Dell, EMC, Dell Technologies, Cisco,

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

Tuesday, July 24, 2018

DARPA Effort Pushes Beyond Moore’s Law

Responding to market forces that are driving the otherwise vibrant global electronics industry toward narrower computing applications and away from the next wave of innovation, the Defense Advanced Research Projects Agency (DARPA) is pouring $1.5 billion into “foundational enhancements” designed to jumpstart the post-Moore’s Law electronics industry. The five-year roadmap dubbed Electronic Resurgence Initiative (ERI) hits the ground running this week with a summit in San Francisco focusing on what program managers identify as ERI’s three pillars: new chip architectures, IC design and materials and integration. “What we are trying to do are foundational enhancements that are beyond what any one corporate partner would want to do or try to do on their own,” said William Chappell, director of DARPA’s Microsystems Technology Office. “The double-edged sword of Moore’s Law is that while the train is kept moving, the costs across the board from fab to design to verification have all gone up precipitously and even exponentially,” Chappell added. “What we are trying to do is look at other avenues, for example in fabrication, where you can get benefit through either novel materials or novel integration processes that don’t rely just on the scaling of transistors.” DARPA also announced a list of university and corporate research efforts aim at advancing its “three-pillar” chip strategy. For example, ERI places a large bet on maintaining U.S. and European leadership in chip design and the valuable intellectual property it generates. “We need to make sure that we have new inventions occurring across our country and other allied countries where we invent new processes as older processes are being replicated en masse.” While chip maker Intel Corp. and challengers such as GPU specialist Nvidia Corp. and ARM increasingly focus on specific applications, many driven by AI and other emerging machine learning applications, DARPA is attempting to reboot the semiconductor R&D cycle in response to huge global chip investments. Chappell specifically cited China’s estimated $150 billion investment in an indigenous chip industry. That initiative would underpin broader efforts, including Beijing’s national strategy to become the world leader in AI by 2030. The technological battleground in this global competition, DARPA reckons, revolves around new materials, automating the design of new systems-on-chips (SoCs) along with novel architectures that could, for example, be mixed and match as building blocks in larger systems. One effort, dubbed Intelligent Design of Electronic Assets, or IDEA, seeks to develop a platform supporting an automated design flow for SoCs along with the chip packages and interconnects to larger systems. These future chip design flows would leverage machine learning, analytics and automation technologies used to verify chip designs before they are mass produced. Contactors for the IDEA program include Cadence Design Systems, Nvidia and Carnegie Mellon University. ERI’s chip architecture research focuses on reconfigurable frameworks that leverage specialized hardware to tackle specific computing problems. Contractors for the Software Defined Hardware (SDH) initiative include Intel, Nvidia and top U.S. engineering schools. The hardware effort along with a separate “domain-specific” SoC effort were launched last fall. Meanwhile, Nvidia will lead a team of industry and university researchers under the SDH program seeking performance gains akin to today’s ASICs “without sacrificing programmability for data-intensive algorithms,” the company said Tuesday (July 24). Among the goals of chip architecture effort is bringing an open-source software approach to hardware development. Among the applications are machine vison and machine learning, Chappell added. The semiconductor materials and circuit integration theme addresses looming performance issues such as “memory bottlenecks” that plague current big data applications. One task is finding new ways to combine dissimilar blocks of chip intellectual property, many based on different materials and circuit designs. The initiative that includes researchers from MIT, Stanford and other leading engineering schools would focus on both emerging 3-D SoC designs as well as future approaches under the rubric, Foundations for Novel Compute, or FRANC, program. Among the contractors for the 3-D chip effort is Skywater Technology Foundry, a pure-play 200-mm chip fab in Minnesota with roots dating back to Control Data Corp. and, more recently, Cypress Semiconductor. Ultimately, DARPA’s Chappell said, the chip initiative is designed “to see what the art of the possible is.” He added: “It’s more important than ever that we have new inventions coming out of the pipeline to make sure the semiconductor space does not become a commodity.”

https://www.hpcwire.com/2018/07/24/darpa-effort-pushes-beyond-moores-law/

Thursday, July 19, 2018

Pure Storage Announces New Converged Infrastructure Solutions to Accelerate Entire Spectrum of Analytics

MOUNTAIN VIEW, Calif., July 18, 2018 /PRNewswire/ --  @PureStorage (NYSE: PSTG), the all-flash storage platform that helps innovators build a better world with data, today announced #FlashStack™ with FlashBlade™ to accelerate data warehouses. The company also announced that AIRI™ and AIRI™ Mini, the #AI -ready infrastructure solutions from Pure Storage and @NVIDIA, are now available with @Cisco Nexus ethernet switches. Cisco and Pure Storage customers can now establish a spectrum of data analytics to turn data into innovation, no matter what phase in their data journey.   In time, more data will live in "hot" or "warm" tiers, where value can easily be extracted. With this shift, organizations must leverage the latest tools of extraction, and the storage models that allow these tools to work most effectively and efficiently. "Data is the lifeblood of today's economy and the fuel for innovation. New cures, new industries, new possibilities will arise – all because of data. This new era demands a new data mindset and strategy," said Michael Sotnick, VP Partners, Services & Business Development, Pure Storage. "For organizations to stand the test of time, they must understand how to innovate and differentiate through data." AI-Ready-Infrastructure   Data is the fuel driving today's AI revolution. While many pioneers have pushed the limits of what's possible with AI within their own industries, the vast majority of enterprises have had to struggle to get started. Today, not all data's value is easily accessible. Without fast, easy access, data becomes an organization's most expensive asset rather than its most valuable. AI needs to reach more enterprises faster, and provide value immediately. Pure Storage is working with Cisco to make this reality for customers with AIRI, the industry's first solution to simplify AI-at-scale, powered by the NVIDIA DGX-1. "The days where value is measured in dollar per GB have passed. Value is now measured in simulations per day. The industry focuses on outcome orientation because simplicity, performance and resiliency afford us the time to do so," said Ed Horley, VP of Engineering at IT solutions provider Groupware Technology. "With AIRI, customers can begin their AI initiatives within hours and its simple scalable design allows it to grow with your AI needs. By eliminating these infrastructure complexities, customers can focus on what's important - creating innovative products and services." AIRI is the industry's first complete AI-ready infrastructure, architected by Pure Storage and NVIDIA to extend the power of NVIDIA DGX™ systems. Powered by FlashBlade storage and NVIDIA DGX-1 servers, AIRI offers enterprises a simple, fast, and future-proof infrastructure. It is configured with four NVIDIA DGX-1 Systems, four petaFLOPS of deep learning performance, Pure FlashBlade 15x 17TB Blades, 1.5M NFS IOPS, and two 100Gb switches with RDMA. AIRI Mini is powered by one Pure Storage FlashBlade, configured with 7x17TB blades, two NVIDIA DGX-1 servers, Cisco 100 GbE switches, supporting GPUDirect RDMA for maximum distributed training performance, and delivers two petaFLOPS of deep learning performance. All software comes included, so that data scientists can get started on their projects by simply turning the system on. FlashStack Powered by FlashBlade While some organizations are using Data Lakes or AI data pipelines, many organizations still deploy most of their mission critical work on a data warehouse. FlashStack powered by FlashBlade, purpose-built for Oracle Data Warehouse, is an integrated infrastructure solution validated by Cisco. "While AIRI is the perfect fit for organizations that need to deploy AI, there are many organizations just dipping their toes into single server solutions," said Matt Burr, GM of FlashBlade, Pure Storage. "For organizations that need an interim step between single-server and AIRI, FlashStack is the solution." There are three primary problems with existing data warehouse solutions: Performance – Data warehouse appliances historically have not kept up with data growth. Pricing – When compute and storage are coupled, as is with warehouse appliances, organizations end up buying too much of one or the other. Flexibility – An inflexible appliance is only effective for a single workload. Data warehouses aren't protected for the future when data pipeline and AI are involved. The new FlashStack, built on FlashBlade and engineered specifically to modernize the Oracle Data Warehouse, delivers more than 2x faster performance than the next-best system. Customers only pay for the amount of compute and blades needed for their project or initiative. FlashStack for FlashBlade is ready for extension into modern analytics and AI when ready. AIRI and AIRI Mini are available now through select reseller partners. FlashStack for FlashBlade is available now through all FlashStack certified reseller partners. Please visit our website for more information.

