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Nvidia’s ‘GPU Cloud’ Adopts Containers for AI Development

Thu, 05/11/2017 - 13:30

GPU powerhouse Nvidia’s entry into the cloud market is differentiated from public cloud leaders by its focus on delivering development tools for training artificial intelligence models and running AI workloads using application containers.

Nvidia CEO Jensen Huang unveiled the “GPU-accelerated cloud platform optimized for deep learning” during the company’s annual technology conference on Wednesday (May 10). Its AI development stack runs on the company’s distribution of Docker containers and is touted as “purpose built” for developing deep learning models on GPUs.

Among the goals is giving AI developers easier access to the growing suite of deep learning software available for AI applications. The on-ramp approach to GPU-based cloud computing addresses growing requirements to gather into a single stack the proliferation of deep learning frameworks, drivers, libraries, operating systems and processors used for AI development.

Nvidia CEO Jensen Huang unveils the components of the chipmaker’s GPU Cloud

“We took this incredibly complicated [software] stack and containerized it,” Huang stressed. Once these frameworks and other software building blocks were bundled, Nvidia created a cloud registry for the development stack to speed development of deep learning machines. “You download the container of your choice,” Huang added.

The software components within Nvidia’s AI supercomputer are bundled into a Docker container the company calls the Nvidia Graphics Cloud software stack. The idea is to make the up-to-date stack more readily available while optimizing performance.

The GPU cloud approach also addresses the computing resources needed to train neural networks, the company stressed. Developers could run the stack on GPU-powered machines, on Nvidia’s DGX systems or “the ten thousand GPUs that are in the cloud,” Huang said.

In one click, a single instance is created in the GPU cloud, the desired container is downloaded and “we burst your workload into the cloud,” the Nvidia CEO explained. “This is really the first hybrid, deep learning cloud computing platform.”

The graphics processor vendor based in Santa Clara, Calif., also announced the latest iteration of its Volta chip architecture, a high-end GPU dubbed Tesla V100 designed to power emerging AI development.

The combination of cutting edge graphics processing and scalable cloud computing resources is intended to attract a growing legion of AI developers who could leverage the service to build models of varying sizes, and then move them from prototyping to deployment in production via Docker containers.

The combination of the new graphics processor, the GPU cloud and AI software bundled and delivered in containers is seen by market watchers as a new way to boost AI development.

“The majority of deep learning applications will be run in cloud and hyper-scale environments, and the [Docker container] implementation lets users design on their own GPU systems then migrate to the cloud,” explained Addison Snell, CEO of Intersect360 Research.

The deep learning software stack is the “most interesting use of containers I’ve seen,” Snell added.

Huang said the GPU cloud platform would be available for beta testing in July. Pricing details would follow later, the company added.

–Tiffany Trader contributed to this report.

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Inspur Unveil AGX-2 Ultra-High Density AI Computing Server With NVIDIA®Tesla® V100 Support

Thu, 05/11/2017 - 10:10

San Jose, California, May 10, 2017 – Inspur, a leading datacenter and cloud computing total solutions provider, cooperate with NVIDIA® to unveil AGX-2, an ultra—high density AI computing server to accelerate Artificial Intelligence at GTC (GPU Technology Conference)2017. It is the world’s first 2U 8-GPUs with enabled Link™ 2.0, which is designed to provide maximum throughput for superior application performance to science and engineering computing, taking AI computing to the next level.

The AGX-2 supports up to 8 lastest NVIDIA®Tesla® V100 GPUs, offering either PCI-e interface or NVLink 2.0 for faster interlink connections between the GPUs, reaching peak performance results of up to 150GB/s. AGX-2 provides great I/O expansion capabilities, supporting 8x NVMe/SAS/SATA hot swap hard drives and high-speed cluster interconnect for up to 4x 100Gbps EDR InfiniBand ™ connector cards. AGX-2 supports both air-cooling and on-chip liquid-cooling to optimize and improve power efficiency and performance.

According to the LINPACK benchmark results, the AGX-2 achieves 29.33 TFLOPS, which is 2.47 times faster than the testing on NF5288M4 manufactured by Inspur with 4 GPUs in a 2U form factor. Regarding the real performance of training on an AI model, the AGX-2 delivers 1165 images/s, which is 2.49 times faster than the NF5288M4 with 4 Tesla M40, when the GoogLeNet model is trained with TensorFlow.

Leijun Hu, Vice President of Inspur Group, said “NVIDIA is the world leader in visual computing and is reshaping the next era of AI computing. Inspur partners with NVIDIA to announce the new and innovative AGX-2 GPU server today, which offers high computing density and enables faster, easier multi-GPU computing. The cooperation between the two companies also shows Inspur’s capability to develop high performance computing servers to propel AI, deep learning and advanced analytics, and we are hoping to provide even more energy-efficient computing solutions to serve customers around the world.

“Inspur , while having a long term cooperation with NVIDIA  ,has rich R&D and practice experience in computing system for deep learning as well as long time history cooperation with NVIDIA,” said Marc Hamilton. VP, Solutions Architecture and Engineering at NVIDIA. “The launch of AGX-2, the Ultra Dense Server which employs world’s top NVIDIA Tesla P100 GPU and high-speed interconnect NVLink technology, will comprehensively increase efficiency in AI and scientific engineering computing in terms of performance and energy consumption, and and it will provide both Chinese and global enterprises with leading high-performance computing capability.”

Inspur is a global leading server manufacturer, providing total computing solutions to the world’s leading AI and cloud computing companies, such as Baidu, Alibaba and Tencent. From building the world’s fastest supercomputer to being a leading server provider in China and across the world, Inspur is well positioned to be the fastest growing vendor for Cloud Data Center Solutions worldwide.

Visit the Inspur booth (#911) at the NVIDIA 2017 GPU Technology Conference, May 8~11, McEnery Convention Center, in San Jose, California. For more information on Inspur’s selection of GPU accelerated solutions, visit www.inspursystems.com.

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Vizua, Orange Silicon Valley Demonstrate GPU-Accelerated Supercomputing for AI and AR

Thu, 05/11/2017 - 08:41

SILICON VALLEY, Calif., and PARIS, France, May 11, 2017 — Vizua (vizua3D.com) and Orange Silicon Valley are prototyping a new GPU-enabled Cloud. Based on Vizua’s server computing patents and supercomputing platform from Orange Silicon Valley, the new Cloud will serve the emerging artificial intelligence (AI) and augmented reality (AR) applications in a wide range of industries.

Because of increasing data and sophisticated algorithms for processing and display, the biggest challenge today for AI and AR applications is computing power. Additionally, investing in dedicated resources can be costly — not only for upfront acquisition, but also maintenance and depreciation. And, dedicated resources often are not accessible to every device, everywhere, and make it difficult to share data and experiences. The new cloud prototype from Vizua/Orange Silicon Valley works towards addressing all these problems.

The new cloud prototype provides high speed GPU enabled processing on-demand and is accessible to devices everywhere. A single server has supercomputing capability with 20 NVIDIA GPUs capable of supporting real time 3D rendering of 200 Microsoft HoloLens users.

