Tag Archives: NVIDIA Tesla

The NVIDIA Quadro vDWS Virtualisation Software Revealed!

SINGAPORE, 18 August 2017 — NVIDIA today introduced the new Quadro vDWS virtualisation software that turns NVIDIA Tesla-accelerated serversinto powerful workstations and provide IT departments the resources they need to address the demands of an enterprise-wide virtual workspace. Certified with hundreds of professional workstation applications, NVIDIA Quadro has long been the industry standard for professional visualisation in the enterprise.

 

The NVIDIA Quadro Virtual Data Center Workstation Software (Quadro vDWS)

Now, NVIDIA Quadro Virtual Data Center Workstation Software (Quadro vDWS) delivers an unparalleled experience running both virtualised graphics and compute workloads on any virtual workstation or laptop from NVIDIA Tesla-accelerated data centres.

Available for over 120 systems from 33 system vendors, Quadro vDWS provides high-end performance to multiple enterprise users from the same GPU for lower cost of ownership. Quadro vDWS enables greater mobility and collaboration among globally dispersed teams.

It also addresses the increasingly compute-intensive workflows –with their exponential growth in data size and complexity –associated with new technologies for 3D, photo realistic rendering, virtual reality and deep learning. These are particularly common in such fields as engineering and science, where, for example, simulations are conducted during the design process to accurately predict final products.

When powered by NVIDIA Pascal architecture-based Tesla GPU accelerators, Quadro vDWS provides:

  • The ability to create complex 3D and photo real designs – Up to 24GB of GPU memory for working with large, immersive models.
  • Increased productivity – Up to double the graphics performance of the previous NVIDIA GPU architecture lets users make better, faster decisions.
  • Unified graphics and compute workloads – Supports accelerated graphics and compute (CUDA and OpenCL) workflows to streamline design and computer-aided engineering simulation.
  • Better performance for Linux users – NVIDIA NVENC delivers better performance and user density by off-loading H.264 video encoding, a compute-intensive task, from the CPU for Linux virtual workstation users.“The enterprise is transforming.

Workflows are evolving to incorporate AI, photorealism, VR, and greater collaboration among employees. The Quadro visualisation platform is evolving with the enterprise to provide the performance required,” said Bob Pette, Vice President of Professional Visualization at NVIDIA. “With Quadro vDWS on Tesla-powered servers, businesses can tackle larger datasets, power the most demanding applications and meet the need for greater mobility.”

 

NVIDIA GRID vPC Powers the Modern Virtual Workspace

At the same time as manufacturing and design workloads are growing more complex, everyday programs like Windows 10, Office 365 and streaming applications like YouTube now require graphics acceleration to deliver features, functionality and a great virtual PC user experience for the digital workplace. To address the growing demand for graphics-accelerated VDI, NVIDIA announced improvements to its NVIDIA GRID vPC product.

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By leveraging new enhancements in the NVIDIA GRID August 2017 Release and Pascal-based NVIDIA Tesla GPU accelerators, GRID vPC provides:

  • Improved user density and scalability – The Tesla P40 supports up to 24 instances of 1GB virtual desktop profiles, which is a 50 percent increase when compared to the Tesla M60. Taking advantage of this market-leading graphics virtualisation density, IT departments can optimise their infrastructure investment and deliver graphics-accelerated virtualized desktops and applications across the enterprise.
  • Greater vGPU insight – End-to-end management and monitoring tools provide vGPU visibility at the host or guest level. Application-level monitoring capabilities allow IT to intelligently design, manage and support their end users’ experience. New integrations with VMware vRealize Operations (vROps), Citrix Director and XenCenter enable flexibility and control from a single, unified view.
  • Better service – A new GPU resource scheduler helps IT departments enable seamless, consistent allocation of vGPU resources to the user, preventing latency and a degraded user experience caused by a “noisy neighbour.”

Tesla-Powered Virtual GPU Solutions Deliver More Power for More Users NVIDIA Quadro vDWS and NVIDIA GRID vPC solutions are designed for optimal performance with Pascal-based Tesla GPU accelerators. Joining the NVIDIA Tesla P4, P40 and P100 is the new Tesla P6. It is designed entirely for blade servers and delivers 16GB of memory and supports up to 16 instances of 1GB virtual desktop profiles.