https://www.prnewswire.com/news-releases/pure-storage-announces-new-converged-infrastructure-solutions-to-accelerate-entire-spectrum-of-analytics-300682743.html

Thursday, April 12, 2018

Report: 2 Major PC Makers Say No To Nvidia GeForce Partner Program

Since HardOCP unleashed its exclusive about @Nvidia 's troublesome #GeForce Partner Program (GPP), there's been a flurry of both visible and behind-the-scenes in the PC gaming industry. I detailed some of the ripples GPP caused with @Gigabyte and @MSI, meanwhile @HardOCP has a newer story about @ASUS and their new "AREZ" brand. Nvidia has remained disturbingly quiet about the transparent program, but journalist Kyle Bennett has kept ratting cages and has a fresh round of reveals. There's good news and also very troubling news.


Let's disclose this up front: the information below concerns @HP and @Dell. Neither of these companies have confirmed any of it officially. The revelations are based on off the record, backchannel discussions conducted by HardOCP's Kyle Bennett. He's been in the industry for 2 decades and has more trusted contacts than most, and he's been correct about many of the original GeForce Partner Program details. Still, it's always sensible to take unconfirmed reports with a grain of salt.

As you know, the GeForce Partner Program is an incentive created by Nvidia to offer its partners (graphics card companies like ASUS, MSI and Gigabyte) and OEMs (major PC makers like Dell and HP) early access to upcoming technology, robust promotional support and a host of other benefits. That's the official stance.
https://www.forbes.com/sites/jasonevangelho/2018/04/12/2-major-pc-makers-say-no-to-nvidia-geforce-partner-program/#51adfcf110cd

Pure Storage And NVIDIA Announce AIRI Converged Infrastructure Reference Architecture

Last month, I attended @NVIDIA ’s GTC 2018 conference, which has turned into one of the premier #AI, #ML and #DL conferences in the world. You can read my analysis here. Part of the reason for this reputation is NVIDIA’s leadership in machine and deep learning training which it helped to create and popularize using its highly programmable and performant GPUs.

With less fanfare and credit, Pure Systems has taken a similar approach to storage in AI, ML, and DL, and have brought out AI-optimized FlashBlade storage systems to take advantage of the parallelized AI workflows. An AI solution without the right storage is like a fiddler crab with one giant claw- it can only get around as fast as that one, giant claw. We have previously covered this potential imbalance in a blog and a paper that both talk about the importance of parallelized storage when it comes to parallelized AI workloads. @NVIDIA and @Pure Storage are now pulling their combined “super-powers” together to a create what we normally expect from @HPE, @DellEMC, @IBM, and @Lenovo- that is, a converged infrastructure platform, called “ #AIRI ”. AIRI (AI Ready Infrastructure) is an AI-optimized converged infrastructure reference design, available from Pure and NVIDIA partners and contains different combinations of Pure Storage FlashBlades, NVIDIA DGX-1s, and Arista switches. DeloitteVoice What Today's Business Leaders Can Learn From The 1953 Mt. Everest Conquest AIRI is targeted at enterprise, not CSPs, who design and then get ODMs to build their own systems. Holistically, enterprises have two choices to get into AI. They can choose on-prem systems like AIRI or from vendors like HPE, Dell EMC, IBM, and Lenovo or use AI cloud services from AWS, Azure, GCP or IBM Cloud or Watson. Enterprises do not want to fall behind like they did with the public cloud, inevitably costing them both time and efficiency. Amazon.com AWS had a seven-year public cloud head-start, and many enterprises were caught flat-footed with the private cloud. Therefore, enterprises are hoping to get a much quicker start on AI applications that help to address security, cost or latency problems, and should be local and not in the public cloud.

https://www.forbes.com/sites/patrickmoorhead/2018/04/10/pure-storage-and-nvidia-announce-airi-converged-infrastructure-reference-architecture/#77fd57ea373b

Wednesday, April 11, 2018

Advanced Scale Forum Announces Sponsorship from Dell EMC, HPE, Microsoft and Others