This supercomputing platform with optimized software from Vizua3D can handle over 10 times more users per GPU than a regular virtual machine (VM). It can also process extremely large files by optimizing the distribution over GPUs. Terabytes of data can be managed and loaded in a few seconds, and it is possible to launch a cloud service by leveraging such supercomputing infrastructure powered by the latest NVIDIA Pascal GPU architecture. Furthermore, the cloud based solution is designed to address a wide spectrum of markets: radiology, architecture, design, retail, industry, archeology, and gaming, to name just a few.

In the medical field, TeraRecon, with its Within Image Analysis product (WIA™), is leveraging the supercomputing testbed infrastructure from Orange Silicon Valley to accelerate AI workloads. The prototype AI platform from Orange Silicon Valley allows WIA™ to boost performance for deep neural network based training, testing and application of the AI engine to improve clinical workflow as well as the diagnosis of patients.

“We are very pleased to partner with the Orange Silicon Valley team,” says Sylvain Ordureau, co-founder and CEO of Vizua. “Orange Silicon Valley and Vizua are prototyping a new HPC Cloud GPU platform to optimize the cost, performance and number of users per GPU to enable modern, intelligent applications in a wide variety of markets.”

“Deep Learning on hundreds of Tera Bytes of imaging data requires enormous computational capability and real time holographic rendering workloads with augmented cognitive capabilities are demanding when we scale to hundreds of users. Real-time 3D imaging combined with Artificial Intelligence powered inferencing capability will revolutionize medicine,” says Georges Nahon CEO of Orange Silicon Valley.

Vizua will be at GTC 2017 San Jose (CA) May 7-11 to demonstrate the power of their technology on the supercomputing server prototype from Orange Silicon Valley. Booth visitors can experience the Microsoft HoloLens and view shared virtual models projected on real objects with very high precision and incredible image quality. AI medical applications showcasing 3D printed medical models and TeraRecon software (terarecon.com) will also be demonstrated.

About Vizua (vizua3D.com)

Vizua develops technology for the modern Cloud. Vizua’s Cloud application platform can host any application and enables GPU-accelerated performance streamed to customers using standard HTML5 with significantly reduced cost compared to other solutions. The 3D suite from Vizua integrates with Vizua Cloud technologies, and offers a full range of features for 3D/VR/AR applications. Vizua is a privately held company based in Seattle, WA and was founded by industry veterans in technology and business. For more information, contact Sylvain Ordureau, Co-Founder/CEO, +1 (514) 424 8598/+33 6 20 52 10 91

About TeraRecon (terarecon.com)

TeraRecon is the largest independent, vendor neutral medical image viewing solution provider with a focus on advanced image processing innovation. TeraRecon iNtuition and iNteract+ solutions advance the accessibility, performance, clinical functionality and medical imaging workflow throughout many areas of the healthcare ecosystem. The company provides world class advanced visualization 3D post-processing tools, as well as a spectrum of enterprise medical image viewing, diagnostic interpretation, image sharing, interoperability and collaboration solutions. TeraRecon is a privately-held company with its world headquarters in Foster City, California with major offices in Acton, MA, Durham, NC, Frankfurt, Germany and Tokyo, Japan.

About Orange Silicon Valley (http://www.orangesv.com/)

Orange Silicon Valley (OSV) is the wholly owned innovation subsidiary of Orange SA, one of the world’s leading telecommunications operators, serving 265 million customers across 29 countries. Through research, development, and strategic analysis, Orange Silicon Valley actively participates in the disruptive innovations that are changing the way we communicate. OSV contributes to and engages with the regional Silicon Valley ecosystem through numerous programs, such as our Orange Fab startup accelerator, Orange Institute, and ongoing collaborations with partners. Orange Silicon Valley acts as a guide to the digital revolution occurring in the San Francisco Bay Area, regularly hosting startups, businesses, and corporate leadership from around the world.

Source: Vizua

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IBM PowerAI Solution Cuts Deep Learning Training from Weeks to Hours

Thu, 05/11/2017 - 08:28

ARMONK, N.Y. May 11, 2017 — IBM (NYSE: IBM) has announced a significant new release of its PowerAI deep learning software distribution on Power Systems that attacks the major challenges facing data scientists and developers by simplifying the development experience with tools and data preparation while also dramatically reducing the time required for AI system training from weeks to hours.Data scientists and developers use deep learning to develop applications ranging from computer vision for self-driving cars to real time fraud detection and credit risk analysis systems. These cognitive applications are much more compute resource hungry than traditional applications and can often overwhelm x86 systems.  

“IBM PowerAI on Power servers with GPU accelerators provide at least twice the performance of our x86 platform; everything is faster and easier: adding memory, setting up new servers and so on,” said current PowerAI customer Ari Juntunen, CTO at Elinar Oy Ltd. “As a result, we can get new solutions to market quickly, protecting our edge over the competition. We think that the combination of IBM Power and PowerAI is the best platform for AI developers in the market today. For AI, speed is everything —nothing else comes close in our opinion.”

The new PowerAI roadmap announced today offers four significant new features that address critical customer needs for AI system performance, effective data preparation, and enterprise-level software:

  • Ease of Use: A new software tool called “AI Vision” that an application developer can use with limited knowledge about deep learning to train and deploy deep learning models targeted at computer vision for their application needs.
  • Tools for data preparation:  Integration with IBM Spectrum Conductor cluster virtualization software that integrates Apache Spark to ease the process of transforming unstructured as well as structured data sets to prepare them for deep learning training
  • Decreased training time: A distributed computing version of TensorFlow, a popular open-source machine learning framework first built by Google.  This distributed version of TensorFlow takes advantage of a virtualized cluster of GPU-accelerated servers using cost-efficient, high-performance computing methods to bring deep learning training time down from weeks to hours
  • Easier model development: A new software tool called “DL Insight” that enables data scientists to rapidly get better accuracy from their deep learning models. This tool monitors the deep learning training process and automatically adjusts parameters for peak performance.

“Data scientists and an emerging community of cognitive developers will lead much of the innovation in the cognitive era. Our objective with PowerAI is to make their journey to AI as easy, intuitive and productive as possible,” said Bob Picciano, Senior Vice President, IBM Cognitive Systems. “Power AI reduces the frustration of waiting and increases productivity.  Power Systems were designed for data and this next era of computing, in great contrast to x86 servers which were designed for the client/server programmable era of the past.”

PowerAI Support for New NVIDIA Volta Data Center GPU

PowerAI is optimized for IBM Power Systems S822LC for HPC, which are designed for data-intensive workloads like deep learning, machine learning and AI. The tight integration of IBM POWER processors and NVIDIA GPUs is enabled by the NVIDIA NVLink high-speed interconnect. This “super-highway” between the POWER processor and NVIDIA GPUs helps enables extremely fast data movement between the two types of processors. This exclusive CPU-to-GPU coupling delivers higher performance in AI training, which is a key metric for developer productivity. It enables innovation at a faster pace, so developers can invent and try new models, parameter settings and data sets. PowerAI will support the NVIDIA Volta architecture announced today. Volta features the next generation NVLink, with two key enhancements that benefit PowerAI customers: (a) the data transfer between the Power9 CPUs and Volta GPUs is ten times faster than between Volta GPUs and x86 CPUs, which rely on the old PCI-e 3.0 interface, first introduced 4 years ago, and (b) it is memory coherent, which makes programming GPU accelerators much easier for software developers by automatically moving data between the system memory connected to the Power9 CPU and the GPU memory.

