“The multidisciplinary research team and computational facilities –including MareNostrum– make BSC an international centre of excellence in e-Science. Since its establishment in 2005, BSC has developed an active role in fostering HPC in Spain and Europe as an essential tool for international competitiveness in science and engineering. The center manages the Red Española de Supercomputación (RES), and is a hosting member of the Partnership for Advanced Computing in Europe (PRACE) initiative.”
Today ORNL announced the full schedule of 2017 GPU Hackathons at multiple locations around the world. “The goal of each hackathon is for current or prospective user groups of large hybrid CPU-GPU systems to send teams of at least 3 developers along with either (1) a (potentially) scalable application that could benefit from GPU accelerators, or (2) an application running on accelerators that need optimization. There will be intensive mentoring during this 5-day hands-on workshop, with the goal that the teams leave with applications running on GPUs, or at least with a clear roadmap of how to get there.”
Applications such as machine learning and deep learning require incredible compute power, and these are becoming more crucial to daily life every day. These applications help provide artificial intelligence for self-driving cars, climate prediction, drugs that treat today’s worst diseases, plus other solutions to more of our world’s most important challenges. There is a multitude of ways to increase compute power but one of the easiest is to use the most powerful GPUs.
The Seventh International Workshop on Accelerators and Hybrid Exascale Systems (AsHES) has issued its Call for Papers. The event takes place May 29 in Orlando, Florida in conjunction with the IEEE International Parallel and Distributed Processing Symposium.
Congratulations go out to Sunita Chandrasekaran, assistant professor of computer science at the University of Delaware, who has won the 2016 IEEE-CS TCHPC Award for Excellence for Early Career Researchers in High Performance Computing. “Chandrasekaran’s research interests include programming accelerators (GPUs), exploring the suitability of high-level parallel programming models such as OpenMP and OpenACC for current and future platforms, and validating and verifying emerging directive-based parallel programming models.”
“New Radeon Instinct accelerators will offer organizations powerful GPU-based solutions for deep learning inference and training. Along with the new hardware offerings, AMD announced MIOpen, a free, open-source library for GPU accelerators intended to enable high-performance machine intelligence implementations, and new, optimized deep learning frameworks on AMD’s ROCm software to build the foundation of the next evolution of machine intelligence workloads.”
“The competition is an opportunity to showcase the world’s brightest computer science students’ expertise in a friendly, yet spirited competition,” said Martin Meuer, managing director of the ISC Group. “We are very pleased to host these 12 compelling university teams from around the world. We look forward to this very engaging competition and wish the teams good luck.”
In this video from SC16, Abdul Hamid Al Halabi from Nvidia describes how the company is accelerating Deep Learning for Healthcare. “From Electronic Health Records (EHR) to wearables, every year the flood of heterogeneous healthcare data increases exponentially. Deep learning has the power to unlock the potential within this data.Harnessing the power of GPUs, healthcare and medical researchers are able to design and train more sophisticated neural networks—networks that can accelerate high-throughput screening for drug discovery, guide pre-operative strategies, or work in conjunction with traditional techniques and apparatus to detect invasive cancer cells in real-time during surgery.”
In this Nvidia podcast, Bryan Catanzaro from Baidu describes how machines with Deep Learning capabilities are now better at recognizing objects in images than humans. “AI gets better and better until it kind of disappears into the background,” says Catanzaro — NVIDIA’s head of applied deep learning research — in conversation with host Michael Copeland on this week’s edition of the new AI Podcast. “Once you stop noticing that it’s there because it works so well — that’s when it’s really landed.”
Today Cray announced the results of a deep learning collaboration with Microsoft CSCS designed to expand the horizons of running deep learning algorithms at scale using the power of Cray supercomputers. “Cray’s proficiency in performance analysis and profiling, combined with the unique architecture of the XC systems, allowed us to bring deep learning problems to our Piz Daint system and scale them in a way that nobody else has,” said Prof. Dr. Thomas C. Schulthess, director of the Swiss National Supercomputing Centre (CSCS). “What is most exciting is that our researchers and scientists will now be able to use our existing Cray XC supercomputer to take on a new class of deep learning problems that were previously infeasible.”