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Tutorial: GPU Performance Nuggets

In this video from the 2016 Blue Waters Symposium, GPU Performance Nuggets – Carl Pearson and Simon Garcia De Gonzalo from the University of Illinois present: GPU Performance Nuggets. “In this talk, we introduce a pair of Nvidia performance tools available on Blue Waters. We discuss what the GPU memory hierarchy provides for your application. We then present a case study that explores if memory hierarchy optimization can go too far.”

Video: Combining Simulation & Experiment for Nanoscale 3-D Printing

In this video, ORNL researchers use supercomputers to simulate nanomanufacturing, the process of building microscopic devices atom by atom. Simulated here is the construction of a 250-nanometer 3-D cube by focused electron beam induced deposition.

SC16 Welcomes Families with On-Site Childcare

For the first time, SC16 will offer childcare in the convention center to registered attendees and exhibitors. “This will provide an opportunity for the family to be together while one or both parents enjoy either parts or all of the conference. Of course, it is entirely optional, but we listened to our audience and this seemed to be a growing need. I realize this is a small step, but hopefully it is the first of many more small steps to come.”

OpenPOWER Summit Europe Comes to Barcelona Oct. 26-28

Today the OpenPOWER Foundation announced that their inaugural OpenPOWER Summit Europe will take place Oct. 26-28 in Barcelona, Spain. Held in conjunction with OpenStack Europe, the OpenPOWER Summit Europe, the event will feature speakers and demonstrations from the OpenPOWER ecosystem, including industry leaders and academia sharing their technical solutions and state of the art advancements.

Video: What is Driving Heterogeneity in HPC?

Wen-mei Hwu from the University of Illinois at Urbana-Champaign presented this talk at the Blue Waters Symposium. “In the 21st Century, we are able to understand, design, and create what we can compute. Computational models are allowing us to see even farther, going back and forth in time, learn better, test hypothesis that cannot be verified any other way, and create safe artificial processes.”

Radio Free HPC Looks at IDF 2016

In this podcast, the Radio Free HPC team reviews the recent 2016 Intel Developer Forum. “How will Intel return to growth in the face of a declining PC market? At IDF, they put the spotlight on IoT and Machine Learning. With new threats rising from the likes of AMD and Nvidia, will Chipzilla make the right moves? Tune in to find out.”

SC16 Facing Housing Crunch in Salt Lake City

Over at the SC16 Blog, Elizabeth Leake writes that there will be a bit of a housing crunch in Salt Lake City this year during the world’s largest supercomputing conference.

Nvidia Donates DGX-1 Machine Learning Supercomputer to OpenAI Non-profit

This week Nvidia CEO Jen-Hsun Huang hand-delivered one of the company’s new DGX-1 Machine Learning supercomputers to the OpenAI non-profit in San Francisco. “The DGX-1 is a huge advance,” OpenAI Research Scientist Ilya Sutskever said. “It will allow us to explore problems that were completely unexplored before, and it will allow us to achieve levels of performance that weren’t achievable.”

Taming Heterogeneity in HPC – The DEEP-ER take

Norbert Eicker from the Jülich Supercomputing Centre presented this talk at the SAI Computing Conference in London. “The ultimate goal is to reduce the burden on the application developers. To this end DEEP/-ER provides a well-accustomed programming environment that saves application developers from some of the tedious and often costly code modernization work. Confining this work to code-annotation as proposed by DEEP/-ER is a major advancement.”

Video: Intel Sneak Peek at Knights Mill Processor for Machine Learning

In this video from the 2016 Intel Developer Forum, Diane Bryant describes the company’s efforts to advance Machine Learning and Artificial Intelligence. Along the way, she offers a sneak peak at the Knights Mill processor, the next generation of Intel Xeon Phi slated for release sometime in 2017. “Now you can scale your machine learning and deep learning applications quickly – and gain insights more efficiently – with your existing hardware infrastructure. Popular open frameworks newly optimized for Intel, together with our advanced math libraries, make Intel Architecture-based platforms a smart choice for these projects.”