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TERATEC 2017 Forum
Workshop 4 - Wednesday, June 28 from 9:00 to 12:30
Evolution of HPC software environment

Towards accelerated Deep Learning in HPC and Hyperscale architectures

Abstract : Deep Neural Nets and Big Data are introducing a new programming paradigm, deep learning frameworks have achieved over-whelming superiority in many fields of pattern recognition in recent years. Furthermore speeding and scaling up deep learning algorithms with GPUs has been shown to lead to increased performance in benchmark tasks and to enable discovery of complex high-level features. Recent efforts to train extremely large networks (with over 1 billion parameters) have relied on cloud- like computing infrastructure, announcing the convergence of accelerated HPC and Hyperscale architectures. In this context we will present the latest NVIDIA hardware & software developments aiming to ease and accelerate the design and deployment of Deep Learning applications.

Bio : François Courteille is a principal solution architect with NVIDIA, helping customers and partners develop accelerated High Performance Computing and Machine Learning solutions (hardware & software architecture). He is particularly focused on HPC applications of energy industry, mostly Oil & Gas. Prior to joining NVIDIA, François spent three decades as technical leader for HPC companies, Control Data Corporation, Evans & Sutherland, Convex, NEC Corporation, where he ported and tuned HPC application software on large scale parallel and vector systems. He has a MS degree in Computer Science from Institut National des Sciences Appliquées (INSA) de Lyon, France.

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