SAN DIEGO, Calif., April 10, 2018 — Advanced Scale Forum 2018, a power summit designed to solve challenges in the build-out of scalable advanced enterprise computing solutions, today announced this year’s sponsor lineup. The summit, produced by Tabor Communications (publisher of HPCwire, EnterpriseTech, Datanami) and nGage Events, is scheduled for May 6-8, 2018 at the Hyatt Regency Lost Pines Resort & Spa in Austin, Texas, and will focus on #AI, #deeplearning and #machinelearning, #Blockchain, advanced-scale #cloudcomputing, #IoTedge to #IoTcore and high performance computing #HPC. Advanced Scale Forum (#ASF) is proud to announce the following sponsorships with key technology providers, who will be conducting private, board room briefings to leading enterprise executives: @Accenture, @Dell EMC, @HPE, @Microsoft, @NVIDIA, @Quantum, @Attunity, @CB Technologies, @HDF, @WekaIO, @AMD, @Arcadia Data, @Cray, @E8 Storage, @Intel, @Lawrence Livermore National Laboratory, @MapR, @NetApp, @Nimbix, @Penguin Computing, @Six Nines, @Striim, @System Fabric Works, @Talking Data, @Data Vortex, @Memsql and @PSSC Labs. Through these sponsorships, ASF brings attendees face-to-face with advanced-scale IT’s leading solution providers as they share the case studies that have driven the most value for their customers. This diversity of vendor perspectives allows for full view of industry direction, including: strategy, consulting, digital technology and operations, scale-out tiered storage, cloud and hybrid environments, deep learning and modern AI, and more. “Convergence around the high end of computing, data analytics and AI is transforming the business computing landscape, where modern IT solutions are engines for innovation and growth,” said Tiffany Trader, Managing Editor of HPCwire and Director of Editorial, Tabor Communications. “Advanced Scale Forum brings you face-to-face with these emerging technologies as vehicles for driving real-world results. Through leadership keynotes, case studies, one-on-one meetings, and breakout sessions, this three-day summit provides a tailored experience like no other. The information gained over the course of the summit is essential for staying ahead in our industry.” Helping to drive the sponsor lineup has been the announcement of this 2018’s Summit Agenda, which features thought leaders and industry experts on hot-button topics from Blockchain, data governance, artificial intelligence and machine learning, and the first ever demonstration of a Gulfstream aircraft simulation outside of NDA-restricted sales meetings. “Advanced Scale Forum is a one-of-a-kind executive summit,” said Tom Tabor, CEO of Tabor Communications. “Nowhere in the world is there an event that’s exclusive to leaders from the industrial end-user and vendor communities where mission-critical, production-level challenges are discussed with a focus of utilizing advanced-scale computing technologies as a cornerstone of the solution. “Each year we are honored to bring more vendors, thought leaders and decision makers to the table, and the activity surrounding our 2018 event has been unprecedented. We’re immensely proud of this year’s program as well as the breadth of in-depth sponsor participation, and cannot wait to share it with our attendees.”

Amazon Web Services brings machine learning to edge IoT

Public cloud provider @Amazon Web Services ( #AWS) has updated its edge computing platform, #Greengrass, to incorporate #machinelearning. The new offering brings together Internet of Things ( #IoT) capabilities, machine learning ( #ML), and edge computing to help organisations use AWS to build, train, and test ML models. These can then be deployed to small, low-powered, intermittently-connected IoT devices, including those running in factories, vehicles, fields, and homes. The latest version will be able to run #Apache #MXNet and TensorFlow Lite models locally on edge IoT devices using @NVIDIA #Jetson TX2 and @Intel #Atom architectures, according to @JeffBarr, AWS evangelist, in a blog post. The optimised libraries can take advantage of GPU and FPGA hardware accelerators at the edge in order to provide fast, local machine learning inferences. According to Barr, customers can also use Amazon SageMaker and other cloud-based ML tools to build, train, and test their models before deploying them to IoT devices. Users can also deploy 32-bit Raspberry Pi devices for local testing, he added. Use cases Barr said that Amazon has tossed the IoT, machine learning, and edge computing “into a blender” with the new offering. Among the potential use cases for Greengrass ML are precision farming, he said. Using the system, intelligent devices in the field could process images of soil, plants, pests, and crops, take local corrective action based on changing conditions, and send status reports to the cloud. The service could also be used for physical security, he added. Smart devices, such as Amazon’s deep-learning-enabled camera, DeepLens, could process images and scenes locally, looking for objects, watching for changes, and even detecting faces. “When something of interest or concern arises, the device can pass the image or the video to the cloud and use Amazon Rekognition to take a closer look,” explained Barr. The technology could also be used in industrial maintenance. Sensors would be able to run inference operations on power consumption, noise levels, and even vibration to flag anomalies, predict failures, and detect faulty equipment, he said. Internet of Business says With Dell, Microsoft, and a range of other vendors now focusing their IoT efforts on the critical edge environment and the distributed core, the race is on the make edge deployments faster, smarter, more intuitive – and cheaper – so that IoT projects can realise their full potential. In the long run, as the IoT rises, many organisations will find that their cloud investments will become more diffused. For many processing tasks, computing will need to be brought much closer to the point of delivery

Friday, April 6, 2018

Oculus Rift VR Gaming PC

I'm constantly looking for technical projects my son and I can enjoy working on together. As a father that spends most of his days time immersed in #datacenter technologies, and having a son that spends the majority of his free time playing video games, I thought it only fitting that with our forces combined we embark on the VR PC building journey. I spent time researching the differences between the @HTCVive and @Facebooks Oculus Rift #VR platforms. The choice for me to go with Oculus Rift was simple once reading about the Rift Core 2.0 update. Below is a list of hardware components purchased for my VR platform. I'd ask you to wish me luck but who needs luck when your prepared? 

“Luck Is What Happens When Preparation Meets  Opportunity". @Seneca

@AMD #Ryzen 7 1700
@MSI #X370 Gaming Pro Carbon
@Nvidia #GTX 1080Ti
@OculusRift
@Geil 16gb DDR4
@Nvidia 750 watt Gold PS
@Microsoft Windows 10 Home 64bit
@Samsung 850 #VNand EVO 500gb
@Samsung 960 #NVMe EVO M.2 250gb
@NZXT H440 Mid Tower


Have a great weekend. I know I will!


Tuesday, April 3, 2018

Secretive Chinese bitcoin mining company just revealed a new chip that could hurt AMD, Nvidia

One of Wall Street's biggest worries for @AMD and @Nvidia just became real. China-based @Bitmain on Tuesday revealed its specialized digital currency mining system for ethereum, which analysts have predicted could hurt demand for graphics cards.
"We are pleased to announce the Antminer E3, world's most powerful and efficient EtHash ASIC miner. Ordering limit of one miner per user and not available in China. Limited stock, order here now," Bitmain tweeted on social media Tuesday.

Wednesday, March 28, 2018

Supermicro's New Scale-Up Artificial Intelligence and Machine Learning Systems with 8 NVIDIA Tesla V100 with NVLink GPUs Deliver Superior Performance and System Density

