Showing posts with label Nvidia. Show all posts
Showing posts with label Nvidia. Show all posts

Monday, May 27, 2019

NVIDIA unveils EGX Edge Computing Platform

NVIDIA introduced its EGX edge computing platform for performing low-latency AI on continuous streaming data between 5G base stations or other edge locations.

NVIDIA said EGX was created to perform instantaneous, high-throughput AI where data is created – with guaranteed response times, while reducing the amount of data that must be sent to the cloud.

“Enterprises demand more powerful computing at the edge to process their oceans of raw data — streaming in from countless interactions with customers and facilities — to make rapid, AI-enhanced decisions that can drive their business,” said Bob Pette, vice president and general manager of Enterprise and Edge Computing at NVIDIA. “A scalable platform like NVIDIA EGX allows them to easily deploy systems to meet their needs on premises, in the cloud or both.”

EGX begins with the tiny NVIDIA Jetson Nano, which delivers one-half trillion operations per second (TOPS) of processing in only a few watts, and scales all the way to a full rack of NVIDIA T4 servers, delivering more than 10,000 TOPS.

EGX servers are available from global enterprise computing providers ATOS, Cisco, Dell EMC, Fujitsu, Hewlett Packard Enterprise, Inspur and Lenovo. They are also available from major server and IoT system makers Abaco, Acer, ADLINK, Advantech, ASRock Rack, ASUS, AverMedia, Cloudian, Connect Tech, Curtiss-Wright, GIGABYTE, Leetop, MiiVii, Musashi Seimitsu, QCT, Sugon, Supermicro, Tyan, WiBase and Wiwynn.

NVIDIA EGX servers are tuned for NVIDIA Edge Stack and NGC-Ready validated for CUDA-accelerated containers.

NVIDIA has partnered with Red Hat to integrate and optimize NVIDIA Edge Stack with OpenShift. NVIDIA Edge Stack is optimized software that includes NVIDIA drivers, a CUDA Kubernetes plugin, a CUDA container runtime, CUDA-X libraries and containerized AI frameworks and applications, including TensorRT, TensorRT Inference Server and DeepStream.

NVIDIA is also promoting “On-Prem AI Cloud-in-a-Box”, which combines the full range of NVIDIA AI computing technologies with Red Hat OpenShift and NVIDIA Edge Stack together with Mellanox and Cisco security, networking and storage technologies.

“Mellanox Smart NICs and switches provide the ideal I/O connectivity for data access that scale from the edge to hyperscale data centers,” said Michael Kagan, chief technology officer at Mellanox Technologies. “The combination of high-performance, low-latency and accelerated networking provides a new infrastructure tier of computing that is critical to efficiently access and supply the data needed to fuel the next generation of advanced AI solutions on edge platforms such as NVIDIA EGX.”

“Cisco is excited to collaborate with NVIDIA to provide edge-to-core full stack solutions for our customers, leveraging Cisco’s EGX-enabled platforms with Cisco compute, fabric, storage, and management software and our leading Ethernet and IP-based networking technologies,” said Kaustubh Das, vice president of Cisco Computing Systems.

https://nvidianews.nvidia.com/news/nvidia-launches-edge-computing-platform-to-bring-real-time-ai-to-global-industries

Monday, March 18, 2019

AWS to offer NVIDIA T4 Tensor Core GPUs

Amazon Web Services will begin offering NVIDIA T4 Tensor Core GPUs as part of its Amazon Elastic Compute Cloud (EC2) G4 instances.

The new G4 instances are aimed at AI services. Through AWS Marketplace, customers will be able to pair the G4 instances with NVIDIA GPU acceleration software, including NVIDIA CUDA-X AI libraries for accelerating deep learning, machine learning and data analytics.

T4 will also be supported by Amazon Elastic Container Service for Kubernetes, making it possible to deploy, manage and scale containerized applications on EC2 G4 GPU instances using Kubernetes.

