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NVIDIA, Microsoft Launch Industry-Standard Hyperscale GPU Accelerator

NVIDIA with Microsoft today unveiled blueprints for a new hyperscale GPU accelerator to drive AI cloud computing. Providing hyperscale data centers with a fast, flexible path for AI, the new HGX-1 hyperscale GPU accelerator is an open-source design released in conjunction with Microsoft's Project Olympus.

HGX-1 does for cloud-based AI workloads what ATX -- Advanced Technology eXtended -- did for PC motherboards when it was introduced more than two decades ago. It establishes an industry standard that can be rapidly and efficiently embraced to help meet surging market demand. The new architecture is designed to meet the exploding demand for AI computing in the cloud -- in fields such as autonomous driving, personalized healthcare, superhuman voice recognition, data and video analytics, and molecular simulations.

AMD's VEGA Alive and Well - Announced MI25 VEGA as Deep Learning Accelerator

The team at Videocardz has published a story with some interesting slides regarding AMD's push towards the highly-lucrative deep learning market with their INSTINCT line-up of graphics cards - and VEGA being announced as a full-fledged solution means we are perhaps (hopefully) closer to seeing a solution based on it for the consumer market as well.

Alongside the VEGA-based MI25, AMD also announced the MI6 (5.7 TFLOPS in FP32 operations, with 224 GB/s of memory bandwidth and <150 W of board power), looking suspiciously like a Polaris 10 card in disguise; and the MI8 (which appropriately delivers 8.2 TFLOPS in FP32 computations, as well as 512 GB/s memory bandwidth and <175 W typical board power), with the memory bandwidth numbers being the most telling, and putting the MI8 closely along a Fiji architecture-based solution.

NVIDIA Announces DGX SaturnV: The World's Most Efficient Supercomputer

This week NVIDIA announced their latest innovation to the HPC landscape, the DGX SaturnV. Destined for the likes of universities and companies with a need for deep learning capabilities, the DGX SaturnV sets a new benchmark for energy efficiency in High Performance Computing. While not managing the title of the fastest supercomputer this year, the SaturnV takes a respectable placing of 28th in the top 500 list, while promising much lower running costs for performance on tap.

Capable of delivering 9.46 GFLOPS of computational speed per Watt of energy consumed, it bests last years best effort of 6.67 GFLOPS/W by 42%. The SaturnV is comprised of 125 DGX-1 deep learning systems, and each DGX-1 contains no less than eight Tesla P100 cards. Where a single GTX1080 can churn out 138 GFLOPS of FP16 calculations, a single Telsa P100 can deliver a massive 21.2 TFLOPS. The singular DGX-1 units are already in the field, including being used by NVIDIA themselves.

IBM and NVIDIA Team Up on World's Fastest Deep Learning Enterprise Solution

IBM and NVIDIA today announced collaboration on a new deep learning tool optimized for the latest IBM and NVIDIA technologies to help train computers to think and learn in more human-like ways at a faster pace. Deep learning is a fast growing machine learning method that extracts information by crunching through millions of pieces of data to detect and rank the most important aspects from the data. Publicly supported among leading consumer web and mobile application companies, deep learning is quickly being adopted by more traditional business enterprises.

Deep learning and other artificial intelligence capabilities are being used across a wide range of industry sectors; in banking to advance fraud detection through facial recognition; in automotive for self-driving automobiles and in retail for fully automated call centers with computers that can better understand speech and answer questions.

NVIDIA Launches Maxed-out GP102 Based Quadro P6000

Late last week, NVIDIA announced the TITAN X Pascal, its fastest consumer graphics offering targeted at gamers and PC enthusiasts. The reign of TITAN X Pascal being the fastest single-GPU graphics card could be short-lived, as NVIDIA announced a Quadro product based on the same "GP102" silicon, which maxes out its on-die resources. The new Quadro P6000, announced at SIGGRAPH alongside the GP104-based Quadro P5000, features all 3,840 CUDA cores physically present on the chip.