Pascal-based Tesla accelerators provide IT departments the graphics and compute virtualization resources needed to meet demands and scale across the enterprise. Availability Quadro vDWS and NVIDIA GRID vPC solutions are available today in over 100 server systems worldwide, including those from Cisco, Citrix, Dell, HP, IBM, Lenovo, VMware and others, with support for new Pascal-based features and functionality starting September 1.

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Fujitsu Supercomputer For RIKEN Uses 24 NVIDIA DGX-1s

SINGAPORE, 7 March 2016Fujitsu announced today that it is using 24 NVIDIA DGX-1 AI systems to help build a Fujitsu supercomputer for RIKEN, Japan’s largest comprehensive research institution, for deep learning research.

The largest customer installation of DGX-1 systems to date, the Fujitsu supercomputer will accelerate the application of AI to solve complex challenges in healthcare, manufacturing and public safety.

“DGX-1 is like a time-machine for AI researchers,” said Jen-HsunHuang, founder and CEO of NVIDIA. “Enterprises, research centres and universities worldwide are adopting DGX-1 to ride the wave of deep learning —the technology breakthrough at the centreof the AI revolution.”

The RIKEN Center for Advanced IntelligenceProject will use the new Fujitsu supercomputer, scheduled to go online next month, to accelerate AI research in several areas, including medicine, manufacturing, healthcare and disaster preparedness.

“We believe that the NVIDIA DGX-1-based system will acceleratereal-world implementation of the latest AI technologies technologies as well as research into next-generation AI algorithms,” said Arimichi Kunisawa, head of the Technical Computing Solution Unit at Fujitsu Limited. “Fujitsu is leveraging its extensive experience in high-performance computing development and AI research to support R&D that utilises this system, contributing to the creation of a future in which AI is used to find solutions to a variety of social issues.”

 

The New Fujitsu Supercomputer Runs On 24 NVIDIA DGX-1s

Conventional HPC architectures are proving too costly and inefficient for meeting the needs of AI researchers. That’s why companies like Fujitsu and customers such as RIKEN are looking for GPU-based solutions that reduce cost and power consumption while increasing performance.

Each DGX-1 combines the power of eight NVIDIA Tesla P100 GPUs with an integrated software stack optimised for deep learning frameworks, delivering the performance of 250 conventional x86 servers.

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The system features a number of technological innovations unique to the DGX-1, including:

  • Containerised deep learning frameworks, optimised by NVIDIA for maximum GPU-accelerated deep learning training
  • Greater performance and multi-GPU scaling with NVIDIA NVLink, accelerating time to discovery
  • An integrated software and hardware architecture optimized for deep learning

The supercomputer will also use 32 Fujitsu PRIMERGY servers, which, combined with the DGX-1 systems, will boost its total theoretical processing performance to 4 petaflops when running half-precision floating-point calculations.

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NVIDIA Tesla P100 For PCIe-Based Servers Overview

On June 20, 2016, NVIDIA officially unveiled their Tesla P100 accelerator for PCIe-based servers. This is a long-expected PCI Express variant of the Tesla P100 accelerator that was launched in April using the NVIDIA NVLink interconnect. Let’s check out what’s new!

 

NVIDIA Tesla P100

The NVIDIA Tesla P100 was originally unveiled at the GPU Technology Conference on April 5, 2016. Touted as the world’s most advanced hyperscale data center accelerator, it was built around the new NVIDIA Pascal architecture and the proprietary NVIDIA NVLink high-speed GPU interconnect.

Like all other Pascal-based GPUs, the NVIDIA Tesla P100 is fabricated on the 16 nm FinFET process technology. Even with the much smaller process technology, the Tesla P100 is the largest FinFET chip ever built.

Unlike the Pascal-based GeForce GTX 1080 and GTX 1070 GPUs designed for desktop gaming though, the Tesla P100 uses HBM2 memory. In fact, the P100  is actually built on top of the HBM2 memory chips in a single package. This new package technology, Chip on Wafer on Substrate (CoWoS), allows for a 3X boost in memory bandwidth to 720 GB/s.

The NVIDIA NVLink interconnect allows up to eight Tesla P100 accelerators to be linked in a single node. This allows a single Tesla P100-based server node to outperform 48 dual-socket CPU server nodes.

 

Now Available With PCIe Interface

To make Tesla P100 available for HPC (High Performance Computing) applications, NVIDIA has just introduced the Tesla P100 with a PCI Express interface. This is basically the PCI Express version of the original Tesla P100.