“NVIDIA and AWS have worked together for a long time to help customers run compute-intensive AI workloads in the cloud and create incredible new AI solutions,” said Matt Garman, vice president of Compute Services at AWS. “With our new T4-based G4 instances, we’re making it even easier and more cost-effective for customers to accelerate their machine learning inference and graphics-intensive applications.”

T4 will join other Amazon EC2 instances featuring NVIDIA GPUs, providing developers and data scientists with the most sophisticated compute resources available to support a variety of customer needs.

Monday, March 11, 2019

With Mellanox, NVIDIA targets full compute/network/storage stack

NVIDIA agreed to acquire Mellanox in a deal valued at approximately $6.9 billion.

The merger targets data centers in general and the high-performance computing (HPC) market in particular. Together, NVIDIA’s computing platform and Mellanox’s interconnects power over 250 of the world’s TOP500 supercomputers and have as customers every major cloud service provider and computer maker. Mellanox pioneered the InfiniBand interconnect technology, which along with its high-speed Ethernet products is now used in over half of the world’s fastest supercomputers and in many leading hyperscale datacenters.

NVIDIA said the acquired assets enables it to data center-scale workloads across the entire computing, networking and storage stack to achieve higher performance, greater utilization and lower operating cost for customers.

“The emergence of AI and data science, as well as billions of simultaneous computer users, is fueling skyrocketing demand on the world’s datacenters,” said Jensen Huang, founder and CEO of NVIDIA. “Addressing this demand will require holistic architectures that connect vast numbers of fast computing nodes over intelligent networking fabrics to form a giant datacenter-scale compute engine.

“We share the same vision for accelerated computing as NVIDIA,” said Eyal Waldman, founder and CEO of Mellanox. “Combining our two companies comes as a natural extension of our longstanding partnership and is a great fit given our common performance-driven cultures. This combination will foster the creation of powerful technology and fantastic opportunities for our people.”

NVIDIA also promised to continue investing in Israel, where Mellanox is based.

The companies expect to close the deal by the end of 2019.




Thursday, February 14, 2019

NVIDIA expects return to sustained growth

NVIDIA reported revenue of $2.21 billion for its fourth quarter ended Jan. 27, 2019, down 24 percent from $2.91 billion a year earlier, and down 31 percent from $3.18 billion in the previous quarter. GAAP earnings per diluted share for the quarter were $0.92, down 48 percent from $1.78 a year ago and down 53 percent from $1.97 in the previous quarter. Non-GAAP earnings per diluted share were $0.80, down 53 percent from $1.72 a year earlier and down 57 percent from $1.84 in the previous quarter.

For fiscal 2019, revenue was $11.72 billion, up 21 percent from $9.71 billion a year earlier. GAAP earnings per diluted share were $6.63, up 38 percent from $4.82 a year earlier. Non-GAAP earnings per diluted share were $6.64, up 35 percent from $4.92 a year earlier.

“This was a turbulent close to what had been a great year,” said Jensen Huang, founder and CEO of NVIDIA. “The combination of post-crypto excess channel inventory and recent deteriorating end-market conditions drove a disappointing quarter."

“Despite this setback, NVIDIA’s fundamental position and the markets we serve are strong. The accelerated computing platform we pioneered is central to some of world’s most important and fastest growing industries – from artificial intelligence to autonomous vehicles to robotics. We fully expect to return to sustained growth."

Tuesday, January 29, 2019

Microsoft Azure adds Nvidia Quadro Virtual Workstation

Microsoft Azure will begin offering NVIDIA Tesla GPU-accelerated Quadro Virtual Workstation (Quadro vWS). Customers can spin up a GPU-accelerated virtual workstation in minutes from the Azure marketplace without having to manage endpoints or back-end infrastructure.

"We’re focused on delivering the best and broadest range of GPU-accelerated capabilities in the public cloud,” said Talal Alqinawi, senior director of Microsoft Azure at Microsoft Corp. “NVIDIA Quadro vWS expands customer choice of GPU offerings on Azure to bring powerful professional workstations in the cloud to meet the needs of the most demanding applications from any device, anywhere."