Besides 3,840 CUDA cores, the P6000 features a maximum FP32 (single-precision floating point) performance of up to 12 TFLOP/s. The card also features 24 GB of GDDR5X memory, across the chip's 384-bit wide memory interface. The Quadro P5000, on the other hand, features 2,560 CUDA cores, up to 8.9 TFLOP/s FP32 performance, and 16 GB of GDDR5X memory across a 256-bit wide memory interface. It's interesting to note that neither cards feature full FP64 (double-precision) machinery, and that is cleverly relegated to NVIDIA's HPC product line, the Tesla P-series.

NVIDIA Announces a PCI-Express Variant of its Tesla P100 HPC Accelerator

NVIDIA announced a PCI-Express add-on card variant of its Tesla P100 HPC accelerator, at the 2016 International Supercomputing Conference, held in Frankfurt, Germany. The card is about 30 cm long, 2-slot thick, and of standard height, and is designed for PCIe multi-slot servers. The company had introduced the Tesla P100 earlier this year in April, with a dense mezzanine form-factor variant for servers with NVLink.

The PCIe variant of the P100 offers slightly lower performance than the NVLink variant, because of lower clock speeds, although the core-configuration of the GP100 silicon remains unchanged. It offers FP64 (double-precision floating-point) performance of 4.70 TFLOP/s, FP32 (single-precision) performance of 9.30 TFLOP/s, and FP16 performance of 18.7 TFLOP/s, compared to the NVLink variant's 5.3 TFLOP/s, 10.6 TFLOP/s, and 21 TFLOP/s, respectively. The card comes in two sub-variants based on memory, there's a 16 GB variant with 720 GB/s memory bandwidth and 4 MB L3 cache, and a 12 GB variant with 548 GB/s and 3 MB L3 cache. Both sub-variants feature 3,584 CUDA cores based on the "Pascal" architecture, and core clock speed of 1300 MHz.

NVIDIA Launches World's First Deep Learning Supercomputer

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 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 Unveils the Tesla P100 HPC Board based on "Pascal" Architecture

NVIDIA unveiled the Tesla P100, the first product based on the company's "Pascal" GPU architecture. At its core is a swanky new multi-chip module, similar in its essential layout to the AMD "Fiji." A 15 billion-transistor GPU die sits on top of a silicon wafer, through which a 4096-bit wide HBM2 memory interface wires it to four 3D HBM2 stacks; and with the wafer sitting on the fiberglass substrate that's rooted into the PCB over a ball-grid array. With the GPU die, wafer, and memory dies put together, this package has a cumulative transistor count of 150 billion transistors. The GPU die is built on the 16 nm FinFET process, and is 600 mm² in area.

The P100 sits on top of a space-efficient PCB that looks less like a video card, and more like a compact module that can be tucked away into ultra-high density supercomputing cluster boxes, such as the new NVIDIA DGX-1. The P100 offers a double-precision (FP64) compute performance of 5.3 TFLOP/s, FP32 performance of 10.6 TFLOP/s, and FP16 performance of a whopping 21.2 TFLOP/s. The chip has registers as big as 14.2 MB, and an L2 cache of 4 MB. In addition to PCI-Express, each P100 chip will be equipped with NVLink, and in-house developed high-bandwidth interconnect by NVIDIA, with bandwidths as high as 80 GB/s per direction, 160 GB/s both directions. This allows extremely high-bandwidth paths between GPUs, so they could share memory and work more like single-GPUs. The P100 is already in volume production, with its target customers already having bought it all the way up to its OEM channel availability some time in Q1-2017.

NVIDIA GP100 Silicon to Feature 4 TFLOPs DPFP Performance

NVIDIA's upcoming flagship GPU based on its next-generation "Pascal" architecture, codenamed GP100, is shaping up to be a number-crunching monster. According to a leaked slide by an NVIDIA research fellow, the company is designing the chip to serve up double-precision floating-point (DPFP) performance as high as 4 TFLOP/s, a 3-fold increase from the 1.31 TFLOP/s offered by the Tesla K20, based on the "Kepler" GK110 silicon.

The same slide also reveals single-precision floating-point (SPFP) performance to be as high as 12 TFLOP/s, four times that of the GK110, and nearly double that of the GM200. The slide also appears to settle the speculation on whether GP100 will use stacked HBM2 memory, or GDDR5X. Given the 1 TB/s memory bandwidth mentioned on the slide, we're inclined to hand it to stacked HBM2.