 

Massive Leap In Performance

Such High Performance Computing servers can already make use of the NVIDIA Tesla K80 accelerators, that are based on the previous-generation NVIDIA Maxwell architecture. The new NVIDIA Pascal architecture, coupled with much faster HBM2 memory, allow for a massive leap in performance. Check out these results that NVIDIA provided :

Ultimately, the NVIDIA Tesla P100 for PCIe-based servers promises to deliver “dramatically more” performance for your money. As a bonus, the energy cost of running Tesla P100-based servers is much lower than CPU-based servers, and those savings accrue over time.

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Two Configurations

The NVIDIA Tesla P100 for PCIe-based servers will be slightly (~11-12%) slower than the NVLink version, turning out up to 4.7 teraflops of double-precision performance, 9.3 teraflops of single-precision performance, and 18.7 teraflops of half-precision performance.

The Tesla P100 will be offered in two configurations. The high-end configuration will have 16 GB of HBM2 memory with a maximum memory bandwidth of 720 GB/s. The lower-end configuration will have 12 GB of HBM2 memory with a maximum memory bandwidth of 540 GB/s.

 

Complete NVIDIA Slides

For those who are interested in more details, here are the NVIDIA Tesla P100 for PCIe-based Servers slides.

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NVIDIA Captures Three Major Computex 2016 Awards

TAIPEI,  May 31, 2016NVIDIA won big at the Computex 2016 Best Choice Awards, with the NVIDIA Tesla M40 GPU and NVIDIA Jetson TX1 module hauling in Gold Awards and the NVIDIA SHIELD Android TV clinching a Category Award.

Garnering these three prestigious awards extends the company’s winning streak — the longest of any international Computex exhibitor — to eight consecutive years. Taiwan’s President Tsai Ing-wen will hand out the awards.

Nearly 375 technology products from more than 140 vendors vied for the Best Choice Awards at Computex 2016, the largest technology tradeshow in Asia and second largest in the world. The Best Choice Awards, established in 2002, honour innovation, functionality and market potential.

The Gold Award-winning NVIDIA Tesla M40 GPU is the world’s fastest deep learning training accelerator. Purpose-built to dramatically reduce training time, the Tesla M40 can crank through deep learning models within hours versus days on CPU-based compute systems.

The NVIDIA Jetson TX1, the other Gold Award winner, is the world’s most advanced system for embedded visual computing. A supercomputer on a module that’s the size of a credit card, it offers embedded computing developers the highest performance, latest technology and the best development platform.

Winner of the Digital Entertainment & AR/VR Application Category Award, NVIDIA SHIELD transforms the living room entertainment experience with 4K streaming, advanced gaming and Android TV. It also comes with GeForce NOW, the only game-streaming service that delivers GeForce GTX gaming to the TV instantly.

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NVIDIA Expands NVIDIA GRID With Tesla M10 GPU

Singapore, 20 May 2016 NVIDIA today opened the path toward virtualising all enterprise applications with the introduction of NVIDIA GRID with the new NVIDIA Tesla M10 GPU. The offering provides the industry’s highest user density — supporting 64 users per board and 128 users per server — so businesses can deliver virtualised apps to all of their employees at an affordable cost.

The NVIDIA GRID software lets enterprises simplify deployments of virtual applications, desktops and workstations to meet all use cases and workloads. It provides a great user experience for every modern business application, such as Outlook, Office 2016, web browsers, Adobe Photoshop and the Windows 10 operating system.

In addition, the offering eases enterprise adoption through a flexible subscription model that allows organisations to balance capital and operating expenses while enjoying the lowest total cost of ownership.
Ongoing software updates provide the latest innovations to customers.

 

Exploding Growth of GPU-Accelerated Business Applications

The use of GPU-accelerated business applications by office workers is exploding. The percentage of accelerated apps has more than doubled in the past five years, with half that growth coming in the first months of 2016 alone, according to SysTrack Community data provided by Lakeside Software. To provide the best user experience, these applications increasingly use OpenGL and DirectX APIs, as well as graphics technology from the data center through to the device. To achieve the level of experience users require and have come to expect, virtual environments need access to the GPU.