SAN JOSE, Calif., March 27, 2018 /PRNewswire/ -- Super Micro Computer, Inc. (NASDAQ: SMCI), a global leader in enterprise computing, storage, networking solutions and green computing technology, today is showcasing the industry's broadest selection of GPU server platforms that support NVIDIA® Tesla® V100 PCI-E and V100 SXM2 GPU accelerators at the GPU Technology Conference in the San Jose McEnery Convention Center, booth 215, through March 29. @Supermicro offers best performing #GPU servers with #Tesla V100 PCI-E and V100 SXM2 32GB GPUs For maximum acceleration of highly parallel applications like #artificialintelligence ( #AI ), #deeplearning, #selfdriving cars, #smartcities, #healthcare, #bigdata, #HPC, #virtualreality and more, Supermicro's new 4U system with next-generation @NVIDIA #NVLink™ interconnect technology is optimized for maximum performance. SuperServer 4029GP-TVRT supports eight NVIDIA Tesla V100 32GB SXM2 GPU accelerators with maximum GPU-to-GPU bandwidth for cluster and hyper-scale applications. Incorporating the latest NVIDIA NVLink technology with over five times the bandwidth of PCI-E 3.0, this system features independent GPU and CPU thermal zones to ensure uncompromised performance and stability under the most demanding workloads. "On initial internal benchmark tests, our 4029GP-TVRT system was able to achieve 5,188 images per second on ResNet-50 and 3,709 images per second on InceptionV3 workloads," said Charles Liang, President and CEO of Supermicro.  "We also see very impressive, almost linear performance increases when scaling to multiple systems using GPU Direct RDMA. With our latest innovations incorporating the new NVIDIA V100 32GB PCI-E and V100 32GB SXM2 GPUs with 2X memory in performance-optimized 1U and 4U systems with next-generation NVLink, our customers can accelerate their applications and innovations to help solve the world's most complex and challenging problems." "Enterprise customers will benefit from a new level of computing efficiency with Supermicro's high-density servers optimized for NVIDIA Tesla V100 32GB data center GPUs," said Ian Buck, vice president and general manager of accelerated computing at NVIDIA. "Twice the memory with V100 drives up to 50 percent faster results on complex deep learning and scientific applications and improves developer productivity by reducing the need to optimize for memory." "At Preferred Networks, we continue to leverage Supermicro's high-performance 4U GPU servers to successfully power our private supercomputers," said Ryosuke Okuta, CTO of Preferred Networks. "These state-of-the-art systems are already powering our current supercomputer applications, and we have already begun the process of deploying Supermicro's optimized new 4U GPU systems loaded with NVIDIA Tesla V100 32GB GPUs to drive our upcoming new private supercomputers." Supermicro is also demonstrating the performance-optimized 4U SuperServer 4029GR-TRT2 system that can support up to 10 PCI-E NVIDIA Tesla V100 accelerators with Supermicro's innovative and GPU-optimized single root complex PCI-E design, which dramatically improves GPU peer-to-peer communication performance. For even greater density, the SuperServer 1029GQ-TRT supports up to four NVIDIA Tesla V100 PCI-E GPU accelerators in only 1U of rack space and the new SuperServer 1029GQ-TVRT supports four NVIDIA Tesla V100 SXM2 32GB GPU accelerators in 1U.   With the convergence of big data analytics and machine learning, the latest NVIDIA GPU architectures, and improved machine learning algorithms, deep learning applications require the processing power of multiple GPUs that must communicate efficiently and effectively to expand the GPU network. Supermicro's single-root GPU system allows multiple NVIDIA GPUs to communicate efficiently to minimize latency and maximize throughput as measured by the NCCL P2PBandwidthTest

NVIDIA to Unleash Deep Learning in Hyperscale Datacenters

@NVIDIA CEO unveils Volta-based GV 100 for workstations, new inferencing software, technologies providing a 10x boost for deep learning, a self-driving car simulator, and more. Millions of servers powering the world’s hyperscale data centers are about to get a lot smarter. NVIDIA CEO @JensenHuang Tuesday announced new technologies and partnerships that promise to slash the cost of delivering deep learning-powered services. Speaking at the kickoff of the company’s ninth annual GPU Technology Conference, Huang described a “Cambrian Explosion” of technologies driven by GPU-powered deep learning that are bringing support for new capabilities that go far beyond accelerating images and video. “In the future, starting with this generation, starting with today, we can now accelerate voice, speech, natural language understanding and recommender systems as well as images and video,” Huang, clad in his trademark leather jacket, told an audience of 8,500 technologists, business leaders, scientists, analysts and press gathered at the San Jose Convention Center. Over the course of a two-and-a-half hour keynote, Huang also unveiled a series of advances to NVIDIA’s deep learning computing platform that deliver a 10x performance boost on deep learning workloads from just six months ago; launched GV 100, transforming workstations with 118.5 TFLOPS of deep learning performance; introduced DRIVE Constellation to run self-driving car systems for billions of simulated miles. Power to the Pros Huang’s keynote got off to a brisk start, with the launch of the new Quadro GV 100. Based on Volta, the world’s most advanced GPU architecture, Quadro GV100 packs 7.4 TFLOPS double-precision, 14.8 TFLOPS single-precision and 118.5 TFLOPS deep learning performance, and is equipped with 32GB of high-bandwidth memory capacity. NVIDIA CEO Jensen Huang launches the Quadro GV100. GV100 sports a new interconnect called NVLink 2 that extends the programming and memory model out of our GPU to a second one. They essentially function as one GPU. These two combined have 10,000 CUDA cores, 236 teraflops of Tensor Cores, all used to revolutionize modern computer graphics, with 64GB of memory. Deep Learning’s Swift Rise The announcements come as deep learning gathers momentum. In less than a decade, the computing power of GPUs has grown 20x — representing growth of 1.7x per year, far outstripping Moore’s law, Huang said. “We are all in on deep learning, and this is the result,” Huang said. Drawn to that growing power, in just five years the number of GPU developers has risen 10x to 820,000. Downloads of CUDA, our parallel computing platform, have risen 5x to 8 million. “More data, more computing are compounding together into a double exponential for AI, that’s one of the reasons why it’s moving so fast” Huang said. Bringing Deep Learning Inferencing to Millions of Servers The next step: putting deep learning to work on a massive scale. To meet this challenge, technology will have to address seven challenges: programability, latency, accuracy, size, throughput, energy efficiency and rate of learning. Together, they form the acronym PLASTER.

Tuesday, March 27, 2018

New Amulet Hotkey CoreStation Solutions Simplify Windows Migration and Enhance Compute Performance