About PowerAI

PowerAI’s curated, tested, and pre-packaged distribution of the major deep learning frameworks run on IBM Power System severs built for AI. This combination of best-in-class hardware and software is capable of handling compute-heavy workloads with ease. PowerAI provides data scientists and developers with the fastest time to development, both on-premises and in the cloud.

PowerAI is an enterprise software distribution of popular machine learning and deep learning open-source application frameworks. PowerAI is a single download, easy to install binary distribution that includes TensorFlow, Caffe, Torch, Theano, Chainer, NVIDIA DIGITS, and other machine and deep learning frameworks, along with the associated libraries and packages.

The PowerAI ecosystem includes software like Anaconda from Continuum Analytics, the H2O machine learning libraries from H2O, the Bons.ai AI software developer tools, and many others. IBM also offers enterprise support and services to developers using deep learning for their applications.

Source: IBM

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Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

Thu, 05/11/2017 - 00:47

At Nvidia’s GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company’s much-anticipated Volta architecture and flagship high-end GPU, the Tesla V100, noting that it took several thousand engineers several years to create, at an approximate development cost of $3 billion.

One thing is undeniable about the Volta V100: it is a giant chip, 33 percent larger than the Pascal P100 and once again “the biggest GPU ever made.” Fabricated by TSMC on a custom 12-nm FFN high performance manufacturing process, the V100 GPU squeezes 21.1 billion transistors and almost 100 billion via connectors on an 815 mm2 die, about the size of the Apple watch, said Huang.

“It is at the limits of photolithography,” Huang told the crowd. “You can’t make a chip any bigger than this because transistors would fall on the ground. Every single transistor that is possible to make by today’s physics was crammed into this processor.”

“To make one chip work per 12-inch wafer, I would characterize as unlikely,” added the CEO. “And so the fact that this was manufactured was a great feat.”

This is a domain specific chip, said Jonah Alben, senior vice president of GPU engineering at Nvidia. “This chip can run games very well if we want it to, but the focus [of the V100] is to be a great chip for AI and for HPC, so we dedicated all the resources we could until it was illegal to do more.”

“The first thing to know about Volta is it a giant leap for machine learning,” Luke Durant, principal engineer, CUDA Software, Nvidia followed. “[However,] we still are completely focused on high-performance computing. Across the board we’re seeing about a 1.5x speedup as compared to Pascal, just one year ago.”

Volta is a major launch for Nvidia, but not exactly a surprise. Back in 2014, the architecture was tapped to power the next-generation CORAL supercomputers, Summit and Sierra, in partnership with IBM, Mellanox and the Department of Energy. Those computers, expected to reach at least 200 petaflops of performance, are now due to be installed later this year into early 2018.

The new V100 touts spec’d performance of 7.5 teraflops double-precision, 15 teraflops single-precision, and 30 teraflops half-precision. This is nearly a 42 percent increase in peak flops over one year.

The Volta architecture introduces a brand new type of processor, Tensor Core, designed to accelerate AI workloads. With 640 Tensor Cores (8 per SM), V100 delivers 120 teraflops of deep learning performance, providing 6-12 times higher peak teraflops for Tensor operations compared with previous-generation silicon.

Volta is also slated to provide up to 60 tera-ops of INT8 performance. Nvidia kept the INT8 instructions to maintain compatibility with existing code bases and also reported that having a dedicated integer unit on Volta would help write machine learning kernels.

Tesla comparison over the last five years. Source: Nvidia. Click to Expand.

“With the V100, the most important statement isn’t the raw performance, although Nvidia managed to raise eyebrows with that,” commented Intersect360 Research CEO Addison Snell. “It’s that they are designing chips for double-precision 64-bit performance, single-precision 32-bit performance, or tensor performance, in the same package, so a single processor targets a range of applications in AI and HPC.”

Volta comes with 6MB of L2 cache and 16GB of HBM2 memory, providing 900 GB/s of bandwidth. The SMX2 form factor V100 features NVLink2 connectivity with nearly twice the throughput of the prior generation NVLink, going from 160 GB/s to 300 GB/s. Designers accomplished this by adding 50 percent more links and running them 28 percent faster.

Similar to the Pascal GP100, the Volta GV100 SM incorporates 64 FP32 cores and 32 FP64 cores per SM, however the new GPU has 80 SMs compared with 56 on the GP100. It thus has many more registers and supports more threads, warps, and thread blocks compared with previous Tesla generation GPUs, according to Nvidia.

Major features of the Volta SM include:

+ New mixed-precision FP16/FP32 Tensor Cores purpose-built for deep learning matrix arithmetic.

+ Enhanced L1 data cache for higher performance and lower latency.

+ Streamlined instruction set for simpler decoding and reduced instruction latencies.

+ Higher clocks and higher power efficiency.

“It has a completely different instruction set than Pascal,” remarked Bryan Catanzaro, vice president, Applied Deep Learning Research at Nvidia. “It’s fundamentally extremely different. Volta is not Pascal with Tensor Core thrown onto it – it’s a completely different processor.”

Catanzaro, who returned to Nvidia from Baidu six months ago, emphasized how the architectural changes wrought greater flexibility and power efficiency.

“It’s worth noting that Volta has the biggest change to the GPU threading model basically since I can remember and I’ve been programming GPUs for a while,” he said. “With Volta we can actually have forward progress guarantees for threads inside the same warp even if they need to synchronize, which we have never been able to do before. This is going to enable a lot more interesting algorithms to be written using the GPU, so a lot of code that you just couldn’t write before because it potentially would hang the GPU based on that thread scheduling model is now possible. I’m pretty excited about that, especially for some sparser kinds of data analytics workloads there’s a lot of use cases where we want to be collaborating between threads in more complicated ways and Volta has a thread scheduler can accommodate that.

“It’s actually pretty remarkable to me that we were able to get more flexibility and better performance-per-watt. Because I was really concerned when I heard that they were going to change the Volta thread scheduler that it was going to give up performance-per-watt, because the reason that the old one wasn’t as flexible is you get a lot of energy efficiency by ganging up threads together and having the capability to let the threads be more independent then makes me worried that performance-per-watt is going to be worse, but actually it got better, so that’s pretty exciting.”

Added Alben: “This was done through a combination of process and architectural changes but primarily architecture. This was a very significant rewrite of the processor architecture. The Tensor Core part is obviously very [significant] but even if you look at FP32 and FP64, we’re talking about 50 percent more performance in the same power budget as where we’re at with Pascal. Every few years, we say, hey we discovered something really cool. We basically discovered a new architectural approach we could pursue that unlocks even more power efficiency than we had previously. The Volta SM is a really ambitious design; there’s a lot of different elements in there, obviously Tensor Core is one part, but the architectural power efficiency is a big part of this design.”