“High-performing knowledge workers require high-performing business applications for maximum productivity. That requires GPU acceleration,” said Jim McHugh, vice president and general manager of the NVIDIA GRID business at NVIDIA. “With the highest user density in the industry, NVIDIA GRID with the Tesla M10 makes it easy and affordable for businesses to virtualise every application for knowledge workers with no compromise in erformance.”

“While the need for advanced GPU technology has commonly been associated with the usage of 3D applications, as enterprises make the move to software like Windows 10, Office 365, and other SaaS and web apps, IT departments will increasingly seek the benefits of GPU acceleration to provide everyday business tools to all of their users,” said Robert Young, research director, IT Service Management and Client Virtualisation Software at IDC.

NVIDIA GRID is the industry standard for graphics virtualisation, supported by every enterprise OEM with full compatibility with every PC application. By working with offerings from Citrix and VMware, the leading virtualisation vendors, it delivers amazing experience with virtual applications or remote desktop session host for less than US$2 a month per user and to virtual PCs for less than US$6 a month per user.

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The Tesla M10 joins the Tesla M6 and M60 as options for NVIDIA GRID, making GRID the only solution on the market that meets the performance needs of all enterprise users — from the most demanding designers and engineers to knowledge workers.

  • Increased user density per server: The Tesla M10 delivers a superior experience at the highest user density on the market, allowing up to 64 users per board or up to 128 users per server with just two boards. This enables enterprises to expand service to more employees at a lower cost per user.
  • Expanded software offering: NVIDIA GRID comes in three software meditions to meet different user needs. For office workers, NVIDIA Virtual Applications (vApps) and NVIDIA VirtualPC (vPC) deliver Windows 7, Windows 8 and Windows 10 virtual desktops and PC applications with native user experience. NVIDIA Virtual Workstation (vWS) delivers the power of a workstation from the data center, so designers, engineers and architects are free to work on their professional graphics applications from anywhere.
  • Flexible subscription model, ongoing updates: With ongoing innovation and feature updates in the software and the data center GPUs that power NVIDIA GRID, customers benefit from increased value through the life of the deployment. To ease adoption, NVIDIA is offering a new annual subscription model that offers customers access to the latest upgrades along with enterprise-class support and maintenance. Customers can buy up to three years of annual subscription upfront. Alternatively, they can choose the perpetual license model.

 

Tesla M10 Availability

The latest NVIDIA GRID software is available worldwidet today. The Tesla M10 will be generally available starting August 2016.

 

Experience NVIDIA GRID

The combined offering will be on display at Citrix Synergy starting May 24 at the Las Vegas Convention Center in NVIDIA booth 704G.

Users are encouraged to experience NVIDIA GRID for themselves through the NVIDIA GRID Test Drive. This experience gives users instant access to hours of NVIDIA GRID vGPU acceleration on a Windows desktop.

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4,500 NVIDIA Tesla GPU Upgrade For Piz Daint Supercomputer

Singapore, April 6, 2016—NVIDIA today announced that Pascal architecture-based NVIDIA Tesla GPU accelerators will power an upgraded version of Europe’s fastest supercomputer, the Piz Daint system at the Swiss National Supercomputing Center (CSCS) in Lugano, Switzerland. The upgrade is expected to more than double Piz Daint’s speed, with most of the system’s performance expected to come from its Tesla GPUs.

Piz Daint, named after a mountain in the Swiss Alps, currently delivers 7.8 petaflops of compute performance, or 7.8 quadrillion mathematical calculations per second. That puts it at No. 7 in the latest TOP500 list of the world’s fastest supercomputers. CSCS plans to upgrade the system later this year with 4,500 Pascal-based GPUs.

 

Piz Daint Supercomputer Upgrade

Pascal is the most advanced GPU architecture ever built, delivering unmatched performance and efficiency to power the most computationally demanding applications. Pascal-based Tesla GPUs will allow researchers to solve larger, more complex problems that are currently out of reach in cosmology, materials science, seismology, climatology and a host of other fields.

Pascal GPUs feature a number of breakthrough technologies, including second-generation High Bandwidth Memory (HBM2) that delivers three times higher bandwidth than the previous generation architecture, and 16nm FinFET technology for unprecedented energy efficiency. For scientists with near infinite computing needs, Pascal GPUs deliver a giant leap in application performance and time to discovery for their scientific research.

