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Machine & Deep Learning: Practical Deployments and Best Practices for the Next Two Years

Arno Kolster from Providentia Worldwide gave this talk at the HPC User Forum in Milwaukee. “Providentia Worldwide is a new venture in technology and solutions consulting which bridges the gap between High Performance Computing and Enterprise Hyperscale computing. We take the best practices from the most demanding compute environments in the world and apply those techniques and design patterns to your business.”

AI Breakthroughs and Initiatives at the Pittsburgh Supercomputing Center

Nick Nystrom and Paola Buitrago from PSC gave this talk at the HPC User Forum in Milwaukee. “The Bridges supercomputer at PSC offers the possibility for experts in fields that never before used supercomputers to tackle problems in Big Data and answer questions based on information that no human would live long enough to study by reading it directly.”

Intel Parallel Studio XE 2018 Released

Intel has announced the release of Intel® Parallel Studio XE 2018, with updated compilers and developer tools. It is now available for downloading on a 30-day trial basis. ” This week’s formal release of the fully supported product is notable with new features that further enhance the toolset for accelerating HPC applications.”

CCIX Project to link ARM Processors and FPGAs for HPC

Today ARM, Xilinx, Cadence, and Taiwan Semiconductor announced plans to produce the first test chip for the Cache Coherent Interconnect for Accelerators (CCIX) project. CCIX (pronounced “C6”) aims to prove that many-core ARM processors linked to FPGAs have a home in HPC. “The test chip will not only demonstrate how the latest Arm technology with coherent multichip accelerators can scale across the data center, but reinforces our commitment to solving the challenge of accessing data quickly and easily.”

Video: Characterization and Benchmarking of Deep Learning

 Natalia Vassilieva from HP Labs gave this talk at the HPC User Forum in Milwaukee. “Our Deep Learning Cookbook is based on a massive collection of performance results for various deep learning workloads on different hardware/software stacks, and analytical performance models. This combination enables us to estimate the performance of a given workload and to recommend an optimal hardware/software stack for that workload. Additionally, we use the Cookbook to detect bottlenecks in existing hardware and to guide the design of future systems for artificial intelligence and deep learning.”

Solving AI Hardware Challenges

For many deep learning startups out there, buying AI hardware and a large quantity of powerful GPUs is not feasible. So many of these startup companies are turning to cloud GPU computing to crunch their data and run their algorithms. Katie Rivera, of One Stop Systems, explores some of the AI hardware challenges that can arise, as well as the new tools designed to tackle these issues. 

MIT Paper Sheds Light on How Neural Networks Think

MIT researchers have developed a new general-purpose technique sheds light on inner workings of neural nets trained to process language. “During training, a neural net continually readjusts thousands of internal parameters until it can reliably perform some task, such as identifying objects in digital images or translating text from one language to another. But on their own, the final values of those parameters say very little about how the neural net does what it does.”

New OrionX Survey: Insights in Artificial Intelligence

In this Radio Free HPC podcast, Dan Olds and Shahin Khan from OrionX describe their new AI Survey. “OrionX Research has completed one the most comprehensive surveys to date of Artificial Intelligence, Machine Learning, and Deep Learning. With over 300 respondents in North America, representing 13 industries, our model indicates a confidence level of 95% and a margin of error of 6%. Covering 144 questions/data points, it provides a comprehensive view of what customers are doing and planning to do with AI/ML/DL.”

Video: The State of Bioinformatics in HPC

“In the last few years DNA sequencing technologies have become extremely cheap enabling us to quickly generate terabytes of data for a few thousand dollars. Analysis of this data has become the new bottleneck. Novel compute-intensive streaming approaches that leverage this data without the time-costly step of genome assembly and how UWA’s Edwards group leveraged these approaches to find new breeding targets in crop species are presented.”

Trends in the Worldwide HPC Market

In this video from the HPC User Forum in Milwaukee, Earl Joseph and Steve Conway from Hyperion Research present and update on HPC, AI, and Storage markets. “Hyperion Research forecasts that the worldwide HPC server-based AI market will expand at a 29.5% CAGR to reach more than $1.26 billion in 2021, up more than three-fold from $346 million in 2016.”