Monday, January 28, 2019

NVIDIA trims guidance citing weakness in gaming and data centers

NVIDIA now estimates its Q4 revenue to be $2.20 billion compared to previous guidance of $2.70 billion. The company cited weakness in gaming and data center segments, as well as "deteriorating macroeconomic conditions, particularly in China."

In gaming, NVIDIA said there was a sequential decline due to excess mid-range channel inventory following the crypto-currency boom that proceeded as expected. However, consumer demand for NVIDIA gaming GPUs weakened faster than expected and sales of certain high-end GPUs using NVIDIA’s new Turing architecture were lower than expected. 

In the data center segment, revenue also came in short of expectations. A number of deals in the company’s forecast did not close in the last month of the quarter as customers shifted to a more cautious approach.

“Q4 was an extraordinary, unusually turbulent, and disappointing quarter,” said Jensen Huang, founder and CEO of NVIDIA. “Looking forward, we are confident in our strategies and growth drivers. The foundation of our business is strong and more evident than ever – the accelerated computing model NVIDIA pioneered is the best path forward to serve the world’s insatiable computing needs.  The markets we are creating – gaming, design, HPC, AI and autonomous vehicles – are important, growing and will be very large. We have excellent strategic positions in all of them.”

Thursday, August 16, 2018

NVIDIA posts 40% sales growth in Q2 but warns on crypto sales

NVIDIA reported revenue of $3.12 billion for its second quarter ended July 29, 2018, up 40 percent from $2.23 billion a year earlier, and down 3 percent from $3.21 billion in the previous quarter.
GAAP earnings per diluted share for the quarter were $1.76, up 91 percent from $0.92 a year ago and down 11 percent from $1.98 in the previous quarter. Non-GAAP earnings per diluted share were $1.94, up 92 percent from $1.01 a year earlier and down 5 percent from $2.05 in the previous quarter.

“Growth across every platform - AI, Gaming, Professional Visualization, self-driving cars - drove another great quarter,” said Jensen Huang, founder and CEO of NVIDIA. “Fueling our growth is the widening gap between demand for computing across every industry and the limits reached by traditional computing. Developers are jumping on the GPU-accelerated computing model that we pioneered for the boost they need.

However, Colette Kress, NVIDIA's CFO, warned that boom in sales for crypto mining applications is over. In a prepared statement on the company's investor call she said "“Our revenue outlook had anticipated cryptocurrency-specific products declining to approximately $100 million, while actual crypto-specific product revenue was $18 million.”

For its data centers category, quarterly sales amounted to $760 million, driven by demand from hyperscale customers.

Monday, August 13, 2018

Samsung supplies 16Gb GDDR6 Memory for NVIDIA Quadro

Samsung Electronics has supplied its latest 16-gigabit (Gb) Graphics Double Data Rate 6 (GDDR6) memory for NVIDIA’s new Turing architecture-based Quadro RTX™ GPUs.

Samsung's 16Gb GDDR6, which doubles the device capacity of the company’s 20-nanometer 8Gb GDDR5 memory. The new solution performs at a 14 Gbps pin speed with data transfers of 56 gigabytes per second (GB/s), which represents a 75 percent increase over 8Gb GDDR5 with its 8Gbps pin speed.

Samsung says its GDDR6 consumes 35 percent less power than that required by the leading GDDR5 graphics solutions.

"It’s a tremendous privilege to have been selected by NVIDIA to launch Samsung’s 16Gb GDDR6, and to have enjoyed the full confidence of their design team in making our key contribution to the NVIDIA Quadro RTX GPUs," said Jim Elliott, Corporate Senior Vice President at Samsung Semiconductor, Inc.

Monday, August 6, 2018

Google Cloud expands its virtual workstations with NVIDIA Tesla GPUs

Google Cloud Platform began offering virtual workstations optimized for graphics-intensive applications and machine learning inference based on the NVIDIA Tesla P4 GPU.

