6 novembre 2018 au CNRS, rue Michel-Ange, Paris.
9:15 – 9:30 Introduction de la journée (Antoine Petit, Président-Directeur Général du CNRS)
Session IA pour le HPC
9:30 – 10:35 Introduction à l’IA (Nicolas Valyatis, CMLA)
10:35 – 11:05 Realtime capable first-principle-based fusion reactor turbulence modeling using neural networks (Karel van de Plassche, DIFFER)
Predicting particle and energy transport in fusion reactors is essential for the interpretation of current-day fusion experiments, and in extrapolating to future reactors. In fusion-relevant plasmas turbulence is the main transport channel, and calculating this turbulent transport is computationally expensive. Fortunately, reduced turbulence models have been successful in reproducing experimental profiles in many cases, offering a 6 orders of magnitude speedup compared to their nonlinear counterparts. However, these models are still not fast enough to be real-time capable. We sketch a pathway towards circumventing the conflicting constraints of accuracy and tractability in turbulence modelling, towards real-time capability. We use the QuaLiKiz reduced model [Bourdelle PPCF 2016] to generate a large database of turbulent fluxes. Neural networks are then trained on this dataset, offering a surrogate model that when coupled to the control-oriented fast tokamak simulator RAPTOR is able to simulate 1 second of plasma evolution in 10 CPU seconds, 4 orders of magnitude faster than the original QuaLiKiz model.
Karel van de Plassche is a fusion researcher and software engineer focusing on applying machine learning for creating surrogate models within fusion modeling frameworks, employing HPC for turbulence model dataset generation and neural network training. He gained his MSc in 2018 in Science and Technology of Nuclear Fusion at the Eindhoven University of Technology, concurrent with working on Software Defined Networking at the startup PhotonX within the COSIGN EU-FP7 project. He is currently employed at the Dutch Institute for Fundamental Energy Research (DIFFER), in the Integrated Modelling and Transport Group.
11:05 – 11:35 Pause
11:35 – 12:05 IA pour les géo-sciences
12:05 – 12:35 Point Genci
12:35 – 14:00 Repas
Session HPC pour l’IA
14:00 – 14:45 Machine learning and the post-Dennard era of climate simulation (V. Balaji, Princeton University, Visiting Scientist, LSCE)
Conventional computational hardware has reached some physical limits: the phenomenon known as ‘Dennard scaling’ gave rise to Moore’s Law, and many cycles of exponential growth in computing capacity. The consequence is that we now anticipate a computing future of increased concurrency and slower arithmetic. Earth system models, which are weak-scaling and memory-bandwidth-bound, face a particular challenge given their complexity in physical-chemical-biological space, to which mapping single algorithms or approaches is not possible. A particular aspect of such ‘multi-scale multi-physics’ models that is under-appreciated is that they are built using a combination of local process-level and global system-level observational constraints, for which the calibration process itself remains a substantial computational challenge. In this talk, we examine approaches to Earth system modeling in the post-Dennard era. The possibilities include following the industry trend toward machine learning and build models that learn; stochastic methods and emulators for fast exploration of uncertainty; using fewer bits of precision, among others. The talk will present ideas and challenges and the future of Earth system models as we prepare for a post-Dennard future.
Dr. V. Balaji (https://www.gfdl.noaa.gov/v-balaji-homepage/) has headed the Modeling Systems Division at NOAA/GFDL since 2004, with appointments in Princeton University’s Cooperative Institute for Modeling the Earth System (CIMES), and associate faculty at the Princeton Institute for Computational Science and Engineering (PICSciE) and the Princeton Environmental Institute (PEI). With a background in physics and climate science, he has also become an expert in the area of parallel computing and scientific infrastructure. He serves on the Scientific Advisory Board of the Max-Planck Institute for Meteorology in Hamburg, and the National Center for Atmospheric Research. He is a sought-after speaker and lecturer and is committed to provide training in the use of climate models in developing nations, leading workshops for advanced students and researchers in South Africa and India.
In 2017, he was among the first recipients of French President Macron’s ‘Make Our Planet Great Again’ award marking the second anniversary of the Paris Climate Accord.
14:45 – 15:45 Post-K: A Game Changing Supercomputer for Convergence of HPC and Big Data / AI (Satoshi Matsuoka, Director Riken-CCS / Professor, Tokyo Institute of Technology)
With rapid rise and increase of Big Data and AI as a new breed of high-performance workloads on supercomputers, we need to accommodate them at scale, and thus the need for R&D for HW and SW Infrastructures where traditional simulation-based HPC and BD/AI would converge, in a BYTES-oriented fashion. The TSUBAME3 supercomputer at Tokyo Institute of Technology which has become online in Aug. 2017, embodies various BYTES-oriented features to allow for such convergence to happen at scale, including significant scalable horizontal bandwidth as well as support for deep memory hierarchy and capacity, along with high flops in low precision arithmetic for deep learning.. TSUBAM3’s technologies ave been commoditized to construct one of the world’s largest BD/AI focused open and public computing infrastructure called ABCI (AI-Based Bridging Infrastructure), hosted by AIST-AIRC (AI Research Center), the largest public funded AI research center in Japan. Although not a supercomputer for HPC, its Linpack ranking is No.1 in Japan and No.5 in the world, as well as embodying 550 AI-Petaflops for AI, as well as being extremely energy efficient with novel warm water cooling pod design. Finally, Post-K is the flagship next generation national supercomputer being developed by Riken and Fujitsu in collaboration. Post-K will have hyperscale class resource in one exascale machine, with well more than 100,000 nodes and number of sever-class Arm CPU cores approaching 10 million. Post-K is slated to perform 100 times faster on some key applications c.f. its predecessor, the K-Computer, but also will likely to be the premier big data and AI/ML infrastructure. Currently, we are conducting research to scale deep learning to more than 10,000 nodes on Post-K, where we would obtain near top GPU-class performance on each node.
15:45 – 16:15 Pause
16:15 -16:45 Big Data Challenge in Human Brain Research (Katrin Amunts, Institut of Neuroscience and Medicine)
The human brain has a multi-level organisation and high complexity. New approaches are necessary to decode the brain with its 86 billion nerve cells, which form complex networks. To elucidate brain architecture at the level of nerve cells and their axons while preserving the topography of the whole organ makes it necessary to analyse data sets of several petabytes per brain, which should be actively accessible while minimizing their transport. Thus, ultra-high resolution models pose massive challenges in terms of data processing, visualisation and analysis. The Human Brain Project addresses such data challenges. It creates a cutting-edge European infrastructure to enable cloud-based collaboration among researchers coming from different disciplines around the world, and develops platforms with databases, workflow systems, petabyte storage, and supercomputers opening new perspective to decode the human brain.
16:45 – 17:15 Modèles IA pour l’agro/botanique (Alexis Joly, Plant@NET)
17:15 Clôture et Save-the-date
Forum sponsorisé par la société Intel