 

Nvidia showed off three different V100 form factors at GTC: the 300 watt SXM2 (mezzanine) module; an inferencing accelerator for hyperscale that is a 150 watt full height, half length (FHHL) PCIe card about the size of a CD case; and the standard PCIe two-slot, full-length card.

DGX-1 with eight V100s

V100 GPUs will be available starting next quarter, according to Nvidia. Customers can pre-order the Volta-series DGX-1 box now for $149,000, $20,000 more than the list price for the Pascal-equipped version.

In addition to the coming DGX-1 Volta refresh, Nvidia also released the new DGX Station. Billed as a “personal supercomputer for AI development,” DGX Station provides four NVLink-connected Tesla V100s to deliver 480 (peak) Tensor teraflops in a 1,500 watt water-cooled chassis for $69,000.

Riding the wave of AI and HPC announcements made this week and on the heels of a stronger-than-expected first quarter (recording revenue of $1.94 billion with record datacenter sales of $409 million), Nvidia shares were up 18 percent as of close of market Wednesday, reaching $121.29, an all-time high.

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Themis Introduces Compact 2U Rugged HPC Server at GTC

Wed, 05/10/2017 - 18:28

SAN JOSE, Calif., May 10, 2017 — Themis Computer, a manufacturer of rugged computing hardware and NVIDIA partner, is showcasing its new RES-NT2 2U GPU Computing server this week at the GPU Technology Conference, booth 302, in San Jose.

A new addition to the RES-NT2 High Performance Computing (HPC) family, the RES-NT2 2U brings virtualization and supercomputing capabilities to space-limited demanding environments. Its shallow 23-inch lightweight chassis supports two dual-slot NVIDIA GPU accelerators, 32TB storage, 1TB ECC DDR4 memory, and two E5-2600 v3/v4 series Xeon processors with up to 18 cores per socket. A wide range of I/O, storage options, enhanced reliability features, and expansion choices allow users maximum flexibility for current and future system requirements.

“We wanted to address the size, weight, and power (SWAP) limitations of our HPC customers,” said Bill Kehret, CEO of Themis Computer. “Our 2U form factor now allows rugged applications—which were previously limited by space and storage—the ability to utilize advanced NVIDIA computing at the tactical edge.”

Among the new system’s highlights are:

  • Performance Features: The RES NT2 2U is available with two double-width NVIDIA Tesla GPU accelerators, two Intel E5-2600 v3/v4 series processors with up to 18 cores per socket, 1TB ECC DDR4 2400MHz Memory with 16x DIMM slots, optional 100Gbit Ethernet, and eight removable front-access drives. IPMI v2.0, TPM 2.0, popular operating systems, and other GPU accelerators are supported.
  • Environmental Resiliency: The system is enclosed in a 23-inch corrosion resistant aluminum and stainless steel reinforced chassis that operates in a temperature range of 0°C to +50°C depending on configuration. Advanced thermal and mechanical design features provide superior resilience to shock, vibration, and temperature extremes while meeting MIL-STD-810G (Shock and Vibration) specifications. Dual redundant, hot swappable AC power supplies provide high availability.
  • Scalability: The server comes with five PCIe x16 expansion slots, extensive high speed I/O, and eight SAS3 capable 2.5-inch drive slots that accommodate either SSD (up to 4TB each) or HDD (up to 2TB each). System expansion is available through the addition of commercially available, off the shelf (COTS) networking cards, specialized processing such as encryption, high speed storage, and other value-added options.

Rugged Virtualization or Supercomputing
Designed for rugged military, commercial, and industrial applications, the RES-NT2 allows users up to a three-fold increase in computing performance without compromising operational reliability. RES-NT2 with NVIDIA GRID™ enables multiple virtual machines to have direct access to high performance graphic processing such as 3D visualization, visual simulation, and virtual reality. With teraflops of single and double precision performance, NVIDIA Tesla GPUs deliver record speeds for artificial intelligence, deep learning, signal processing, modeling, and computational physics. RES-NT2 delivers supercomputing and desktop virtualization anywhere – in the back of a truck, aboard a ship, or on an aircraft.

Pricing and Availability
RES-NT2 2U servers are available. Specifications and product options are available at www.themis.com/res-nt2.

Upcoming Themis Events

Themis looks forward to demonstrating computing solutions and discussing current and future computing requirements. The company will be exhibiting at the Special Operation Forces Industry Conference (SOFIC), Booth 20, in Tampa Florida from May 15-18, 2017, and at the U.S. Pavilion, Booth N4-352 at DSEI in Excel London from September 12-15, 2017. For appointments and other future events, please visit www.themis.com/events.

About Themis Computer

Themis combines high-performance computing and advanced thermal and mechanical design expertise to deliver reliable standards-based and custom embedded computing solutions for OEMs, systems integrators, and application providers. Themis products achieve a superior balance between standard commercial technology and ruggedness, keeping mission-critical applications available in the most demanding environments.

Source: Themis Computer

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HPE Launches Servers, Services, and Collaboration at GTC

Wed, 05/10/2017 - 15:00

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, and formal support for NVIDIA’s just announced Volta GPU architecture – all as part of a barrage of announcements made at GTC. In short order GTC has become an important launch venue for a broadening spectrum of AI/deep learning-related offerings that leverage accelerator technology. GTC’s growing prominence also showcases, at least for present, NVIDIA’s dominance.

Among HPE announcement highlights are:

  • The new HPE SGI 8600 is based on the SGI ICE XA architecture with support for optimal combination of liquid-cooled GPU performance with NVIDIA Tesla GPU accelerators with NVLink interconnect technology. HPE says the new server provides scale and efficiency for complex, largest environments – “up to thousands of nodes with leading power efficiency.”
  • Interactive rendering capability from a central datacenter with the HPE Apollo 6500 and NVIDIA Tesla GPUs certified with NVIDIA VCA software.
  • Support for NVIDIA’s next generation Tesla GPUs based on its Volta architecture when available in production quantities in the Apollo 2000, Apollo 6500 and Proliant DL380 servers.
  • Collaboration with Kinetica (GPU-accelerated and analytics database) to develop a real-time fraud detection product with GPU acceleration. Designed specifically for consumer credit card transaction processing, the new solution, will be demoed at the HPE booth during GTC
HPE SGI 8600

Customers pursuing Deep Learning projects face a variety of challenges including a lack of mature IT infrastructure and technology capabilities leading to poor performance, efficiency and time to value,” said Bill Mannel, vice president and general manager, High Performance Computing and Artificial Intelligence at Hewlett Packard Enterprise in the formal press release. “To address these challenges, HPE is introducing new optimized GPU compute platforms, an enhanced collaboration with NVIDIA and HPE Pointnext Services from the Core Datacenter to the Intelligent Edge.”