The new support enables users to turn any instance with one or more GPUs into a high-end workstation. P4s offer 8GB of GDDR5 memory

https://cloud.google.com/


Tuesday, May 29, 2018

Supermicro unveils 2 PetaFLOP SuperServer based on New NVIDIA HGX-2

Super Micro Computer is using the new NVIDIA HGX-2 cloud server platform to develop a 2 PetaFLOP "SuperServer" aimed at artificial intelligence (AI) and high-performance computing (HPC) applications.

"To help address the rapidly expanding size of AI models that sometimes require weeks to train, Supermicro is developing cloud servers based on the HGX-2 platform that will deliver more than double the performance," said Charles Liang, president and CEO of Supermicro. "The HGX-2 system will enable efficient training of complex models. It combines 16 Tesla V100 32GB SXM3 GPUs connected via NVLink and NVSwitch to work as a unified 2 PetaFlop accelerator with half a terabyte of aggregate memory to deliver unmatched compute power."

The design packs over 80,000 CUDA cores.

Monday, April 2, 2018

Mellanox interconnects NVIDIA's new DGX-2 AI box

Mellanox Technologies confirmed that its InfiniBand and Ethernet are used in the new NVIDIA DGX-2 artificial intelligence (AI) system.

NVIDIA's DGX-2, which delivers 2 Petaflops of system performance, is powered by sixteen GPUs and eight Mellanox ConnectX adapters, supporting both EDR InfiniBand and 100 GigE connectivity.

The embedded Mellanox network adapters provide overall 1600 gigabit per second bi-directional data throughout, which enables scaling up AI capabilities for building the largest Deep Learning compute systems.

"We are excited to collaborate with NVIDIA and to bring the performance advantages of our EDR InfiniBand and 100 gigabit Ethernet to the new DGX-2 Artificial Intelligence platform," said Gilad Shainer, vice president of marketing at Mellanox Technologies. "Doubling the network throughput as compared to previous systems to provide overall bi-directional 1600 gigabit per second data speed enables the DGX-2 platform to analyze growing amounts of data, and to dramatically improve Deep Learning application performance."

Tuesday, March 27, 2018

NVIDIA brings 10x performance for Deep Learning System

At its annual GTC conference in San Jose, NVIDIA introduced its 2 petaflop, DGX-2 Deep Learning System, promising a 10x performance boost on deep learning workloads compared with the previous generation from six months ago.

Key advancements in NVIDIA platform include a 2x memory boost to NVIDIA Tesla V100 datacenter GPU, and a revolutionary new GPU interconnect fabric called NVIDIA NVSwitch, which enables up to 16 Tesla V100 GPUs to simultaneously communicate at a record speed of 2.4 terabytes per second. NVIDIA also introduced an updated, fully optimized software stack.

These advancements enable the NVIDIA DGX-2 server to deliver two petaflops of computational power -- the equivalent of 300 servers occupying 15 racks of datacenter space, while being 60x smaller and 18x more power efficient.

“The extraordinary advances of deep learning only hint at what is still to come,” said Jensen Huang, NVIDIA founder and CEO, as he unveiled the news at GTC 2018. “Many of these advances stand on NVIDIA’s deep learning platform, which has quickly become the world’s standard. We are dramatically enhancing our platform’s performance at a pace far exceeding Moore’s law, enabling breakthroughs that will help revolutionize healthcare, transportation, science exploration and countless other areas.”

Wednesday, January 24, 2018

Silicon wars heat up in 2018 – the autonomous vehicle opportunity

Preamble: autonomous vehicles represent an enormous opportunity for the tech industry, including for mobile operators and network equipment suppliers. The first and second parts of this article looked at recent developments at Intel and at Qualcomm, both of which are moving rapidly to consolidate an early lead into a full-fledged platform for autonomous vehicles. This part of the article looks at two other players with newly-announced platforms: NVIDIA and Molex.

NVIDIA building the world's first autonomous machine processor

NVIDIA is pursuing a “holistic” strategy for the autonomous vehicle challenge, choosing to develop silicon, the software stack, the tools, and the development necessary to achieve driverless safety at the ISO 26262 certification level.

