Today the DOE Exascale Computing Project announced the following changes to their strategic plan. The ECP project now plans to deploy the first Exascale system in the U.S. in 2021, a full 1-2 years earlier than previously planned. This system will be built from a “novel architecture” that will be put out for bid in the near future. According to Argonne’s Paul Messina, Director, Exascale Computing Project, “It won’t be something out there like quantum computing, but we are looking for new ideas in terms of processing and networking technologies for the machine.”
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 video from SC16, Don Clegg from Supermicro describes the company’s broad range of HPC solutions. “Innovation is at the core of Supermicro product development and benefits the HPC community with first-to-market integration of advanced technology such as our 1U with four and 4U with eight Pascal P100 SXM2 GPUs or 4U with ten PCI-e GPU systems, hot-swap U.2 NVMe, upcoming fabric technologies like Red Rock Canyon and PCI-E switches, as well as new architecture designs like our new high-density BigTwin system design.”
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 the PASC17 Conference announced that Matthias Troyer from Microsoft Research will give this year’s public lecture on the topic “Towards Quantum High Performance Computing.” The event will take place June 26-28 in Lugano, Switzerland.
Prof. Taisuke Boku from the University of Tsukuba & JCAHPC presented this talk at the DDN User Group at SC16. “Thanks to DDN’s IME Burst Buffer, researchers using Oakforest-PACS at the Joint Center for Advanced High Performance Computing (JCAHPC) are able to improve modeling of fundamental physical systems and advance understanding of requirements for Exascale-level systems architectures. With DDN’s advanced technology, JCAHPC has achieved effective I/O performance exceeding 1TB/s in writing tens of thousands of processes to the same file.”
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.”