The beefed up HPE-NVIDIA collaboration is interesting. The partners say they will “jointly address GPU technology integration and deep learning expertise challenges to accelerate the adoption of technologies that provide real-time insights from massive data volumes.” The combination of HPE’s purchase of SGI and Tokyo Institute of Technology’s choice of SGI for its next TSUBAME machine, which is focused on AI/deep learning and leverages NVIDIA GPUS, was a natural push for a deeper relationship.

“As the artificial intelligence era takes hold, enterprises are increasingly adopting NVIDIA’s GPU computing platform to generate insights from decades of untapped data,” said Ian Buck, General Manager of Accelerated Computing at NVIDIA. “Expanding our collaboration with HPE around deep learning will help enterprises deploy, manage and optimize their GPU computing infrastructure and realize the benefits of AI and deep learning in their business.”

The planned Tokyo supercomputer is one of the largest NVIDIA Tesla P100 GPU based clusters. HPE and NVIDIA say the collaboration will deliver:

  • Enhanced Centers of Excellence for benchmarking, code modernization 
and proof of concept initiatives. The locations include Korea, Sydney, Grenoble, Bangalore and Houston
  • Early access program for Volta-based NVIDIA Tesla SXM2 GPU systems powered with eight GPUs for selected customers in 4Q 2017

Satoshi Matsuoka, Professor and TSUBAME Leader, Tokyo Institute of Technology described the new machine as a converged world-leading HPC and deep learning platform. “The NVIDIA Tesla P100 SXM2 node solution enables GPU based Deep Learning capability to be scalable to the entire size of our TSUBAME 3.0 system. We look forward to continuing our partnership with HPE to work together on future projects in HPC and Deep Learning,” said Matsuoka.

HPE used the announcement as an opportunity to promote its new technology services organization – Pointnext – introduced in March. Pointnext combined consulting and support organizations under one umbrella. It has three main offerings; advisory services, professional services, and operational services.

Pointnext is described as an “IT services organization built to make Hybrid IT simple and power the Intelligent Edge. As an agile technology partner, we help you to modernize your legacy infrastructure with the flexibility of the cloud, and maximize the value of your connected devices.” HPE reports Pointcast has roughly 25,000 “specialists” in 80 countries.

Apollo 6500

Also noteworthy is the Kinetica collaboration. Like virtually all HPC technology system providers seeking expansion into the enterprise, HPE is working on “vertical” solutions for particular workflows. Kinetica expertise should help HPE implement such systems.

“We look forward to advancing the Kinetica GPU database with HPE and jointly offering a best-of-breed GPU-accelerated analytics solution that converges Artificial Intelligence and Business Intelligence workloads for financial services as well as for retail, healthcare and other industries,” said Chris Prendergast, vice president of business development and alliances, Kinetica.

Steve Conway, SVP for research, market watcher Hyperion Research, said, “With the need to embed more intelligence and automation into data analytics to address scientific and business challenges, artificial intelligence-based techniques are growing in importance. HPE’s systems and solutions innovations announced today are designed to address key performance and expertise constraints affecting deep learning.”

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NVIDIA & H2O.ai Announce Major Partnership News

Wed, 05/10/2017 - 14:50

MOUNTAIN VIEW, Calif., May 10, 2017 — H2O.ai, the company bringing AI to business, today announced that it has collaborated with NVIDIA to offer its best-of-breed machine learning algorithms in a newly minted GPU edition. In addition, H2O’s platform will be optimized for NVIDIA DGX-1 systems.

Enterprises can use this end-to-end solution to operate on large datasets, iterate faster, deploy quickly and gain real-time insights. H2O’s AI solutions enable customers to train machine learning models up to 75x faster compared to conventional CPU-based solutions. Potential use cases for this GPU integration include customer service, fraud prevention, financial advising and healthcare personalization. As part of the initial release, H2O.ai demonstrated its Generalized Linear Model and Gradient Boosted Tree implementation that are accelerated using GPUs.

“AI with automatic machine learning will make data monetization possible for anyone with data. H2O.ai is excited to unveil our growing partnership with NVIDIA to bring interpretable, fast and accurate algorithms to GPUs.” said Sri Ambati, CEO and Co-founder of H2O.ai. “H2O.ai with NVIDIA GPU acceleration brings high-performance cloud-neutral learning and inference stack for enterprise AI communities.”

About 9,000 enterprises and a third of Fortune 500 companies are using H2O.ai’s open source platform to derive value from data with machine learning and deep learning and receive real-time insights and make actionable decisions.

“Future advancements in machine learning will unlock opportunities for us to create breakthrough consumer experiences in ways that we can’t even imagine today,” said Adam Wenchel, VP of AI and Data Innovation at Capital One. “As users of the H2O.ai and NVIDIA platforms, we see GPU acceleration of machine learning as a transformative development for the enterprise distributed machine learning community.”

“Enterprises across industries will benefit from H2O.ai’s machine learning portfolio powered by the NVIDIA deep learning platform,” said Jim McHugh, vice president and general manager at NVIDIA. “Customers can now leverage H2O.ai machine learning algorithms to do clustering, classification, predictive analysis, and anomaly detection at speeds only possible on our platform.”

H2O.ai has enjoyed a banner year so far. The company was named a Strong Performer in The Forrester Wave: Predictive Analytics & Machine Learning (PAML) Solutions, Q1 2017 report by Forrester Research in March, a Visionary in Gartner’s February Magic Quadrant and one of CB Insights’ AI 100 in January. 169 of the Fortune 500 companies—8 of the world’s top 10 banks, 7 of the top 10 insurance companies and 4 of the top 10 healthcare companies—depend on H2O.ai to power their data science.

For more information about H2O.ai, please visit www.h2o.ai.

About H2O.ai

H2O.ai is focused on bringing AI to businesses through software. Its flagship product is H2O, the leading open source platform that makes it easy for financial services, insurance and healthcare companies to deploy AI and deep learning to solve complex problems. More than 9,000 organizations and 80,000+ data scientists depend on H2O for critical applications like predictive maintenance and operational intelligence. The company — which was recently named to the CB Insights AI 100 — is used by 169 Fortune 500 enterprises, including 8 of the world’s 10 largest banks, 7 of the 10 largest insurance companies and 4 of the top 10 healthcare companies. Notable customers include Capital One, Progressive Insurance, Transamerica, Comcast, Nielsen Catalina Solutions, Macy’s, Walgreens and Kaiser Permanente.

Source: H2O.ai

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Cray Delivers Production-Ready AI With CS-Storm Accelerated Cluster Supercomputers

Wed, 05/10/2017 - 14:46

SEATTLE, May 10, 2017 — Cray Inc. (Nasdaq:CRAY) today announced the launch of two new Cray CS-Storm accelerated cluster supercomputers – the Cray CS-Storm 500GT and the Cray CS-Storm 500NX. Purpose built for the most demanding artificial intelligence (AI) workloads, the new Cray systems will provide customers with powerful, accelerator-optimized solutions for running machine learning and deep learning applications.

