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The Industrialization of Deep Learning – Intro

Deep learning is a method of creating artificial intelligence systems that combine computer-based multi-layer neural networks with intensive training techniques and large data sets to enable analysis and predictive decision making. A fundamental aspect of deep learning environments is that they transcend finite programmable constraints to the realm of extensible and trainable systems. Recent developments in technology and algorithms have enabled deep learning systems to not only equal but to exceed human capabilities in the pace of processing vast amounts of information.

Video: HPE Apollo 6500 Takes GPU Density to the Next Level

In this video from ISC 2016, Greg Schmidt from Hewlett Packard Enterprise describes the new Apollo 6500 server. 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.

CocoLink Using Consumer GPUs for Deep Learning

CocoLink, a subsidiary of Seoul National University, in collaboration with Orange Silicon Valley, has upgraded its KLIMAX 210 server with 20 of the latest GeForce 1080 GPUs – with the eventual goal of scaling the single 4U rack to more than 200 teraflops.

Video: Nvidia’s Peter Messmer on Co-Design at PASC16

In this video from PASC16, Peter Messmer from Nvidia gives his perspectives on the conference and his work on co-design for high performance computing. “Using a combination of specialized rather than one type fits all processing elements offers the advantage of providing the most economical hardware for each task in a complex application. In order to produce optimal codes for such heterogeneous systems, application developers will need to design algorithms with the architectural options in mind.”

Nvidia Showcases DGX-1 Deep Learning Supercomputer at ISC 2016

In this video from ISC 2016, Marc Hamilton from Nvidia describes the new DGX-1 Deep Learning Supercomputer. “The NVIDIA DGX-1 is the world’s first purpose-built system for deep learning with fully integrated hardware and software that can be deployed quickly and easily. Its revolutionary performance significantly accelerates training time, making the NVIDIA DGX-1 the world’s first deep learning supercomputer in a box.”

Supermicro Showcases Intel Xeon Phi and Nvidia P100 Solutions at ISC 2016

At ISC 2016, Supermicro debuted the latest innovations in HPC architectures and technologies including a 2U 4-Node server supporting new Intel Xeon Phi processors (formerly code named Knights Landing) with integrated or external Intel Omni-Path fabric option, together with associated 4U/Tower development workstation; 1U SuperServer supporting up to 4 GPU including the next generation P100 GPU; Lustre High Performance File system; and 1U 48-port top-of-rack network switch with 100Gbps Intel Omni-Path Architecture (OPA) providing a unique HPC cluster solution offering excellent bandwidth, latency and message rate that is highly scalable and easily serviceable.

Video: Asetek Showcases Liquid Cooling at ISC 2016

In this video from ISC 2016, Steve Branton from Asetek describes the company’s innovative liquid cooling solutions for HPC. “Because liquid is 4,000 times better at storing and transferring heat than air, Asetek’s solutions provide immediate and measurable benefits to large and small data centers alike. RackCDU D2C is a “free cooling” solution that captures between 60% and 80% of server heat, reducing data center cooling cost by over 50% and allowing 2.5x-5x increases in data center server density. D2C removes heat from CPUs, GPUs, memory modules within servers using water as hot as 40°C (105°F), eliminating the need for chilling to cool these components.”

Exxact to Build HPC Solutions Using NVIDIA Tesla P100 GPUs

Today Exxact Corporation announced its planned production of HPC solutions using the NVIDIA Tesla P100 GPU accelerator for PCIe. Exxact will be integrating the Tesla P100 into their Quantum family of servers, which are currently offered with either NVIDIA Tesla M40 or K80 GPUs. The NVIDIA Tesla P100 for PCIe-based servers was introduced at the recent 2016 International Supercomputing Conference and is anticipated to deliver massive leaps in performance and value compared with CPU-based systems. NVIDIA stated the new Tesla P100 will help meet unprecedented computational demands planted on modern data centers.

SGI to Power 1.9 Petaflop Supercomputer at University of Tokyo

The University of Tokyo has chosen SGI to perform advanced data analysis and simulation within its Information Technology Center. The center is one of Japan’s major research and educational institutions for building, applying, and utilizing large computer systems. The new SGI system will begin operation July 1, 2016. “The SGI integrated supercomputer system for data analysis and simulation will support the needs of scientists in new fields such as genome analysis and deep learning in addition to scientists in traditional areas of computational science,” said Professor Hiroshi Nakamura, director of Information Technology Center, the University of Tokyo. “The new system will further ongoing research and contribute to the development of new academic fields that combine data analysis and computational science.”

Slidecast: Announcing the Nvidia Tesla P100 for PCIe Servers

In this slidecast, Marc Hamilton from describes the Nvidia Tesla P100 for PCIe Servers. “The Tesla P100 for PCIe is available in a standard PCIe form factor and is compatible with today’s GPU-accelerated servers. It is optimized to power the most computationally intensive AI and HPC data center applications. A single Tesla P100-powered server delivers higher performance than 50 CPU-only server nodes when running the AMBER molecular dynamics code, and is faster than 32 CPU-only nodes when running the VASP material science applications.”