At this year’s CES 2018, the company unveiled its NVIDIA AI autonomous vehicle platform for automakers. At the heart of the system is a new NVIDIA Xavier autonomous-machine processor, which the company describes as the most complex system on a chip ever created. The chip, which is expected to begin sampling this quarter, is built around a custom 8-core CPU, a new 512-core Volta GPU, a new deep learning accelerator, new computer vision accelerators and new 8K HDR video processors. The SoC has over 9 billion transistors. Everything on-chip is designed for redundacy and diversity. NVIDIA said it invested $2 billion over four years to develop the chip. Over 2,000 engineers worked on its development.

NVIDIA is not just pitching silicon, but instead talking about process, technologies, and simulation systems, as described below:

Process: Sets out the steps for establishing a pervasive safety methodology for the design, management, and documentation of the self-driving system.

Processor Design and Hardware Functionality: Incorporates a diversity of processors to achieve

fail operation capabilities. These include NVIDIA-designed IP related to NVIDIA Xavier covering CPU and GPU processors, deep learning accelerator, image processing ISP, computer vision PVA, and video processors – all at the highest quality and safety standards. Included are lockstep processing and error-correcting code on memory and buses, with built-in testing capabilities. The ASIL-C NVIDIA DRIVE Xavier processor and ASIL-D rated safety microcontroller with appropriate safety logic can achieve the highest system ASIL-D rating.

Software: including third-party software such as BlackBerry QNX’s 64-bit real-time operating system, which is ASIL-D safety certified, along with TTTech’s MotionWise safety application framework, which encapsulates each application and isolates them from each other, while providing real-time computing capability. NVIDIA DRIVE OS offers full support of Adaptive AUTOSAR, the open-standard automotive system architecture and application framework. The NVIDIA toolchain, including the CUDA compiler and TensorRT, uses ISO 26262 Tool Classification Levels.

Algorithms: The NVIDIA DRIVE AV autonomous vehicle software stack performs functions like ego-motion, perception, localization, and path planning. To realize fail operation capability, each functionality includes a redundancy and diversity strategy. For example, perception redundancy is achieved by fusing lidar, camera and radar. Deep learning and computer vision algorithms running on CPU, CUDA GPU, DLA and PVA enhance redundancy and diversity. The NVIDIA DRIVE AV stack is a full backup system to the self-driving stack developed by the automaker, enabling Level 5 autonomous vehicles to achieve the highest level of functional safety.

Virtual Reality Simulation: NVIDIA has created a virtual reality simulator, called NVIDIA AutoSIM, to test the DRIVE platform and simulate against rare conditions. Running on NVIDIA DGX supercomputers, NVIDIA AutoSIM is repeatable for regression testing and will eventually simulate billions of miles.

Based on this platform, NVIDIA published a flurry of press announcements touting its momentum:

Mercedes-Benz unveiled a new in-car infotainment system that uses AI powered by NVIDIA to transform how drivers and passengers interact with their vehicles. The 3D touch-screen displays can be controlled with a new voice-activated assistant that can be summoned with the phrase “Hey, Mercedes.”

Volkswagen is adopting the NVIDIA DRIVE IX platform.

Uber has selected NVIDIA technology for the AI computing system in its future fleet of self-driving cars and freight trucks.
Baidu and ZF, one of the world’s largest automotive suppliers, to create a production-ready AI autonomous vehicle platform based on NVIDIA’s DRIVE Xavier, ZF’s new ProAI car computer and Baidu’s Apollo Pilot.

Molex is building the in-vehicle network

Molex, which is well-known in the communications field for its electrical and fibre optic interconnection systems, is also jumping into to the autonomous vehicle field.  This week, the Lisle, Illinois-based company is highlighting its new, 10G Automotive Ethernet Network for connected and autonomous vehicles at CES 2018.