The new Cray CS-Storm systems are designed for organizations looking for the fastest path to new discoveries, a building block approach to scalability, and the assurance of collaborating with a trusted partner with a long history of designing and deploying tightly-integrated, highly-scalable systems. Leveraging NVIDIA Tesla GPU accelerators, the new Cray CS-Storm systems expand Cray’s portfolio of integrated systems and will give customers a broader range of accelerated supercomputers for computational and data-intensive applications.

“Customer demand for AI-capable infrastructure is growing quickly, and the introduction of our new CS-Storm systems will give our customers a powerful solution for tackling a broad range of deep learning and machine learning workloads at scale with the power of a Cray supercomputer,” said Fred Kohout, Cray’s senior vice president of products and chief marketing officer. “The exponential growth of data sizes, coupled with the need for faster time-to-solutions in AI, dictates the need for a highly-scalable and tuned infrastructure.”

The Cray CS-Storm systems provide up to 187 TOPS (tera operations per second) per node, 2,618 TOPS per rack for machine learning application performance, and up to 658 double precision TFLOPS per rack for HPC application performance. Delivered as a fully integrated cluster supercomputer, the Cray CS-Storm systems include the Cray Programming Environment, Cray Sonexion scale out storage, and full cluster systems management.

The Cray CS-Storm 500GT includes support for up to ten NVIDIA Tesla P40 or P100 PCIe accelerators leveraging balanced or single-root configurations for CPU-to-GPU communications. The CS-Storm 500NX includes support for eight Tesla P100 SXM2 accelerators, utilizing the NVIDIA NVLink high speed interconnect for GPU-to-GPU communications.

“Early adopters of big data analytics and AI have learned a painful lesson as they have struggled to scale their applications and keep pace with data growth and use more sophisticated models,” said Shahin Khan, founding partner at OrionX Research, “You must have the right systems from the beginning to be able to scale, otherwise inefficiencies accumulate and multiply. Expertise in large scale system design and application optimization is critical. That’s an area that Cray has led for decades.”

For more information on the Cray CS-Storm series of supercomputers please visit the Cray website at www.cray.com.

About Cray Inc.

Global supercomputing leader Cray Inc. (Nasdaq:CRAY) provides innovative systems and solutions enabling scientists and engineers in industry, academia and government to meet existing and future simulation and analytics challenges. Leveraging more than 40 years of experience in developing and servicing the world’s most advanced supercomputers, Cray offers a comprehensive portfolio of supercomputers and big data storage and analytics solutions delivering unrivaled performance, efficiency and scalability. Cray’s Adaptive Supercomputing vision is focused on delivering innovative next-generation products that integrate diverse processing technologies into a unified architecture, allowing customers to meet the market’s continued demand for realized performance. Go to www.cray.com for more information.

Source: Cray

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IBM Unveils New AI Software, Will Support Nvidia Volta

Wed, 05/10/2017 - 14:43

ARMONK, N.Y., May 10, 2017 — IBM (NYSE: IBM) today announced a significant new release of its PowerAI deep learning software distribution on Power Systems that attacks the major challenges facing data scientists and developers by simplifying the development experience with tools and data preparation while also dramatically reducing the time required for AI system training from weeks to hours.

Data scientists and developers use deep learning to develop applications ranging from computer vision for self-driving cars to real time fraud detection and credit risk analysis systems. These cognitive applications are much more compute resource hungry than traditional applications and can often overwhelm x86 systems.  

“IBM PowerAI on Power servers with GPU accelerators provide at least twice the performance of our x86 platform; everything is faster and easier: adding memory, setting up new servers and so on,” said current PowerAI customer Ari Juntunen, CTO at Elinar Oy Ltd. “As a result, we can get new solutions to market quickly, protecting our edge over the competition. We think that the combination of IBM Power and PowerAI is the best platform for AI developers in the market today. For AI, speed is everything —nothing else comes close in our opinion.”

The new PowerAI roadmap announced today offers four significant new features that address critical customer needs for AI system performance, effective data preparation, and enterprise-level software:

  • Ease of Use: A new software tool called “AI Vision” that an application developer can use with limited knowledge about deep learning to train and deploy deep learning models targeted at computer vision for their application needs.
  • Tools for data preparation: Integration with IBM Spectrum Conductor cluster virtualization software that integrates Apache Spark to ease the process of transforming unstructured as well as structured data sets to prepare them for deep learning training
  • Decreased training time: A distributed computing version of TensorFlow, a popular open-source machine learning framework first built by Google. This distributed version of TensorFlow takes advantage of a virtualized cluster of GPU-accelerated servers using cost-efficient, high-performance computing methods to bring deep learning training time down from weeks to hours
  • Easier model development: A new software tool called “DL Insight” that enables data scientists to rapidly get better accuracy from their deep learning models. This tool monitors the deep learning training process and automatically adjusts parameters for peak performance.

“Data scientists and an emerging community of cognitive developers will lead much of the innovation in the cognitive era. Our objective with PowerAI is to make their journey to AI as easy, intuitive and productive as possible,” said Bob Picciano, Senior Vice President, IBM Cognitive Systems. “Power AI reduces the frustration of waiting and increases productivity.  Power Systems were designed for data and this next era of computing, in great contrast to x86 servers which were designed for the client/server programmable era of the past.”

PowerAI Support for New NVIDIA Volta Data Center GPU

PowerAI is optimized for IBM Power Systems S822LC for HPC, which are designed for data-intensive workloads like deep learning, machine learning and AI. The tight integration of IBM POWER processors and NVIDIA GPUs is enabled by the NVIDIA NVLink high-speed interconnect. This “super-highway” between the POWER processor and NVIDIA GPUs helps enables extremely fast data movement between the two types of processors. This exclusive CPU-to-GPU coupling delivers higher performance in AI training, which is a key metric for developer productivity. It enables innovation at a faster pace, so developers can invent and try new models, parameter settings and data sets.

PowerAI will support the NVIDIA Volta architecture announced today. Volta features the next generation NVLink, with two key enhancements that benefit PowerAI customers: (a) the data transfer between the Power9 CPUs and Volta GPUs is ten times faster than between Volta GPUs and x86 CPUs, which rely on the old PCI-e 3.0 interface, first introduced 4 years ago, and (b) it is memory coherent, which makes programming GPU accelerators much easier for software developers by automatically moving data between the system memory connected to the Power9 CPU and the GPU memory.

About PowerAI

PowerAI’s curated, tested, and pre-packaged distribution of the major deep learning frameworks run on IBM Power System severs built for AI. This combination of best-in-class hardware and software is capable of handling compute-heavy workloads with ease. PowerAI provides data scientists and developers with the fastest time to development, both on-premises and in the cloud.

PowerAI is an enterprise software distribution of popular machine learning and deep learning open-source application frameworks. PowerAI is a single download, easy to install binary distribution that includes TensorFlow, Caffe, Torch, Theano, Chainer, NVIDIA DIGITS, and other machine and deep learning frameworks, along with the associated libraries and packages.

