Presentation

DescriptionWe propose an innovative method for sustainable promotion of scientific discovery using supercomputers in the Exascale Era by combining (Simulation + Data + Learning (S+D+L)). The BDEC system (Big Data & Extreme Computing), which is scheduled to be introduced to the Tokyo University in 2021, is a Hierarchical, Hybrid, Heterogeneous (h3) system, which consists of computing nodes for computational science and those for data science/machine learning. In this study, we consider the BDEC as the platform for integration of (S+D+L), develop an innovative open-source software platform “h3-Open-BDEC” for integration of (S+D+L), and evaluate the effects of the integration on the BDEC. The h3-Open-BDEC is designed for extracting the maximum performance of the supercomputers with minimum energy consumption focusing on (1) innovative method for numerical analysis based on the new principle of computing by adaptive precision, accuracy verification and automatic tuning, and (2) Hierarchical Data Driven Approach (hDDA) based on machine learning. The hDDA automatically constructs the simplified models for efficient generation of training data using Feature Detection, MOR, UQ, Sparse Modeling and AMR. The h3-Open-BDEC is the first innovative software platform to realize integration of (S+D+L) on supercomputers in the Exascale Era, where computational scientists can achieve such integration without supports by other experts. This integration by h3-Open-BDEC enables significant reduction of computations and power consumptions, compared to those by conventional simulations. This work is supported by Japanese Government from FY.2019 to FY.2023 (JSPS Grant-in-Aid for Scientific Research (S), P.I.: Kengo Nakajima).

Poster PDF

Poster Authors