For the first time, powerful @NVIDIA #Tesla #datacenter GPUs can be used in the industry leading @DellEMC #PowerEdge FX architecture. The #AmuletHotkeyCoreStation #VFC640 GPU accelerated blade server provides market leading densities and efficiency. It is a powerful and agile platform that can handle a broad range of workloads. Support up to 128 graphics accelerated virtual desktops to simplify migration to Windows 10. Use with compute frameworks or #HPC applications to enhance compute performance.... Unique architecture: combines @Intel® Xeon® Scalable processors and NVIDIA® Tesla® data center GPUs with the efficiency of blades, and the cost benefits of rack-based systems, to handle today's evolving workloads. Market leading density: drives efficiency and performance with up to 128 GPU accelerated virtual desktops across four dual-socket servers in only 2U of rack space. Powerful performance: to handle graphics and GPU accelerated compute for virtual workstation, high-performance computing, big data analytics, machine learning and deep learning. NEW YORK, March 27, 2018 /PRNewswire/ - Amulet Hotkey Inc., a leader in design, manufacturing and system integration for remote physical and virtual workstation solutions, today announced a significant addition to the CoreStation blade family based on the Dell EMC PowerEdge FX architecture. For the first time, powerful NVIDIA Tesla data center GPUs can be used in the industry leading Dell EMC PowerEdge FX architecture. The Amulet Hotkey CoreStation VFC640 GPU accelerated blade server uses a unique PCIe expansion module and GPU card developed in collaboration with Dell EMC and NVIDIA product engineering teams. Two Amulet Hotkey DXF-EXP-V modules support up to eight NVIDIA Tesla P6 GPUs in the FX2 server chassis, while maintaining the benefits of the FC640 blades and FX2 architecture. The result is a powerful and agile platform that can handle a broad range of workloads. "The Amulet Hotkey CoreStation VFC640 expands upon the market leading blade workstation portfolio designed to meet the graphics and compute performance needs of professionals while driving customers' IT transformations," said Andrew Jackson, president, Amulet Hotkey Inc. "Our unique solution enables up to eight powerful GPUs, with up to four dual-socket servers in a PowerEdge FX2s chassis. Delivering this capability in an industry standard 2U rackmount form factor demonstrates our commitment to use innovative design and manufacturing to meet enterprise IT needs for a truly flexible and scalable computing architecture." "NVIDIA Tesla data center GPUs and NVIDIA virtual GPU software dramatically increase the graphics and compute performance of blade servers to handle evolving workflows with AI, photorealism and unlimited mobility," said Bob Pette, vice president, professional visualization, NVIDIA. "Customers working with large datasets, complex models or graphics-intensive productivity applications found in Windows 10 can leverage the benefits of high-density blades integrating multiple Tesla GPUs, NVIDIA virtualization software, Dell EMC OEM Solutions and Amulet Hotkey technology." "With the increasing adoption of Artificial Intelligence and Machine Learning, customers are looking for enhanced compute solutions that optimize these workloads," said Ron Pugh, vice President and general manager for the Americas, Dell EMC OEM Solutions. "We are excited to work with Amulet Hotkey to develop game changing solutions for our customers that accelerate IT transformation.

Monday, March 26, 2018

Teradata Joins NVIDIA Partner Program Focused on Accelerating Outcomes from AI, Deep Learning in Key Industries

SAN DIEGO, March 26, 2018 /PRNewswire/ -- @Teradata (NYSE: TDC), the leading cloud-based data and analytics company, today announced that its Think Big Analytics consulting group has joined the @NVIDIA Service Delivery Partner program. NVIDIA GPUs are widely used in deep learning, artificial intelligence ( #AI ) and accelerated analytics applications. As a program member, Think Big Analytics will help clients looking to scale their AI initiatives by working with NVIDIA to optimize deep learning for enterprise use and coordinating go-to-market strategies.   According to a 2017 survey of 260 large, global organizations conducted by Vanson Bourne on behalf of Teradata, the biggest challenges for implementing AI in enterprise environments are a lack of AI-ready IT infrastructure and a lack of talent. This partnership addresses both issues by bringing together two highly regarded services organizations with broad AI experience and a best-in-class AI computing platform. Think Big Analytics is one of the first large, global consultancies to join NVIDIA's partner network. With its exclusive focus on data and analytics, Teradata will be collaborating with NVIDIA on solutions that drive impactful business outcomes in industries including banking and insurance, automotive, manufacturing, telco, healthcare, retail, media and entertainment, and travel and transportation. Teradata's inclusion in the highly selective program was based on the company's extensive real-world experience implementing deep learning, via its AI Velocity services, much of which already included using NVIDIA GPUs. "We've seen the business value created by applying deep learning algorithms enabled by NVIDIA GPUs," said Atif Kureishy, Vice President of Emerging Practices at Think Big Analytics. "This partnership formalizes our use of NVIDIA-based technologies, giving our talented data scientists and engineers greater access to IP and expertise from NVIDIA, but also creating a positive feedback loop that will drive product innovation at both companies."

https://www.prnewswire.com/news-releases/teradata-joins-nvidia-partner-program-focused-on-accelerating-outcomes-from-ai-deep-learning-in-key-industries-300619067.html

Monday, February 26, 2018

Why overcrowding in tech stocks is still risky

Favoring large-cap technology stocks proved a winning strategy during the recent bout of volatility that sent the stock market into correction. But the risk of a crowded trade, one that could be ripe for reversal, in tech remains, according to analysts at RBC Capital Markets. The S&P 500 SPX, +1.18%  has recovered more than half of the losses from the correction earlier this month, when it dropped more than 10%. The benchmark index is up 3.4% since the start of the year, trading near 2,765. The much-beloved tech sector — up nearly 9% year to date — accounted for more than 60% of those S&P gains. Over the past 12 months, the S&P 500 is up 16.4%, while the tech sector gains are more than double that at 35%
In fact, mega-tech names including @Microsoft CorpMSFT, +1.45% @Apple Inc. AAPL, +1.98% @Netflix Inc.NFLX, +2.88% @NVIDIA Corp NVDA, +0.26%  and @Alphabet Inc. GOOG, +1.51%  contributed the lion’s share, according to Howard Silverblatt, senior index analyst at S&P Dow Jones Indexes.

@Amazon.com AMZN, +1.46% technically a consumer discretionary company but often lumped in with tech, gained nearly 30% since the start of the year and contributed nearly a fifth of all the gains on the S&P 500, according to Silverblatt. So-called FAANG stocks, including Amazon, all hit intraday records (though they did not close at all-time highs) on Friday, an event that many technical analysts see as a bullish signal.

Read: Big Tech shake-up — three stocks to buy and three to sell

Solid performance of the sector is one of the main reasons why these stocks continue to be popular with both the sell-side analysts and buy-side managers.

According to Lori Calvasina, head of U.S. equity strategy at RBC Capital Markets, sell-side analysts still overwhelmingly recommend buying tech stocks.