The PowerAI ecosystem includes software like Anaconda from Continuum Analytics, the H2O machine learning libraries from H2O, the Bons.ai AI software developer tools, and many others. IBM also offers enterprise support and services to developers using deep learning for their applications.

Source: IBM

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IBM PowerAI Tools Aim to Ease Deep Learning Data Prep, Shorten Training 

Wed, 05/10/2017 - 12:07

A new set of GPU-powered AI software announced by IBM today brings automation to many of the tedious, time consuming and complex aspects of AI project on-ramping while reducing deep learning training times, according to Big Blue, from weeks to hours with a new, distributed version of TensorFlow running on clusters.

The new PowerAI software is comprised of four primary parts:

  • “AI Vision,” a tool designed for developers with limited knowledge of deep learning to train and deploy deep learning models for computer vision.
  • Integration with IBM Spectrum Conductor cluster virtualization software that integrates Apache Spark to ease transforming unstructured and structured data sets to prepare them for deep learning training.
  • A distributed computing version of TensorFlow, the open-source machine learning framework built by Google, that can run on a virtualized cluster of GPU-accelerated servers, which IBM said cuts learning training time from weeks to hours.
  • “DL Insight,” a new tool that helps data scientists to sharpen the accuracy of deep learning models by monitoring the deep learning training process and automatically adjusting parameters for peak performance.

“We’re adding a set of tools to ease development for data scientists and we’re adding a set of features that accelerate the training time,” IBM’s VP, HPC, AI and Analytic, Sumit Gupta, told EnterpriseTech (HPCwire’s sister publication). “PowerAI makes it much easier for data scientists and developers to use AI to build their applications, rather than having to write complicated code, worry about cluster management, and issues of that kind.”

IBM PowerAI software is “curated, tested, and pre-packaged distribution of the major deep learning frameworks,” including TensorFlow, Caffe, Torch, Theano, Chainer, NVIDIA DIGITS, among others. In making the announcement, IBM called attention to what it said are the performance advantages of GPU-driven AI implementations on the IBM Power Systems S822LC for HPC server, for which PowerAI is optimized.

The server combines IBM POWER processors and NVIDIA GPUs, embedded with a high-speed data interface between the POWER processor and the NVIDIA GPU (NVLink), IBM said. This coupling delivers higher performance in AI training, enabling developers to try new models, parameter settings and data sets at a faster pace, according to IBM.

“IBM PowerAI on Power servers with GPU accelerators provide at least twice the performance of our x86 platform,” said Ari Juntunen, CTO at Elinar Oy Ltd, an electronic content management company. “Everything is faster and easier: adding memory, setting up new servers and so on. As a result, we can get new solutions to market very quickly, protecting our edge over the competition. We think that the combination of IBM Power and PowerAI is the best platform for AI developers in the market today. For AI, speed is everything —nothing else comes close in our opinion.”

IBM also cited the example of Korean Electric Power Research Institution (KEPRI), which wanted to use drones for inspection of high-voltage power lines.

“We needed a deep learning and high speed storage platform that could process and store the vast number of images/videos we receive from the drones,” said KEPRI’s Chan-Wook Lim. “(PowerAI) has met those needs, allowing us to improve our system while also providing a cost reduction for our inspections.”

IBM’s Gupta said KEPRI is typical of the kind of computer vision workloads PowerAI and AI Vision is designed to simplify.

Sumit Gupta, IBM

“The time consuming part of it is using a framework like TensorFlow on a 100M images,” he said. “You run into a challenge when you have a 100M images that need to be transformed and prepped to be input into TensorFlow. We automate the data prep and ETL using Spectrum Conductor…, it automatically launches underneath a whole cluster of Spark jobs, each one of them is running and transforming 5 million of those images at a time. From a user perspective, they don’t even know that…it went and launched a whole lot of jobs on a cluster, all they know is that the data is getting transformed.”

“Data scientists and an emerging community of cognitive developers will lead much of the innovation in the cognitive era. Our objective with PowerAI is to make their journey to AI as easy, intuitive and productive as possible,” said Bob Picciano, Senior Vice President, IBM Cognitive Systems. “Power AI reduces the also reduce frustration of waiting and increase productivity. Power Systems were designed for data and this next era of computing, in great contrast to x86 servers which were designed for the client/server

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NVIDIA Launches Revolutionary Volta GPU Platform, Fueling Next Era of AI and High Performance Computing

Wed, 05/10/2017 - 11:58

SAN JOSE, Calif., May 10, 2017 — NVIDIA (NASDAQ: NVDA) today launched Volta — the world’s most powerful GPU computing architecture, created to drive the next wave of advancement in artificial intelligence and high performance computing.

The company also announced its first Volta-based processor, the NVIDIA Tesla V100 data center GPU, which brings extraordinary speed and scalability for AI inferencing and training, as well as for accelerating HPC and graphics workloads.

“Artificial intelligence is driving the greatest technology advances in human history,” said Jensen Huang, founder and chief executive officer of NVIDIA, who unveiled Volta at his GTC keynote. “It will automate intelligence and spur a wave of social progress unmatched since the industrial revolution.

“Deep learning, a groundbreaking AI approach that creates computer software that learns, has insatiable demand for processing power. Thousands of NVIDIA engineers spent over three years crafting Volta to help meet this need, enabling the industry to realize AI’s life-changing potential,” he said.

Volta, NVIDIA’s seventh-generation GPU architecture, is built with 21 billion transistors and delivers the equivalent performance of 100 CPUs for deep learning.

It provides a 5x improvement over Pascal, the current-generation NVIDIA GPU architecture, in peak teraflops, and 15x over the Maxwell architecture, launched two years ago. This performance surpasses by 4x the improvements that Moore’s law would have predicted.

Demand for accelerating AI has never been greater. Developers, data scientists and researchers increasingly rely on neural networks to power their next advances in fighting cancer, making transportation safer with self-driving vehicles, providing new intelligent customer experiences and more.

Data centers need to deliver exponentially greater processing power as these networks become more complex. And they need to efficiently scale to support the rapid adoption of highly accurate AI-based services, such as natural language virtual assistants, and personalized search and recommendation systems.

Volta will become the new standard for high performance computing. It offers a platform for HPC systems to excel at both computational science and data science for discovering insights. By pairing CUDA cores and the new Volta Tensor Core within a unified architecture, a single server with Tesla V100 GPUs can replace hundreds of commodity CPUs for traditional HPC.

Breakthrough Technologies
The Tesla V100 GPU leapfrogs previous generations of NVIDIA GPUs with groundbreaking technologies that enable it to shatter the 100 teraflops barrier of deep learning performance. They include:

  • Tensor Cores designed to speed AI workloads. Equipped with 640 Tensor Cores, V100 delivers 120 teraflops of deep learning performance, equivalent to the performance of 100 CPUs.
  • New GPU architecture with over 21 billion transistors. It pairs CUDA cores and Tensor Cores within a unified architecture, providing the performance of an AI supercomputer in a single GPU.
  • NVLink provides the next generation of high-speed interconnect linking GPUs, and GPUs to CPUs, with up to 2x the throughput of the prior generation NVLink.
  • 900 GB/sec HBM2 DRAM, developed in collaboration with Samsung, achieves 50 percent more memory bandwidth than previous generation GPUs, essential to support the extraordinary computing throughput of Volta.
  • Volta-optimized software, including CUDA, cuDNN and TensorRT software, which leading frameworks and applications can easily tap into to accelerate AI and research.