The Molex 10 Gbps Automotive Ethernet Network connects Electronic Control Units (ECUs) throughout a vehicle. It offers secure over-the-air software and firmware updates and diagnostics over IP (Dip) to help avoid the need for vehicle recalls and enabling in-vehicle security and diagnostics over IP.  Molex said its platform is compatible with existing network components, and that it provides flexibility for OEMs to accommodate different vehicle profiles.

The Molex 10 Gbps Automotive Ethernet Network incorporates an Aquantia chip optimized for Multi-Gig Ethernet to support data transfers between Electronic Control Units (ECU). Molex is also working with Silicon Valley-based Excelfore, which provides innovative middleware solutions for in-vehicle and vehicle-to-cloud smart mobility networks. This enables over-the-air (OTA) diagnostics, firmware and software updates to different automotive devices, from different vendors, running different operating systems, across multiple networks.

To connect the network to the car’s entertainment system, Molex has formed a partnership with AllGo Systems. AllGo's OTG and Media Solutions support iPhones and Android phones, as well as other smart devices within the car. The idea here is clearly wired and wireless infotainment in automotive cockpit systems. High-resolution navigation data could also be streamed over the in-car network from a head unit running Android to a digital instrument cluster running QNX. The companies envision multiple 4K high-resolution content streams from a network storage device to the head unit and played back on secondary displays.

Molex is also working with Microchip Technology Inc.  on USB Media Modules and USB power delivery solutions for these automotive infotainment systems. The work focuses on the increasing number of USB ports in vehicles, and how USB can deliver more power and bring driver assistance applications to the head unit display.

Finally, let us not forget security. Molex is working with BlackBerry to protect its 10 Gbps Ethernet Automotive Networking platform. This is being developed using the BlackBerry QNX Neutrino SDP 7.0 RTOS, which provides high performance and enhanced kernel-level security based on its microkernel architecture, file encryption, adaptive time partitioning, a high-availability framework, anomaly detection, and multi-level policy-based access control. Communication between modules and other vehicle ECUs and peripheral devices connected to the network will use the BlackBerry Certicom's Managed PKI (Public Key Infrastructure) Service to securely provision and authenticate. In-vehicle connections can be made via Ethernet IP-based devices or LIN, CAN, USB, and other supported legacy communication protocols. As part of the PKI, BlackBerry Certicom’s is providing an efficient and powerful Elliptic-Curve Cryptography (ECC) solution that can also be extended to communications between the vehicle systems and the cloud.

Sunday, November 26, 2017

NVIDIA extends AI healthcare partnership with GE

NVIDIA is working with GE to bring the most sophisticated artificial intelligence (AI) to GE Healthcare’s 500,000 imaging devices globally.

The partnership, which was detailed at the 103rd annual meeting of the Radiological Society of North America (RSNA), includes a new NVIDIA-powered Revolution Frontier CT, advancements to the Vivid E95 4D Ultrasound and development of GE Healthcare’s Applied Intelligence analytics platform.

NVIDIA said its AI computing platform accelerates image processing in the new CT system by 2X.

NVIDIA notes that hhe average hospital generates 50 petabytes of data annually, through medical images, clinical charts and sensors, as well as operational and financial sources, providing many opportunities to accelerate data processing flows.

Wednesday, September 27, 2017

NVIDIA secures server design wins with leading manufacturers

NVIDIA has secured design wins for its Volta architecture-based Tesla V100 GPU accelerators with the leading server manufacturers, including Dell EMC, Hewlett Packard Enterprise, IBM, Supermicro, Inspur, Lenovo and Huawei.

Each NVIDIA V100 GPU features over 21 billion transistors, as well as 640 Tensor Cores, the latest NVLink high-speed interconnect technology, and 900 GB/sec HBM2 DRAM to achieve 50 percent more memory bandwidth than previous generation GPUs. NVIDIA says this enables 120 teraflops of deep learning performance.