“Net ‘buy’ ratings (the percent of technology sector buy ratings less the percent of S&P 500 buy ratings) remain extremely high relative to history, though not quite back to tech bubble peaks,” Calvasina wrote in a note to investors.

https://www.marketwatch.com/story/why-overcrowding-in-tech-stocks-is-still-risky-2018-02-26

Sunday, February 25, 2018

Amazon and Alphabet Take on NVIDIA

@Amazon (NASDAQ: #AMZN) recently joined other tech giants such as @Apple (NASDAQ: #AAPL) and @Alphabet @Google (NASDAQ: #GOOG) (NASDAQ: #GOOGL) in designing its own custom chips. According to a report by The Information, Amazon has begun designing custom artificial intelligence ( #AI ) chips for its @Alexa-enabled home speakers. The report also said that Amazon may be looking to extend the chipmaking venture into products for its Amazon Web Services ( @AWS ) #datacenters. If that happens, it could potentially spell trouble for high-flying AI chipmakers NVIDIA (NASDAQ: NVDA) and Intel (NASDAQ: INTC). Here's how Amazon's ambitions could affect the AI race.  COULD AMAZON'S NEW ARTIFICIAL INTELLIGENCE CHIP THREATEN NVIDIA'S DATA CENTER GROWTH? IMAGE SOURCE: GETTY IMAGES. First to Alexa Alexa has been the main focus for Amazon this year, as Alexa adoption has surprised even optimistic company executives with its growth. With Apple's recent entry into home speakers with the HomePod, however, Amazon likely feels the need to up Alexa's game in both sound and speed. A custom chip could boost Alexa's inference capabilities -- the ability to process external inputs like speech and make sense of them at the device level. Doing more processing at the device level means less information needs to be sent back to the cloud, which means faster response times. Amazon has developed these chipmaking capabilities over the past few years through both internal hires as well as acquisitions, buying Annapurna Labs, an Israeli-based chipmaker, in 2015 for $350 million, and Blink, a maker of low-power security cameras, for $90 million late last year. Many believe Amazon was really after Blink's power-management chipmaking capabilities, rather than just its camera products, as Amazon already has its own Cloud Cam product that it rolled out last year. Then to the data center? The report was mostly centered on Alexa, but an AWS data center chip was also mentioned as a possibility. While Amazon has a big lead in cloud computing, other rivals, especially Alphabet, are investing huge sums to catch up in this all-important race. Alphabet has been investing in custom hardware for years, and recently unveiled its second-generation tensor processing unit (TPU), designed specifically for AI neural networks. According to The New York Times, the TPU development not only puts Alphabet in a race for technological superiority with makers of graphics processing units (GPUs) like NVIDIA, but may also afford Alphabet negotiating leverage over GPU producers as well. (Google still buys NVIDIA chips, according to The Times. GPUs, originally designed for graphics and gaming, are the leading machine-learning chips today.) In addition, Alphabet has started renting out its TPU computing power to developers using Google's Cloud Platform. The combination of technological innovation and the ability to lower costs is likely what caused Amazon to start developing its own chips, as it looks to maintain its cloud computing lead against these well-heeled competitors.

https://www.fool.com/investing/2018/02/23/amazon-and-alphabet-take-on-nvidia.aspx

Thursday, February 1, 2018

IBM Adds V100 GPUs to IBM Cloud; Targets AI, HPC

@IBM today announced availability of @Nvidia #V100 GPUs in the IBM #Cloud. The move keeps pace with @Amazon and @Microsoft #Azure which also offer V100s and is more evidence of the effort by major cloud providers to rapidly beef up their AI and #HPC capabilities. In making the announcement, IBM was quick to note it was “first to offer a comprehensive suite of GPUs including the P100, K80 and M60 on IBM Cloud bare metal and virtual servers.” John Considine, GM of Cloud Infrastructure Services, IBM, wrote in a blogpost, “Starting today, you can equip individual IBM Cloud bare metal servers with up to two Nvidia Tesla V100 PCIe GPU accelerators – Nvidia’s latest, fastest and most advanced GPU architecture. The combination of IBM high-speed network connectivity and bare metal servers with the Tesla V100 GPUs provides higher throughput than traditional virtualized servers.” “We’re focused on delivering new AI capabilities both in the cloud and on premises to help enterprises not only gain critical insights from their data, but also create new value with that data. We’ve been working closely with Nvidia to bring their latest GPU technology, Nvidia Tesla V100, to the cloud.” For now, the V100 will be on x86-based servers in the cloud and only on bare metal servers. Plans for offering V100 on POWER9-based servers in the cloud were not disclosed. “IBM does not comment on the status of future plans. We are working side by side with the IBM Power Systems team to ensure that IBM Cloud will deliver access to the best of IBM technology to allow customers to run HPC and AI workloads,” said a spokesman. The V100 is available in IBM’s POWER9 server line. Considine wrote, “To power on-premises workloads, IBM also offers the industry’s only CPU-to-GPU Nvidia NVLink connection on our latest POWER9 servers.” POWER9 is IBM latest processor (see HPCwire article, IBM Begins Power9 Rollout with Backing from DOE, Google). The new processor is being used in Summit and Sierra, two pre-exascale supercomputers funded by DoE. Like others, IBM is touting its cloud-based ability to handle AI workloads including the ability to speed training of “deep learning models and to create powerful cloud-native applications.” One NASA example cited is fascinating: “NASA Frontier Development Lab this past summer, a team of researchers and data scientists used machine learning techniques on the IBM Cloud to develop new processes for 3D modeling of asteroids from radar data. With an average of 35 new asteroids and near-Earth objects discovered each week, there is currently more data available than experts can keep up with, and existing 3D modeling processes can take several months. Using NVIDIA P100 GPUs on the IBM Cloud and IBM Cloud Object Storage, the team was able to generate asteroid shapes images an average of five to six times faster than previous processes allowed.” Ian Buck, vice president and general manager of Accelerated Computing at Nvidia is quoted in the blog, “The new IBM Cloud offerings based on our Volta technology provide incredible processing speeds and the ability to scale up or down on demand for HPC and deep learning.” IBM also collaborates with technology partners such as Rescale and Bitfusion to enable instant access to GPUs on IBM Cloud.

https://www.google.com/url?rct=j&sa=t&url=https://www.hpcwire.com/2018/01/31/ibm-adds-v100-gpus-ibm-cloud-targets-ai-hpc/&ct=ga&cd=CAEYBCoUMTc5ODM2NDM0MDgwMjY3OTc5MDIyGmI3YjFlM2JkZjkxMzZkZjY6Y29tOmVuOlVT&usg=AFQjCNFOxy6awHGq94dA0MnIw-WyTdNU6Q

