+L) by h3-Open-BDEC enables significant reduction of computations and power consumption, compared to those by conventional simulations. We have applied the prototype of h3-Open-BDEC to Seism3D/OpenSWPC-DAF (Data-Assimilation-Based Forecast), which was developed by ERI/U.Tokyo for integration of simulation and data assimilation. In this talk, we will describe the demonstration of real-time data assimilation of the developed code on the Oakbridge-CX system at the University of Tokyo, using measured data through JDXnet with 2,000+ nationwide observation points.