The upgrade will enable CSCS scientists to do simulations, data analysis and visualisations faster and more efficiently. Piz Daint will be used to analyse data from the Large Hadron Collider at CERN, the world’s largest particle accelerator. The upgrade will also accelerate research on the

Human Brain Project’s High Performance Analytics and Computing Platform, which currently uses Piz Daint. The project’s goal is to build neuromorphic computing systems that use the same principles of computation and cognitive architectures as the brain. The upgrade will also facilitate CSCS research in geophysics, cosmology and materials science.

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“We are taking advantage of NVIDIA GPUs to significantly accelerate simulations in such diverse areas as cosmology, materials science, seismology and climatology,” said Thomas Schulthess, professor of computational physics at ETH Zurich and director of the Swiss National Supercomputing Center. “Tesla accelerators represent a leap forward in computing, allowing our researchers to solve larger, more complex problems that are currently out of reach in a host of fields.”

“CSCS scientists are using Piz Daint to tackle some of the most important computational challenges of our day, like modeling the human brain and uncovering new insights into the origins of the universe,” said Ian Buck, vice president of Accelerated Computing at NVIDIA. “Tesla GPUs deliver a massive leap in application performance, allowing CSCS to push the limits of scientific discovery.”

 

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NVIDIA Tesla P100 GPU Launched

SINGAPORE, April 6, 2016—NVIDIA today introduced the NVIDIA Tesla P100 GPU, the most advanced accelerator ever built. The latest addition to the NVIDIA Tesla Accelerated Computing Platform, the Tesla P100 enables a new class of servers that can deliver the performance of hundreds of CPU server nodes.

Today’s data centres — vast network infrastructures with numerous interconnected commodity CPU servers — process large numbers of transactional workloads, such as web services. But they are inefficient at next-generation artificial intelligence and scientific applications, which require ultra-efficient, lightning-fast server nodes.

Based on the new NVIDIA Pascal GPU architecture with five breakthrough technologies, the Tesla P100 delivers unmatched performance and efficiency to power the most computationally demanding applications.

“Our greatest scientific and technical challenges — finding cures for cancer, understanding climate change, building intelligent machines — require a near-infinite amount of computing performance,” said Jen-Hsun Huang, CEO and co-founder, NVIDIA. “We designed the Pascal GPU architecture from the ground up with innovation at every level. It represents a massive leap forward in computing performance and efficiency, and will help some of the smartest minds drive tomorrow’s advances.”

Dr. John Kelly III, senior vice president, Cognitive Solutions and IBM Research, said: “As we enter this new era of computing, entirely new approaches to the underlying technologies will be required to fully realise the benefits of AI and cognitive. The combination of NVIDIA GPUs and OpenPOWER technology is already accelerating Watson’s learning of new skills. Together, IBM’s Power architecture and NVIDIA’s Pascal architecture with NVLink will further accelerate cognitive workload performance and advance the artificial intelligence industry.”

Five Architectural Breakthroughs

The Tesla P100 delivers its unprecedented performance, scalability and programming efficiency based on five breakthroughs:

  • NVIDIA Pascal architecture for exponential performance leap – A Pascal-based Tesla P100 solution delivers over a 12x increase in neural network training performance compared with a previous-generation NVIDIA Maxwell-based solution.
  • NVIDIA NVLink for maximum application scalability – The NVIDIA NVLink high-speed GPU interconnect scales applications across multiple GPUs, delivering a 5x acceleration in bandwidth compared to today’s best-in-class solution. Up to eight Tesla P100 GPUs can be interconnected with NVLink to maximise application performance in a single node, and IBM has implemented NVLink on its POWER8 CPUs for fast CPU-to-GPU communication.
  • 16nm FinFET for unprecedented energy efficiency – With 15.3 billion transistors built on 16 nanometer FinFET fabrication technology, the Pascal GPU is the world’s largest FinFET chip ever built.2 It is engineered to deliver the fastest performance and best energy efficiency for workloads with near-infinite computing needs.
  • CoWoS with HBM2 for big data workloads – The Pascal architecture unifies processor and data into a single package to deliver unprecedented compute efficiency. An innovative approach to memory design, Chip on Wafer on Substrate (CoWoS) with HBM2, provides a 3x boost in memory bandwidth performance, or 720GB/sec, compared to the Maxwell architecture.
  • New AI algorithms for peak performance – New half-precision instructions deliver more than 21 teraflops of peak performance for deep learning.
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The Tesla P100 GPU accelerator delivers a new level of performance for a range of HPC and deep learning applications, including the AMBER molecular dynamics code, which runs faster on a single server node with Tesla P100 GPUs than on 48 dual-socket CPU server nodes.