Jim Keller to Lead Autopilot Hardware Team at Tesla Motors

Elon Musk handed over the reins of one of Tesla Motors' most important research and development divisions, autopilot, to chip whiz Jim Keller. Keller joined Tesla Motors as Vice President of Autopilot Hardware Engineering. With Tesla being at the very frontier of automobile development, and self-driving cars being the next big thing for the industry, Keller holds an enviable, albeit challenging position.

Jim Keller led teams that designed some of AMD's most commercially successful processors, before a stint at Apple, where he helped it gain hardware self-reliance with the company's Ax series SoCs; and returning to AMD, and leading the team that designed the company's upcoming "Zen" and K-12 micro-architectures. Tesla cars are currently driven by electronics powered by NVIDIA Tegra SoCs. With NVIDIA's immeasurable investments in deep-learning tech that forms the foundation of self-driving car hardware, and Tesla Motors yet choosing a CPU designer to lead its autopilot division, it's easy to speculate that Musk's company is seeking the same kind of hardware self-reliance that Apple did, as its iOS devices were taking off.

Source: Electrek

NVIDIA Details "Pascal" Some More at GTC Japan

NVIDIA revealed more details of its upcoming "Pascal" GPU architecture at the Japanese edition of the Graphics Technology Conference. The architecture will be designed to nearly double performance/Watt over the current "Maxwell" architecture, by implementing the latest tech. This begins with stacked HBM2 (high-bandwidth memory 2). The top "Pascal" based product will feature four 4-gigabyte HBM2 stacks, totaling 16 GB of memory. The combined memory bandwidth for the chip will be 1 TB/s. Internally, bandwidths can touch as high as 2 TB/s. The chip itself will support up to 32 GB of memory, and so enterprise variants (Quadro, Tesla), could max out the capacity. The consumer GeForce variant is expected to serve up 16 GB.

It's also becoming clear that NVIDIA will build its "Pascal" chips on the 16 nanometer FinFET process (AMD will build its next-gen chips on more advanced 14 nm process). NVIDIA is innovating a new interconnect called NVLink, which will change the way the company has been building dual-GPU graphics cards. Currently, dual-GPU cards are essentially two graphics cards on a common PCB, with PCIe bandwidth from the slot shared by a bridge-chip, and an internal SLI bridge connecting the two GPUs. With NVLink, the two GPUs will be interconnected with an 80 GB/s bi-directional data path, letting each GPU directly address memory controlled by the other. This should greatly improve memory management in games that take advantage of newer APIs such as DirectX 12 and Vulkan; and prime the graphics card for higher display resolutions. NVIDIA is expected to launch its first "Pascal" based products in the first half of 2016.

Source: VR World

NVIDIA GPUs to Accelerate Microsoft Azure

NVIDIA today announced that Microsoft will offer NVIDIA GPU-enabled professional graphics applications and accelerated computing capabilities to customers worldwide through its cloud platform, Microsoft Azure. Deploying the latest version of NVIDIA GRID in its new N-Series virtual machine offering, Azure is the first cloud computing platform to provide NVIDIA GRID 2.0 virtualized graphics for enterprise customers.

For the first time, businesses will have the ability to deploy NVIDIA Quadro-grade professional graphics applications and accelerated computing on-premises, in the cloud through Azure, or via a hybrid of the two using both Windows and Linux virtual machines. Azure will also offer customers supercomputing-class performance, with the addition of the NVIDIA Tesla Accelerated Computing Platform's flagship Tesla K80 GPU accelerators, for the most computationally demanding data center and high performance computing (HPC) applications.

IBM, NVIDIA and Mellanox Launch Design Center for Big Data and HPC

IBM, in collaboration with NVIDIA and Mellanox, today announced the establishment of a POWER Acceleration and Design Center in Montpellier, France to advance the development of data-intensive research, industrial, and commercial applications. Born out of the collaborative spirit fostered by the OpenPOWER Foundation - a community co-founded in part by IBM, NVIDIA and Mellanox supporting open development on top of the POWER architecture - the new Center provides commercial and open-source software developers with technical assistance to enable them to develop high performance computing (HPC) applications.