Thursday, January 25, 2018

Intel Q4 Earnings: IoT, NAND & FPGAs Solutions in Spotlight

@Intel Corp. 's INTC initiatives in the Internet of Things ( #IoT), Non-Volatile Memory Solutions and Programmable Solutions Group along with cloud computing is lowering dependence on the PC market. These rapidly-growing market segments present significant prospects for the semiconductor giant in the near future. The company is set to report fourth-quarter 2017 results on Jan 25. Intel Corporation Price and EPS Surprise Intel Corporation Price and EPS Surprise | Intel Corporation Quote Click here  to know how the company's overall Q4 performance is expected to be. Segments to Aid Intel's Growth Internet of Things Group (5.3% of revenues) - Revenues jumped 23.2% from the year-ago quarter and 17.9% quarter over quarter to $849 million. Growth can be attributed to strength in industrial, retail and video applications. Intel closed the acquisition of Israel-based Mobileye, an autonomous vehicle technology provider. The deal will help the company to penetrate the $70-billion autonomous driving systems, data and services market. At CES event, the company announced few important partnerships - with BMW, Nissan, Volkswagen AG, and Ferrari - which show Intel's growing dominance in providing AI platforms which will power future vehicles. It will also leverage Intel's Mobileye-based mapping technology for improving self-driving cars navigation process. Intel also joined forces with SAIC Motor and NavInfo to extend crowdsourced map building to China. SAIC Motor will build cars of Level 3, 4, and 5. These vehicles will be powered by Mobileye technology, which means it will be able to handle most driving situations itself. The Zacks Consensus Estimate for the Internet of Things Group segment is currently pegged at $859 million. Non-Volatile Memory Solutions Group (5.5% of revenues) - Revenues jumped 37.3% year over year and 1.9% sequentially to $891 million. Fab 68, Intel's Dalian factory, continued to beat both ramp rate and yield goal. The factory accounted for more than half of the company's supply in the quarter where more than 70% of the total bits were 3D NAND. Recently, Intel and Micron have announced a mutual agreement to work separately in order to complete development of its third generation of 3D NAND technology. Intel and Micron will jointly develop the 3D XPoint at the Intel-Micron Flash Technologies ("IMFT"). The product, is expected to sample this year, and could enter production as early as 2019 at their joint facility in Lehigh, UT. The Zacks Consensus Estimate for the Non-Volatile Memory Solutions Group is currently pegged at $938 million. Programmable Solutions Group (2.9% of revenues) - The Altera business is now the Programmable Solutions Group, which increased 10.4% from the year-ago quarter and 6.6% sequentially to $469 million. Strength in advanced products, data center, automotive and military drove top-line growth. Notably, Microsoft is deploying Intel FPGAs to develop the industry's fastest public cloud network and acceleration of deep neural networks. The company had announced that it would use Intel's 14-nm Stratix 10 FPGAs for its accelerated deep-learning platform (Project Brainwave). Chinese e-commerce giant Alibaba also selected Intel FPGA's in its Cloud. Moreover, Audi is using Intel's Cyclone V SoC FPGA technology for the Level 3 autonomous driving system in its upcoming A8. The Zacks Consensus Estimate for the Programmable Solutions Group is currently pegged at $475 million. Zacks Rank and Stocks to Consider Intel carries a Zacks Rank #3 (Hold). NVIDIA Corporation NVDA , Western Digital Corporation WDC and Analog Devices, Inc. ADI are some better-ranked stocks in the same sector. All the three companies carry a Zacks Rank #2 (Buy). You can see  the complete list of today's Zacks #1 Rank (Strong Buy) stocks here. NVIDIA, Western Digital and Analog Devices have a long-term earnings growth rate of 10.3%, 25.1% and 10.4%, respectively. The Hottest Tech Mega-Trend of All Last year, it generated $8 billion in global revenues. By 2020, it's predicted to blast through the roof to $47 billion. Famed investor Mark Cuban says it will produce ""the world's first trillionaires,"" but that should still leave plenty of money for regular investors who make the right trades early.

http://m.nasdaq.com/article/intel-q4-earnings-iot-nand--fpgas-solutions-in-spotlight-cm909950

Thursday, January 18, 2018

Machine Learning in Finance: Challenges, Successes & Opportunities

AI or machine learning is changing the way industries across the spectrum interact with their customers, as well as develop their processes. And nowhere is this more evident than in the financial services business. #Machinelearning in finance is pushing the industry to the edge of technological advancement.

A new @insideHPC special report, sponsored by @Dell EMC and @NVIDIA, explores the benefits, challenges and considerations involved with adopting machine learning in finance.

The guide delves into recent key machine learning innovations in financial services to give readers examples of how the technology is being used today, what’s needed to leverage machine learning, what applications and technologies to use and more. It also offers ways to learn from recent successes in the field, as well as ways to connect with the broader machine learning community.

As financial institutions look to machine learning, they should first acknowledge the benefits, and challenges.CLICK TO TWEET

First up, when considering machine learning in finance solutions, one should start with reviewing the challenges involved to make sure to consider all options and scenarios. According to the guide, challenges include regulation and compliance, cybersecurity and fraud detection, risk management — and, of course, competition.

Next up, the report explores some of the technology behind machine learning in finance. Of all of the technological innovations that have made machine learning possible, GPUs have perhaps had the most impact.

Of course, after learning about the technology behind machine learning, a company needs to decide which solution is right for them. The special report goes into detail on some of these solutions, such as the Dell EMC PowerEdge C4140 server and NVIDIA Volta. Frameworks, such as Caffe, Tensorflow, Torch, Microsoft Cognitive Toolkit (CNTK), and Apache Mahout — used to support machine learning applications — are also explored. 

After absorbing all this information, the guide offers case studies, information on professional organizations and further reading on machine learning. Research centers described in this guide include the Dell EMC Customer Solution Center, Dell EMC Machine Learning Knowledge Center and the NVIDIA Deep Learning Institute. 

The guide is ideal for those in the financial services industry who are beginning to explore the potential of machine learning, as well as those looking to expand and maximize its use.

