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HPC Top 5 Stories: October 13, 2017

Check out weekly insights into the world of HPC and AI with this HPC Top 5 Stories.

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HPC Top 5 Stories: October 13, 2017

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  1. HPC TOP 5 STORIES Weekly Insights into the World of High Performance Computing

  2. HPC AND AI HAVE PAVED THE WAY FOR GROUNDBREAKING DISCOVERIES IN SCIENCE, MEDICINE, AND OTHER FIELDS…

  3. PROVING THAT AI IS THE FUTURE OF SUPERCOMPUTING…

  4. TOP 5 HERE ARE THE “TOP FIVE’ STORIES HIGHLIGHTING WHAT’S HOT IN HPC AND AI

  5. TOP 5 1. Accelerating Quantum Chemistry for Drug Discovery 2. SAP Leonardo Machine Learning Portfolio is First Enterprise Offering to use NVIDIA’s Volta AI Platform 3. NVIDIA Tesla V100 GPUs Power TYAN Server 4. Baidu Sheds Precision Without Paying Deep Learning Accuracy Cost 5. Achieving Faster AI with NVIDIA GPUs and TensorRT Webinar

  6. 1 ACCELERATING QUANTUM CHEMISTRY FOR DRUG DISCOVERY In the pharmaceutical industry, drug discovery is a long and expensive process. It takes an average of 12 years and $2.6 billion to bring a new drug to market. One key to speeding the drug discovery process is the ability to more accurately simulate molecular dynamics (MD), to quickly screen millions of potential drug combinations so researchers can focus their energy on the most promising options. All drug discoveries require molecular simulations to understand their potential efficacy. Molecular energetics, where millions of molecules are scanned to determine how they interact with each other, helps in this understanding. However, to have accurate MD simulations, you need accurate quantum mechanical (QM) simulations as well. QM simulations are essential to the process of accurately screening millions of potential drugs. ARTICLE

  7. SAP LEONARDO MACHINE LEARNING PORTFOLIO IS FIRST ENTERPRISE OFFERING TO USE NVIDIA’S VOLTA AI PLATFORM 2 Earlier this year, SAP and NVIDIA expanded their collaboration to create business applications based on artificial intelligence. Now, as NVIDIA’s GPU Technology Conference kicks off in Munich, Germany, the partnership has gained even further substance. SAP installed its first NVIDIA DGX-1 systems – the world’s first AI supercomputer – in Israel and Potsdam in 2016. This was followed by the implementation of NVIDIA DGX-1 systems with NVIDIA Tesla P100 graphics processing units (GPUs) in SAP’s production data center in St. Leon-Rot, Germany and in SAP’s Innovation Labs in Palo Alto, California, and Singapore in September 2017. ARTICLE

  8. 3 NVIDIA TESLA V100 GPUS POWER NEW TYAN SERVER Today TYAN showcased their latest GPU-optimized platforms that target the high performance computing and artificial intelligence sectors at the GPU Technology Conference in Munich. “TYAN’s new GPU computing platforms are designed to provide efficient parallel computing for the analytics of vast amounts of data. By incorporating NVIDIA’s latest Tesla V100 GPU accelerators, TYAN provides our customers with the power to accelerate both high performance and cognitive computing workloads” said Danny Hsu, Vice President of MiTAC Computing Technology Corporation’s TYAN Business Unit. ARTICLE

  9. 4 BAIDU SHEDS PRECISION WITHOUT PAYING DEEP LEARNING ACCURACY COST Today, Baidu Research described another important deep learning milestone in conjunction with GPU maker, Nvidia. Teams demonstrated success training networks with half precision floating point without changing network hyperparameters and keeping with the same accuracy derived from single precision. Previous efforts with mixed lower precision (binary or even 4-bit) have come with losses in accuracy or major network modifications, but with some techniques applied to existing mixed precision approaches, this is no longer the case. This, of course, means better use of memory and higher performance—something that can be put to the test on the newest Nvidia Volta GPUs. ARTICLE

  10. 5 ACHIEVING FASTER AI WITH NVIDIA GPUS AND TENSORRT In this last year, GPU deep learning has gone from a hot research topic to a large-scale deployment challenge in major data centers. That’s because deep learning is extraordinarily effective and now powers every application, from speech recognition to self-driving cars, from language translation to better search. Power efficiency and processing power make GPUs the right fit for deep learning and inference from the edge to the data center. Learn About: 1. How neural nets, frameworks, and GPU architectures have changed significantly in the last year 2. How to save on cost, while achieving better AI performance, efficiency, and responsiveness 3. How to unleash the full potential of NVIDIA GPUs with NVIDIA TensorRT REGISTER

  11. HOW CAN HPC IMPACT YOUR BUSINESS? LEARN MORE

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