Technical experts from IBM, NVIDIA and Mellanox will help developers take advantage of OpenPOWER systems leveraging IBM's open and licensable POWER architecture with the NVIDIA Tesla Accelerated Computing Platform and Mellanox InfiniBand networking solutions. These are the class of systems developed collaboratively with the U.S. Department of Energy for the next generation Sierra and Summit supercomputers and to be used by the United Kingdom's Science and Technology Facilities Council's Hartree Centre for big data research.

NVIDIA Unveils Tesla K80 Dual-Chip Compute Accelerator

NVIDIA today unveiled a new addition to the NVIDIA Tesla Accelerated Computing Platform: the Tesla K80 dual-GPU accelerator, the world's highest performance accelerator designed for a wide range of machine learning, data analytics, scientific, and high performance computing (HPC) applications.

The Tesla K80 dual-GPU is the new flagship offering of the Tesla Accelerated Computing Platform, the leading platform for accelerating data analytics and scientific computing. It combines the world's fastest GPU accelerators, the widely used CUDA parallel computing model, and a comprehensive ecosystem of software developers, software vendors, and datacenter system OEMs.

GIGABYTE Releases its Latest GPU Computing Server

GIGABYTE Technology, a leading creator of high performance server hardware, is happy to announce today the release of its G210-H4G. Developed in partnership with Carri Systems, a French specialist of GPU computing & HPC solutions, this product is a 2U rackmount housing 4 blades capable of receiving one NVIDIA GRID, Tesla or AMD FirePro card each.

In the context of an unfolding GPU virtualization market and the vast amount of new possibilities that it opens, the G210-H4G is an elegant solution designed with simplicity and flexibility in mind to let organizations, big and small, enjoy the benefits of this fast-growing computing trend.

Cray Launches New High Density Cluster Packed With NVIDIA GPU Accelerators

Global supercomputer leader Cray Inc. today announced the launch of the Cray CS-Storm -- a high-density accelerator compute system based on the Cray CS300 cluster supercomputer. Featuring up to eight NVIDIA Tesla GPU accelerators and a peak performance of more than 11 teraflops per node, the Cray CS-Storm system is one of the most powerful single-node cluster architectures available today.

Designed to support highly scalable applications in areas such as energy, life sciences, financial services, and geospatial intelligence, the Cray CS-Storm provides exceptional performance, energy efficiency and reliability within a small footprint. The system leverages the supercomputing architecture of the air-cooled Cray CS300 system, and includes the Cray Advanced Cluster Engine cluster management software, the complete Cray Programming Environment on CS, and NVIDIA Tesla K40 GPU accelerators. The Cray CS-Storm system includes Intel Xeon E5 2600 v2 processors.

Eurotech, AppliedMicro and NVIDIA Develop New HPC System Architecture

Eurotech, a leading provider of embedded and supercomputing technologies, has teamed up with Applied Micro Circuits Corporation and NVIDIA to develop a new, original high performance computing (HPC) system architecture that combines extreme density and best-in-class energy efficiency. The new architecture is based on an innovative highly modular and scalable packaging concept.

Eurotech, which has years of significant experience in designing and manufacturing original HPC systems, has successfully developed an HPC systems architecture that optimizes the benefits of greater density, as well as the energy efficiency of ARM processors and high-performance GPU accelerators.

NVIDIA GPUs Open the Door to ARM64 Entry Into High Performance Computing

NVIDIA today announced that multiple server vendors are leveraging the performance of NVIDIA GPU accelerators to launch the world's first 64-bit ARM development systems for high performance computing (HPC).

ARM64 server processors were primarily designed for micro-servers and web servers because of their extreme energy efficiency. Now, they can tackle HPC-class workloads when paired with GPU accelerators using the NVIDIA CUDA 6.5 parallel programming platform, which supports 64-bit ARM processors.

NVIDIA Slides Supercomputing Technology Into the Car With Tegra K1

NVIDIA's new Tegra K1 mobile processor will help self-driving cars advance from the realm of research into the mass market with its automotive-grade version of the same GPU that powers the world's 10 most energy-efficient supercomputers. The first mobile processor to bring advanced computational capabilities to the car, the NVIDIA Tegra K1 runs a variety of auto applications that had previously not been possible with such low power consumption.