V100-based systems announced include:

  • Dell EMC -- The PowerEdge R740 supporting up to three V100 GPUs for PCIe, the PowerEdge R740XD supporting up to three V100 GPUs for PCIe, and the PowerEdge C4130 supporting up to four V100 GPUs for PCIe or four V100 GPUs for NVIDIA NVLink™ interconnect technology in an SXM2 form factor.
  • HPE -- HPE Apollo 6500 supporting up to eight V100 GPUs for PCIe and HPE ProLiant DL380 systems supporting up to three V100 GPUs for PCIe.
  • IBM -- The next generation of IBM Power Systems servers based on the POWER9 processor will incorporate multiple V100 GPUs and take advantage of the latest generation NVLink interconnect technology -- featuring fast GPU-to-GPU interconnects and an industry-unique OpenPOWER CPU-to-GPU design for maximum throughput.
  • Supermicro -- Products supporting the new Volta GPUs include a 7048GR-TR workstation for all-around high-performance GPU computing, 4028GR-TXRT, 4028GR-TRT and 4028GR-TR2 servers designed to handle the most demanding deep learning applications, and 1028GQ-TRT servers built for applications such as advanced analytics.

Tuesday, September 26, 2017

NVIDIA sees big wins for data center GPUs in China

NVIDIA announced some big wins in China for its Volta GPUs - Alibaba Cloud, Baidu and Tencent are all incorporating the NVIDIA Tesla V100 GPU accelerators into their data centers and cloud-service infrastructures.
Specifically, the three cloud giants are shifting from NVIDIA Pascal architecture-based systems to Volta-based platforms, which offer performance gains for AI inferencing and training.

The NVIDIA V100 data center GPU packs 21 billion transistors and provides a 5x improvement over the preceding NVIDIA Pascal architecture P100 GPU accelerators.


NVIDIA also announced that China's leading original equipment manufacturers -- including Inspur, Lenovo and Huawei -- are using the NVIDIA HGX reference architecture to offer Volta-based accelerated systems for hyperscale data centers.

Saturday, August 19, 2017

NVIDIA adds virtualization software for GPU-accelerated servers

NVIDIA introduced new virtualization software capabilities for NVIDIA Tesla GPU-accelerated servers.

The company said its new Quadro Virtual Data Center Workstation Software (Quadro vDWS) enables enterprises to run both virtualized graphics and compute workloads on any virtual workstation or laptop from NVIDIA Tesla-accelerated data centers. This provides high-end performance to multiple enterprise users from the same GPU for lower cost of ownership.

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

  • The ability to create complex 3D and photoreal 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 visualization 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."

htttp://www.nvidia.com

Friday, August 11, 2017

NVIDIA Cits Growth in Data center, Auto

NVIDIA reported record revenue for its second quarter ended July 30, 2017, of $2.23 billion, up 56 percent from $1.43 billion a year earlier, and up 15 percent from $1.94 billion in the previous quarter. GAAP EPS was $0.92, up 124 percent from a year ago.
"Adoption of NVIDIA GPU computing is accelerating, driving growth across our businesses," said Jensen Huang, founder and chief executive officer of NVIDIA. "Datacenter revenue increased more than two and a half times. A growing number of car and robot-taxi companies are choosing our DRIVE PX self-driving computing platform. And in Gaming, increasingly the world's most popular form of entertainment, we power the fastest growing platforms - GeForce and Nintendo Switch.

"Nearly every industry and company is awakening to the power of AI. Our new Volta GPU, the most complex processor ever built, delivers a 100-fold speedup for deep learning beyond our best GPU of four years ago. This quarter, we shipped Volta in volume to leading AI customers. This is the era of AI, and the NVIDIA GPU has become its brain. We have incredible opportunities ahead of us," he said.

http://investor.nvidia.com/results.cfm


NVIDIA Debuts Latest Quadro Pascal GPUs


NVIDIA introduced its latest line-up of Quadro GPUs products, all based on its Pascal architecture and designed for professional workflows in engineering, deep learning, VR, and many vertical applications. "Professional workflows are now infused with artificial intelligence, virtual reality and photorealism, creating new challenges for our most demanding users," said Bob Pette, vice president of Professional Visualization at NVIDIA. "Our new Quadro...