Training the popular AlexNet deep neural network would take 250 dual-socket CPU server nodes to match the performance of eight Tesla P100 GPUs.4 And the widely used weather forecasting application, COSMO, runs faster on eight Tesla P100 GPUs than on 27 dual-socket CPU servers.

The first accelerator to deliver more than 5 and 10 teraflops of double-precision and singleprecision performance, respectively, the Tesla P100 provides a giant leap in processing capabilities and time-to-discovery for research across a broad spectrum of domains.

Tesla P100 Specifications

Specifications of the Tesla P100 GPU accelerator include:

  • 5.3 teraflops double-precision performance, 10.6 teraflops single-precision performance and 21.2 teraflops half-precision performance with NVIDIA GPU BOOST technology
  • 160GB/sec bi-directional interconnect bandwidth with NVIDIA NVLink
  • 16GB of CoWoS HBM2 stacked memory
  • 720GB/sec memory bandwidth with CoWoS HBM2 stacked memory
  • Enhanced programmability with page migration engine and unified memory
  • ECC protection for increased reliability
  • Server-optimised for highest data centre throughput and reliability

Availability

General availability for the Pascal-based NVIDIA Tesla P100 GPU accelerator in the new NVIDIA DGX-1 deep learning system is in June. It is also expected to be available beginning in early 2017 from leading server manufacturers.

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NVIDIA DGX-1 Deep Learning Supercomputer Launched

April 6, 2016 — NVIDIA today unveiled the NVIDIA DGX-1, the world’s first deep learning supercomputer to meet the unlimited computing demands of artificial intelligence.

The NVIDIA DGX-1 is the first system designed specifically for deep learning — it comes fully integrated with hardware, deep learning software and development tools for quick, easy deployment. It is a turnkey system that contains a new generation of GPU accelerators, delivering the equivalent throughput of 250 x86 servers.

The NVIDIA DGX-1 deep learning system enables researchers and data scientists to easily harness the power of GPU-accelerated computing to create a new class of intelligent machines that learn, see and perceive the world as humans do. It delivers unprecedented levels of computing power to drive next-generation AI applications, allowing researchers to dramatically reduce the time to train larger, more sophisticated deep neural networks.

NVIDIA designed the DGX-1 for a new computing model to power the AI revolution that is sweeping across science, enterprises and increasingly all aspects of daily life. Powerful deep neural networks are driving a new kind of software created with massive amounts of data, which require considerably higher levels of computational performance.

“Artificial intelligence is the most far-reaching technological advancement in our lifetime,” said Jen-Hsun Huang, CEO and co-founder of NVIDIA. “It changes every industry, every company, everything. It will open up markets to benefit everyone. Data scientists and AI researchers today spend far too much time on home-brewed high performance computing solutions. The DGX-1 is easy to deploy and was created for one purpose: to unlock the powers of superhuman capabilities and apply them to problems that were once unsolvable.”

 

Powered by Five Breakthroughs

The NVIDIA DGX-1 deep learning system is built on NVIDIA Tesla P100 GPUs, based on the new NVIDIA Pascal GPU architecture. It provides the throughput of 250 CPU-based servers, networking, cables and racks — all in a single box.

The DGX-1 features four other breakthrough technologies that maximise performance and ease of use. These include the NVIDIA NVLink high-speed interconnect for maximum application scalability; 16nm FinFET fabrication technology for unprecedented energy efficiency; Chip on Wafer on Substrate with HBM2 for big data workloads; and new half-precision instructions to deliver more than 21 teraflops of peak performance for deep learning.

Together, these major technological advancements enable DGX-1 systems equipped with Tesla P100 GPUs to deliver over 12x faster training than four-way NVIDIA Maxwell architecturebased solutions from just one year ago.

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The Pascal architecture has strong support from the artificial intelligence ecosystem.

“NVIDIA GPU is accelerating progress in AI. As neural nets become larger and larger, we not only need faster GPUs with larger and faster memory, but also much faster GPU-to-GPU communication, as well as hardware that can take advantage of reduced-precision arithmetic. This is precisely what Pascal delivers,” said Yann LeCun, director of AI Research at Facebook.