Tegra K1 features a quad-core CPU and a 192-core GPU using the NVIDIA Kepler architecture, the basis for NVIDIA's range of powerful GPUs -- including the processors that are used in the top 10 systems featured in the latest Green500 list of the world's most energy-efficient supercomputers. Tegra K1 will drive camera-based, advanced driver assistance systems (ADAS) -- such as pedestrian detection, blind-spot monitoring, lane-departure warning and street sign recognition -- and can also monitor driver alertness via a dashboard-mounted camera.

ASUS ESC4000 G2 Servers Support Tesla K40 GPU Accelerators

ASUS today announced that its world-leading ESC4000 G2 series server range now supports the next-generation NVIDIA Tesla K40 (Kepler) GPU accelerators.

ESC4000 G2 series ranks very highly in the world's high-performance computing (HPC) tables, claiming the number 59 spot in the November 2013 TOP500 - the definitive list of the world's most powerful commercially available computers. The ESC4000 G2-powered SANAM cluster put in an incredible performance to deliver total processing power of 532.6TFLOPS.

Supermicro Debuts 8x GPU SuperServer Optimized for the NVIDIA Tesla K40

Super Micro Computer, Inc., a global leader in high-performance, high-efficiency server, storage technology and green computing, exhibits its latest high-performance computing (HPC) solutions at the Supercomputing 2013 (SC13) conference this week in Denver, Colorado. In sync with the launch of the NVIDIA Tesla K40 GPU accelerator, Supermicro debuts new 4U 8x GPU SuperServer that supports the new and existing active or passive GPUs (up to 300 W) with an advanced cooling architecture that splits the CPU (up to 150 W x2) and GPU (up to 300 W x8) cooling zones on separate levels for maximum performance and reliability.

In addition, Supermicro has 1U, 2U, 3U SuperServers, FatTwin, SuperWorkstations and SuperBlade platforms ready to support the new K40 GPU accelerator. These high performance, high density servers support up to twenty GPU accelerators per system and in scaled out Super Clusters provide massive parallel processing power to accelerate the most demanding compute intensive applications. Supermicro's new platforms extend the industry's most comprehensive line of servers, storage, networking and server management solutions optimized for Engineering and Scientific Research, Modeling, Simulation and HPC supercomputing applications.

IBM, NVIDIA to Supercharge Corporate Data Center Applications

NVIDIA and IBM today announced plans to collaborate on GPU-accelerated versions of IBM's wide portfolio of enterprise software applications on IBM Power Systems. The move marks the first time that GPU accelerator technology will move beyond the realm of supercomputing and into the heart of enterprise-scale data centers. The collaboration aims to enable IBM customers to more rapidly process, secure and analyze massive volumes of streaming data.

"Companies are looking for new and more efficient ways to drive business value from Big Data and analytics," said Tom Rosamilia, senior vice president, IBM Systems & Technology Group and Integrated Supply Chain. "The combination of IBM and NVIDIA processor technologies can provide clients with an advanced and efficient foundation to achieve this goal."

Cray Adds NVIDIA Tesla K40 to Its Complete Line of Supercomputing Systems

Global supercomputer leader Cray Inc. today announced the Cray CS300 line of cluster supercomputers and the Cray XC30 supercomputers are now available with the NVIDIA Tesla K40 GPU accelerators. Designed to solve the most demanding supercomputing challenges, the NVIDIA Tesla K40 provides 40 percent higher peak performance than its predecessor, the Tesla K20X GPU.

"The addition of the NVIDIA K40 GPUs furthers our vision for Adaptive Supercomputing, which provides outstanding performance with a computing architecture that accommodates powerful CPUs and highly-advanced accelerators from leading technology companies like NVIDIA," said Barry Bolding, vice president of marketing at Cray. "We have proven that acceleration can be productive at high scalability with Cray systems such as 'Titan', 'Blue Waters', and most recently with the delivery of a Cray XC30 system at the Swiss National Supercomputing Centre (CSCS). Together with Cray's latest OpenACC 2.0 compiler, the new NVIDIA K40 GPUs can process larger datasets, reach higher levels of acceleration and provide more efficient compute performance, and we are pleased these features are now available to customers across our complete portfolio of supercomputing solutions."
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