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HPC + Ai: Machine Learning Models in Scientific Computing

Steve Oberlin is CTO of Tesla at NVIDIA.

In this video from the 2019 Stanford HPC Conference, Steve Oberlin from NVIDIA presents: HPC + Ai: Machine Learning Models in Scientific Computing.

Most AI researchers and industry pioneers agree that the wide availability and low cost of highly-efficient and powerful GPUs and accelerated computing parallel programming tools (originally developed to benefit HPC applications) catalyzed the modern revolution in AI/deep learning. Clearly, AI has benefited greatly from HPC. Now, AI methods and tools are starting to be applied to HPC applications to great effect. This talk will describe an emerging workflow that uses traditional numeric simulation codes to generate synthetic data sets to train machine learning algorithms, then employs the resulting AI models to predict the computed results, often with dramatic gains in efficiency, performance, and even accuracy. Some compelling success stories will be shared, and the implications of this new HPC + AI workflow on HPC applications and system architecture in a post-Moore’s Law world considered.”

Steve Oberlin is CTO of NVIDIA. A veteran technologist, entrepreneur and confessed “geek,” Steve Oberlin is chief technology officer (CTO) for NVIDIA’s Tesla business unit responsible for the roadmap, architecture and leading the company’s high-performance computing initiative. Prior to joining NVIDIA in 2013, he served as a distinguished engineer at CA Technologies, co-founder and chief scientist at Cassatt Corporation and vice president of software at Silicon Graphics. Oberlin, began his career at Cray Research working on early vector multiprocessors with Seymour Cray. He held numerous roles at Cray over a two decade span, including vice president of hardware and chief architect responsible for Cray’s first massively parallel processors. He holds more than a dozen U.S. patents.

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