Andrew Ng, chief scientist at Baidu, said: “AI computers are like space rockets: The bigger the better. Pascal’s throughput and interconnect will make the biggest rocket we’ve seen yet.” NVIDIA Launches World’s First Deep Learning Supercomputer

“Microsoft is developing super deep neural networks that are more than 1,000 layers,” said Xuedong Huang, chief speech scientist at Microsoft Research. “NVIDIA Tesla P100’s impressive horsepower will enable Microsoft’s CNTK to accelerate AI breakthroughs.”

 

Comprehensive Deep Learning Software Suite

The NVIDIA DGX-1 system includes a complete suite of optimised deep learning software that allows researchers and data scientists to quickly and easily train deep neural networks. The DGX-1 software includes the NVIDIA Deep Learning GPU Training System (DIGITS), a complete, interactive system for designing deep neural networks (DNNs).

It also includes the newly released NVIDIA CUDA Deep Neural Network library (cuDNN) version 5, a GPUaccelerated library of primitives for designing DNNs. It also includes optimised versions of several widely used deep learning frameworks — Caffe, Theano and Torch. The DGX-1 additionally provides access to cloud management tools, software updates and a repository for containerised applications.

 

NVIDIA DGX-1 Specifications

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  • Up to 170 teraflops of half-precision (FP16) peak performance
  • Eight Tesla P100 GPU accelerators, 16GB memory per GPU
  • NVLink Hybrid Mesh Cube
  • 7TB SSD DL Cache
  • Dual 10GbE, Quad InfiniBand 100Gb networking
  • 3U – 3200W

Optional support services for the NVIDIA DGX-1 improve productivity and reduce downtime for production systems. Hardware and software support provides access to NVIDIA deep learning expertise, and includes cloud management services, software upgrades and updates, and priority resolution of critical issues.

 

NVIDIA DGX-1 Availability

General availability for the NVIDIA DGX-1 deep learning system in the United States is in June, and in other regions beginning in the third quarter direct from NVIDIA and select systems integrators.

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NVIDIA To Accelerate Research At Monash University

MELBOURNE, Australia — Feb. 29, 2016NVIDIA today announced it is collaborating with Monash University to power a new wave of GPU-accelerated research, marking the first step toward a deeply integrated research and development program between research and industry innovation in Australia.

At a ceremony today at Monash’s Clayton campus in Melbourne, NVIDIA’s Chief Technology Officer of Accelerated Computing, Steve Oberlin (right), announced that Monash will join the NVIDIA Technology Centre Asia Pacific. The centre is dedicated to driving scientific research and development work in the region.

[adrotate banner=”4″]NVIDIA and Monash will jointly fund research students, facilitate access to GPU-accelerated computing technologies and leverage their worldwide network of experts to provide industryrelevant training and knowledge exchange.

Also announced at the ceremony by the Australian Chief Scientist Alan Finkel AO was the M3 supercomputer, the third-generation supercomputer available through the MASSIVE (Multimodal Australian ScienceS Imaging and Visualisation Environment) facility. Powered by ultra-high-performance NVIDIA Tesla K80 GPU accelerators, M3 will provide new simulation and real-time data processing capabilities to a wide selection of Australian researchers.

“Monash University has used GPU-accelerated computing to drive discovery in key areas like medicine, robotics, visualisation, mathematics, engineering, and computational chemistry for years,” said Oberlin. “We’re deepening our long-standing relationship with Monash, and look forward to working with them to expand their success using the latest GPU-accelerated computing technologies to drive insight and innovation.”

“Our collaboration with NVIDIA will take Monash research to new heights. By coupling some of Australia’s best researchers with NVIDIA’s accelerated computing technology we’re going to see some incredible impact. Our scientists will produce code that runs faster, but more significantly, their focus on deep learning algorithms will produce outcomes that are smarter,” said Professor Ian Smith, Vice Provost (Research and Research Infrastructure), Monash University.

 

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NVIDIA GRID 2.0 Now Available on Cisco Blade & Rack Servers

For businesses that run on Cisco blade and rack servers, and want to extend their reach with desktop virtualization using the latest high-performance graphics, the wait is over. NVIDIA GRID 2.0 with the NVIDIA Tesla M60 GPU accelerator is now supported on the Cisco UCS C240 M4 rack server.

And, in a first for GRID-based Cisco blades, Cisco is supporting NVIDIA GRID 2.0 with the NVIDIA Tesla M6 on its UCS B200 M4 blade server.