NVIDIA Advances its Pascal-based GPUs for AI


NVIDIA is expanding its Pascal™ architecture-based deep learning platform with the introduction of new Tesla P4 and P40 GPU accelerators and new software. The solution is aimed at accelerating inferencing production workloads for artificial intelligence services, such as voice-activated assistance, email spam filters, and movie and product recommendation engines. NVIDIA said its GPU are better at these tasks than current CPU-based technology, which...


Sunday, February 5, 2017

NVIDIA Debuts Latest Quadro Pascal GPUs

NVIDIA introduced its latest line-up of Quadro GPUs products, all based on its Pascal architecture and designed for professional workflows in engineering, deep learning, VR, and many vertical applications.

"Professional workflows are now infused with artificial intelligence, virtual reality and photorealism, creating new challenges for our most demanding users," said Bob Pette, vice president of Professional Visualization at NVIDIA. "Our new Quadro lineup provides the graphics and compute performance required to address these challenges. And, by unifying compute and design, the Quadro GP100 transforms the average desktop workstation with the power of a supercomputer."

Some highlight of the new generation of Quadro Pascal-based GPUs:

  • the GP100 combines double precision performance with 16GB of high-bandwidth memory (HBM2) so users can conduct simulations during the design process and gather realistic multiphysics simulations faster than ever before. Customers can combine two GP100 GPUs with NVLink technology and scale to 32GB of HBM2 to create a massive visual computing solution on a single workstation.
  • the GP100 provides more than 20 TFLOPS of 16-bit floating point precision computing, making it an ideal development platform to enable deep learning in Windows and Linux environments.
  • the "VR Ready" Quadro GP100 and P4000 have the power to create detailed, lifelike, immersive environments. Larger, more complex designs can be experienced at scale.
  • Pascal-based Quadro GPUs can render photorealistic images more than 18 times faster than a CPU.
  • Visualize data in high resolution and HDR color on up to four 5K displays.
  • for digital signage, up to 32 4K displays can be configured through a single chassis by combining up to eight P4000 GPUs and two Quadro Sync II cards.

The new cards complete the entire NVIDIA Quadro Pascal lineup including the previously announced P6000, P5000 and mobile GPUs. The entire NVIDIA Quadro Pascal lineup supports the latest NVIDIA CUDA 8 compute platform providing developers access to powerful new Pascal features in developer tools, performance enhancements and new libraries including nvGraph.

http://www.nvidia.com

Tuesday, September 13, 2016

NVIDIA Advances its Pascal-based GPUs for AI

NVIDIA is expanding its Pascal™ architecture-based deep learning platform with the introduction of new Tesla P4 and P40 GPU accelerators and new software. The solution is aimed at accelerating inferencing production workloads for artificial intelligence services, such as voice-activated assistance, email spam filters, and movie and product recommendation engines.

NVIDIA said its GPU are better at these tasks than current CPU-based technology, which isn't capable of delivering real-time responsiveness. The Tesla P4 and P40 are specifically designed for inferencing, which uses trained deep neural networks to recognize speech, images or text in response to queries from users and devices.

Based on the Pascal architecture, these GPUs feature specialized inference instructions based on 8-bit (INT8) operations, delivering 45x faster response than CPUs and a 4x improvement over GPU solutions launched less than a year ago.

With 47 tera-operations per second (TOPS) of inference performance with INT8 instructions, a server with eight Tesla P40 accelerators can replace the performance of more than 140 CPU servers.5 At approximately $5,000 per CPU server, this results in savings of more than $650,000 in server acquisition cost.

"With the Tesla P100 and now Tesla P4 and P40, NVIDIA offers the only end-to-end deep learning platform for the data center, unlocking the enormous power of AI for a broad range of industries," said Ian Buck, general manager of accelerated computing at NVIDIA. "They slash training time from days to hours. They enable insight to be extracted instantly. And they produce real-time responses for consumers from AI-powered services."

http://nvidianews.nvidia.com

See also