When the latest version of the Graph 500 list was released Nov. 16 at the SC16 conference, there were two new entries in the top 10, both contributed by Khaled Ibrahim of Berkeley Lab’s Computational Research Division. “Ibrahim explains that such workloads, known as communication-bound applications are typically the most difficult to scale on HPC systems. But finding a way to scale up their performance can have a big payoff by reducing the computational “expense,” or amount of computing time needed to solve a problem.”
“The pharmaceutical industry trend toward joint ventures and collaborations has created a need for new platforms in which to work together. We’ll dive into architectural decisions for building collaborative systems. Examples include how such a platform allowed Human Longevity, Inc. to accelerate software deployment to production in a fast-paced research environment, and how Celgene uses AWS for research collaboration with outside universities and foundations.”
The Supercomputing Frontiers 2017 conference in Singapore has issued its Call for Papers. As Singapore’s annual international HPC conference, Supercomputing Frontiers provides a platform for thought leaders from both academia and industry to interact and discuss visionary ideas, important global trends and substantial innovations in supercomputing. The event takes place March 13-16, 2017.
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.”
Libraries that are tuned to the underlying hardware architecture can increase performance tremendously. Higher level libraries such at the Intel Data Analytics Acceleration Library (Intel DAAL) can assist the developer with highly tuned algorithms for data analysis as well as machine learning. Intel DAAL functions can be called within other, more comprehensive frameworks that deal with the various types of data and storage, increasing the performance and lowering the development time of a wide range of applications.
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.”
In this video from SC16, Roy Kim from Nvidia describes how the company is bringing in a new age of AI with accelerated computing for Deep Learning applications. “Deep learning is the fastest-growing field in artificial intelligence, helping computers make sense of infinite amounts of data in the form of images, sound, and text. Using multiple levels of neural networks, computers now have the capacity to see, learn, and react to complex situations as well or better than humans. This is leading to a profoundly different way of thinking about your data, your technology, and the products and services you deliver.”
“With up to eight high performance NVIDIA GPUs designed for maximum transfer bandwidth, the HPE Apollo 6500 is purpose-built for HPC and deep learning applications. Its high ratio of GPUs to CPUs, dense 4U form factor and efficient design enable organizations to run deep learning recommendation algorithms faster and more efficiently, significantly reducing model training time and accelerating the delivery of real-time results, all while controlling costs.”
“With demand for graduates with AI skills booming, we’ve released the NVIDIA Deep Learning Teaching Kit to help educators give their students hands on experience with GPU-accelerated computing. The kit — co-developed with deep-learning pioneer Yann LeCun, and largely based on his deep learning course at New York University — was announced Monday at the NIPS machine learning conference in Barcelona. Thanks to the rapid development of NVIDIA GPUs, training deep neural networks is more efficient than ever in terms of both time and resource cost. The result is an AI boom that has given machines the ability to perceive — and understand — the world around us in ways that mimic, and even surpass, our own.”