These combinations bring unparalleled graphics performance to high-end applications on any device, anywhere. Organizations can expand their virtualization footprint without compromising performance, user experience or security. They can boost employee productivity with faster access to files and real-time collaboration. And they can centralize IT, so all workloads can be managed and delivered from the data center.

 

Cisco’s Workhorse of Virtualization Deployments

The Cisco UCS B200 M4 blade server is one of the newest in the Cisco UCS portfolio. It delivers enterprise-class performance, flexibility and optimization for data centers and remote sites. It also offers excellent virtual desktop density per blade and ease of management, making for an attractive total cost of ownership.

Key to the Cisco blade’s popularity is its predictable and scalable performance for virtual desktop users. Cisco and NVIDIA have created an unmatched level of integration between the UCS B200 M4 and Tesla M6 card. The card can be discovered and managed via Cisco UCS Manager for administration through a single pane of glass.

 

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Working on the Edge, with NVIDIA GRID

NVIDIA GRID on Cisco blade servers opens new opportunities in industries such as manufacturing, oil and gas, and design, where businesses using applications from the likes of ESRI, AutoCAD, Petrel or Siemens want the flexibility to work from anywhere but require the same high-end application performance on a virtual desktop as they do on a physical one.

By bringing graphics acceleration to virtualization, NVIDIA GRID unlocks the promise of productivity, mobility, security and flexibility for every user. NVIDIA GRID technology on Cisco blade servers pushes desktop virtualization to the edge.

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NVIDIA GPUs Power Facebook’s Deep Machine Learning

Dec. 11, 2015—NVIDIA today announced that Facebook will power its next-generation computing system with the NVIDIA® Tesla® Accelerated Computing Platform, enabling it to drive a broad range of machine learning applications.

While training complex deep neural networks to conduct machine learning can take days or weeks on even the fastest computers, the Tesla platform can slash this by 10-20x. As a result, developers can innovate more quickly and train networks that are more sophisticated, delivering improved capabilities to consumers.

Facebook is the first company to adopt NVIDIA Tesla M40 GPU accelerators, introduced last month, to train deep neural networks. They will play a key role in the new “Big Sur” computing platform, Facebook AI Research’s (FAIR) purpose-built system designed specifically for neural network training.

“Deep learning has started a new era in computing,” said Ian Buck, vice president of accelerated computing at NVIDIA. “Enabled by big data and powerful GPUs, deep learning algorithms can solve problems never possible before. Huge industries from web services and retail to healthcare and cars will be revolutionised. We are thrilled that NVIDIA GPUs have been adopted as the engine of deep learning. Our goal is to provide researchers and companies with the most productive platform to advance this exciting work.”

In addition to reducing neural network training time, GPUs offer a number of other advantages. Their architectural compatibility from generation to generation provides seamless speed-ups for future GPU upgrades. And the Tesla platform’s growing global adoption facilitates open collaboration with researchers around the world, fueling new waves of discovery and innovation in the machine learning field.

 

Big Sur Optimised for Machine Learning

NVIDIA worked with Facebook engineers on the design of Big Sur, optimising it to deliver maximum performance for machine learning workloads, including the training of large neural networks across multiple Tesla GPUs.

[adrotate banner=”4″]Two times faster than Facebook’s existing system, Big Sur will enable the company to train twice as many neural networks – and to create neural networks that are twice as large – which will help develop more accurate models and new classes of advanced applications.

“The key to unlocking the knowledge necessary to develop more intelligent machines lies in the capability of our computing systems,” said Serkan Piantino, engineering director for FAIR. “Most of the major advances in machine learning and AI in the past few years have been contingent on tapping into powerful GPUs and huge data sets to build and train advanced models.”

The addition of Tesla M40 GPUs will help Facebook make new advancements in machine learning research and enable teams across its organisation to use deep neural networks in a variety of products and services.

 

First Open Sourced AI Computing Architecture

Big Sur represents the first time a computing system specifically designed for machine learning and artificial intelligence (AI) research will be released as an open source solution.

Committed to doing its AI work in the open and sharing its findings with the community, Facebook intends to work with its partners to open source Big Sur specifications via the Open Compute Project. This unique approach will make it easier for AI researchers worldwide to share and improve techniques, enabling future innovation in machine learning by harnessing the power of GPU accelerated computing.