https://insidebigdata.com/2018/01/17/machine-learning-in-finance/

Sunday, January 7, 2018

Semiconductor Stocks: Year In Review; Where to Place Your Chips in 2018

With high-flying chip names such as @Micron (MU) and @Nvidia (NVDA) leading the way, #semiconductor stocks were one of 2017’s best-performing groups. Despite ending the year on a rough note, where the Philadelphia Semiconductor Index (SOX) declined almost 7% in late November, the SOX still crushed the broader market, posting 2017 returns of almost 40%, besting the 25% rise in the Dow Jones Industrial Average and a better-than 19% return in the S&P 500 Index. But as Lay’s has told us, when it comes to chips, you can’t eat just one. In that vein, unlike previous years, it was just the sexy growth chip names that dominated 2017. Even traditional powers like Intel (INTC), which rose 30%, ranking as one of the better-performing Dow components, participated in the rally. Thanks to its $15.3 billion acquisition of Israeli self-driving technology company Mobileye, Intel wrote its own narrative as a force to be reckoned with in the autonomous car market, which has become the new battleground for high-tech companies looking new revenue streams. Where Intel thrived Advanced Micro Devices (AMD), which was an early chip favorite in 2017, ended the year on a sour note. This is even though the company’s earnings during 2017 were consistently above expectations. In Q3 the company crushed estimates on both the top and bottom lines. Yet, the stock took a pounding, falling — at one point — more than 15%. Investors were fearful about the company’s lower Q4 guidance, which overlooked that the fourth quarter, with its implied sequential revenue decline, was seasonally weak and has little to do with business fundamentals. Nevertheless, in this fickle market where tons of other growth options exist, investors opted to go elsewhere. And it would seem Micron (up 85% in 2017) — a stock I told you to buy on March 23 at around $26 per share — and Nvidia (up almost 80%) were the beneficiaries. The former, which I proudly own, is operating on its stated objectives and better-diversifying the business, which — in my opinion — effectively removes the threat of what has been a highly cyclical industry for DRAM and NAND memory chip pricing. For Nvidia, few expected it would follow a colossal 2016 performance, during which it posted returns of 235%, with 2017 returns of almost 80%. But thanks to successes in high-growth markets like artificial intelligence, autonomous vehicles, where its chips are at the center of autonomous driving technology, the company posted breathtaking top- and bottom line growth results throughout 2017, which has now crushed Wall Street's estimates the past two years. Elsewhere Broadcom (AVGO) — though it didn’t get nearly the attention as Micron and Nvidia — was a clear winner. With better than 45% returns, the wireless giant, which has chip placement in both Apple (AAPL) and Samsung (SSNLF) products, gives it an advantage over competitors. And its $130 billion offer for Qualcomm (QCOM), which if completed, would be the biggest-ever tie-up in the tech sector, suggest Broadcom has no plans to rest in search of value-creating opportunities. Which companies will be this year’s Micron, Nvidia or Broadcom? Though it’s early, I expect AMD to have a bounce-back year. Other names to keep an eye on include Analog Devices (ADI), Cavium (CAVM), Texas Instruments (TXN), and Xilinx (XLNX). Granted, as evidenced by their strong performances in 2017, lead by Texas Instrument’s 44% gain, these four are not in the discount bins. But not only are they solid in terms of execution (not one missed analysts top or bottom-line estimates in 2017), they each have extensive product portfolios, containing assets that serve multiple applications within primary end markets such as enterprise storage, industrials, wired and wireless businesses, which are all growing at double-digit rates.

http://m.nasdaq.com/article/semiconductor-stocks-year-in-review-where-to-place-your-chips-in-2018-cm900733

Tuesday, January 2, 2018

Why You Should Hold on to Pure Storage (PSTG) Stock for Now

Shares of @Pure Storage Inc. #PSTG have been performing well of late. If you haven't taken advantage of the share price appreciation yet, its time you hold the stock in your portfolio as it looks promising and is poised to carry the momentum ahead. Shares of Pure Storage, which provides software-defined all-flash solutions that are uniquely fast and cloud-capable for customers, have returned 40.7% in a year, substantially outperforming the industry 's growth of 20.9%. What's Going in Favor of Pure Storage? Pure Storage is rapidly gaining traction in the flash storage market, as evident from its expanding customer base. In the third quarter, the company added more than 300 customers, bringing the total base to 4,000 organizations and reflecting an increase of 54% from the year-ago quarter. Notably, the company's cloud customers contributed around 25% to business while existing customers contributed 70%. The company's strong product portfolio with the likes of FlashArray and FlashBlade business segments was the key catalyst. Additionally, its data platform for cloud is gaining traction. Management is optimistic about its scalable storage solutions in an era where the world is typically being driven by big-data, artificial intelligence (AI) and data analytics based information. The company is currently focusing on three major aspects, namely increasing cloud customer base, solidifying the position of its next generation workload related core data infrastructure and tapping large enterprises as they "cloudify" their on-premise IT infrastructure. Management is also positive about the company's partner ecosystem, which assisted it in winning a multimillion dollar deal with a prominent financial services institution. The company stated that the storage market for AI is likely to witness CAGR of 78% over the next five years. Pure Storage is well poised to grab the opportunity. Further, its partnership with NVIDIA, which is a dominant player in AI related computation, is anticipated to be a positive. Notably, the company delivered a positive earnings surprise of 66.7% in the last quarter. Risks Prevail However, @Pure Storage faces intensifying competition in the flash-based storage market from the likes of @Hitachi, @NetApp, @Dell EMC, @Hewlett P Enterprise, @Lenovo and @IBM. The competition has resulted in a decline in the average selling price ("ASP"). Any decline in ASP will impact the results if shipments do not improve considerably. This will eventually hurt top-line growth going forward. Our Take We expect the aforementioned factors to help the company sustain its strong momentum and stay afloat in difficult times. Hence, we suggest that investors hold on to the stock at the moment. Zacks Rank & Key Picks Pure Storage carries a Zacks Rank #3 (Hold). NVIDIA Corporation NVDA , Western Digital Corporation WDC and Analog Devices, Inc. ADI are some better-ranked stocks in the same sector. All the three companies sport a Zacks Rank #1 (Strong Buy). You can see  the complete list of today's Zacks #1 Rank stocks here . NVIDIA, Western Digital and Analog Devices have a long-term earnings growth rate of 10.3%, 25.1% and 10.4%, respectively.

http://m.nasdaq.com/article/why-you-should-hold-on-to-pure-storage-pstg-stock-for-now-cm898859

Monday, January 1, 2018

NVIDIA Prohibits Use Of GeForce GPUs In Datacenters, Only Blockchain Processing Allowed

@NVIDIA made a pretty big change in its #GeForce EULA recently and this is something that could go on to cost a lot of entities an aggregate of millions if not billions of dollars in the long run. The company recently updated their EULA which now prohibits datacenter deployment of their GeForce GPUs for everything but #blockchain processing. Needless to say, this would force a shift to #Quadro and #Tesla’s in any #datacenters that were actually using GeForce cards or had planned to. Change in EULA targets use of GeForce cards in #AI and #DeepLearning applications in non-personal ‘datacenter’ use While there was no explicit restriction against using GeForce graphics cards in datacenters before, this was before deep learning took off in recent past. Previously, the company has always offered consumer variants in crippled form as far as double precision goes but now, with the advent of DNN processing, that is no longer as relevant as it once was. In fact, half precision is the name of the game now and NVIDIA GPUs are extremely good at deep neural net crunching and the consumer variants offer a much higher return on investment in CuDNN then the professional variants. “No Datacenter Deployment. The SOFTWARE is not licensed for datacenter deployment, except that blockchain processing in a datacenter is permitted.” -Extract from the EULA. I wouldn’t even be surprised to learn that there are (were?) data centers being built right now which initially planned to use GeForce cards (prompting this subtle backlash from NVIDIA) for the majority of processing. It is clear that NVIDIA feels that it has spent a lot of R&D on the DNN tech involved and if the industrial customers don’t actually pay the premium, it will cut into its profits. It is also clear that increasing prices of the GeForce cards is not an option if it wants to remain competitive and still retain its hold over the PC market. The solution? restrict datacenters from using their GeForce cards for any kind of processing, forcing them to use Teslas and Quadros for DNN work.

https://www.google.com/amp/s/wccftech.com/nvidia-geforce-eula-prohibits-datacenter-blockchain-allowed/amp/#ampshare=https://wccftech.com/nvidia-geforce-eula-prohibits-datacenter-blockchain-allowed/