Ecosystem Support for Volta
Volta has received broad industry support from leading companies and organizations around the world:

“NVIDIA and AWS have worked together for a long time to help customers run compute-intensive AI workloads in the cloud. We launched the first GPU-optimized cloud instance in 2010, and introduced last year the most powerful GPU instance available in the cloud. AWS is home to some of today’s most innovative and creative AI applications, and we look forward to helping customers continue to build incredible new applications with the next generation of our general-purpose GPU instance family when Volta becomes available later in the year.”
— Matt Garman, vice president of Compute Services, Amazon Web Services

“We express our congratulations to NVIDIA’s latest release of Volta. From Baidu Cloud to Intelligent Driving, Baidu has been strengthening its efforts in building an open AI platform. Together with NVIDIA, we believe we will accelerate the development and application of the global AI technology and create more opportunities for the whole society.”
— Yaqin Zhang, president, Baidu

“NVIDIA and Facebook have been great partners and we are excited about the contributions NVIDIA has made to Facebook’s Caffe2 and PyTorch. We look forward to the AI advances NVIDIA’s new high-performing Volta graphics architecture will enable.”
— Mike Schroepfer, chief technology officer, Facebook

“NVIDIA’s GPUs deliver significant performance boosts for Google Cloud Platform customers. GPUs are an important part of our infrastructure, offering Google and our enterprise customers extra computational power for machine learning or high performance computing and data analysis. Volta’s performance improvements will make GPUs even more powerful and we plan to offer Volta GPUs on GCP.”
— Brad Calder, vice president of Engineering for Google Cloud Platform, Google

“Microsoft and NVIDIA have partnered for years on AI technologies, including Microsoft Azure N-series, Project Olympus and Cognitive Toolkit. The new Volta architecture will unlock extraordinary new capabilities for Microsoft customers.”
— Harry Shum, executive vice president of Microsoft AI and Research Group, Microsoft

“Oak Ridge National Laboratory will begin assembling our next-generation leadership computing system, Summit, this summer. Summit is powered by Volta GPUs and will be the top supercomputer in the U.S. for scientific discovery when completed in 2018. It will keep the U.S. at the forefront of scientific research and help the Department of Energy address complex challenges with computational science and AI-assisted discovery.”
— Jeff Nichols, associate laboratory director of the Computing and Computational Sciences Directorate, Oak Ridge National Laboratory

“A large variety of our products, including voice technology in wechat, photo/video technology in QQ and Qzone, and the deep learning platform based on Tencent Cloud, already rely on AI. We believe Volta will provide unprecedented computing power for our AI developers, and we’re excited to open up those capabilities soon from Tencent Cloud to more clients.”
— Dowson Tong, senior executive vice president, Tencent

About NVIDIA

NVIDIA‘s (NASDAQ: NVDA) invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. More information at http://nvidianews.nvidia.com/.

Source: NVIDIA

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NCSA’s Blue Waters Project Provides $1.08 Billion Direct Return to Illinois’ Economy

Wed, 05/10/2017 - 11:56

URBANA, Ill., May 10, 2017 — The National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign released a study Wednesday detailing the current and projected economic impact of its Blue Waters Project on Illinois’ economy. The project manages the nation’s most powerful sustained-performance supercomputer for open-science and its related workforce development and education program. The study finds that the Blue Waters project—which is a joint investment between the State of Illinois, University of Illinois, National Science Foundation (NSF), and related activities funded by the university, NSF and other federal agencies—has a projected $1.08 billion direct economic impact on Illinois’ economy and will have created 5,772 full-time equivalent employment over the project’s lifespan (October 2007 – June 2019).

Conducted by Dr. Sandy Dall’erba, Director of the Regional Economics Applications Laboratory and Associate Professor of Agricultural and Consumer Economics (ACES) at the University of Illinois at Urbana-Champaign, the study calculated in 2015 values the project’s impact on the creation of economic output (production), employment (Full Time Equivalent, FTE), labor income, local and state tax revenues, and federal tax revenues. The $1.08 billion in economic impact includes $487,143,813 in labor income from 5,772 FTEs, $56,477,093 in state and local taxes, $122,813,903 in federal taxes, and a $227,300,00 impact resulting from research grants awarded from granting agencies to Illinois researchers, faculty, and students because they had access to conduct research on Blue Waters. The study does not include additional economic and societal benefits coming from the significant amount of computer time provided to Illinois researchers, strategic projects, and industry, nor does it account for the workforce development activities of the Blue Waters project—as they are a magnet for recruiting expert talent to Illinois—or the impacts of the science, engineering and research results that can only be accomplished on the Blue Waters.

Another way to express these results is through a multiplier, a measure of the “bang for the buck.” Over the project’s lifespan (October 2007 – June 2019), Blue Water’s output multiplier is 1.864 (every $1 spent for BW-related activities leads to an additional $0.864 of production in the state economy) and its employment multiplier is 2.044 (every job created by BW-related activities leads to one additional job created in the state economy).

“My sincerest gratitude goes to the University of Illinois, the State of Illinois, and the National Science Foundation for supporting this critical project which is enabling us to better understand our world, improve quality of life, and develop the nation’s advanced digital workforce,” said Dr. William “Bill” Gropp, NCSA interim director and co-principal investigator for the Blue Waters Project. “State and federal support for advanced high-performance computing provides immediate economic impact for our communities and positions the United States to lead the world during a critical era for cyberinfrastructure and at the same time provide unique future contributions from the results Blue Waters enables.”

Illinois was awarded the $360 million Blue Waters Project through an NSF Request for Proposal process in 2007. The construction of the National Petascale Computing Facility (NPCF), a state-of-the-art computing and data center housing the system, was made possible by investments of $60 million by the State of Illinois and $87 million by the University of Illinois’ Urbana campus. The construction of the NPCF alone (July 2008-June 2012) generated about $131.7 million in total impact on Illinois’ economy, which includes the creation of 701 direct and indirect jobs, ranging from construction, to technical, to administration. The construction generated $4.3 million in local and state taxes and $9.6 million in federal taxes.

“Illinois is proud to have partnered with the National Science Foundation to support this bold endeavour to create leadership-class resources for researchers in Illinois and around the nation,” said Robert J. Jones, chancellor of the University of Illinois’ Urbana campus. “NCSA has once again proven its ability to excel at stewarding major infrastructure investments and scientific innovations. We intend to continue leading the nation in high-performance computing.”

The Blue Waters project, which has just completed its fourth full year of full service operations, is a key resource for recruiting and retaining world-class researchers and academic professionals. Since the project went online in April of 2013 until the study began in June 2016, it has supported 1,892 direct and indirect jobs and $177.9 million in labor income throughout the State of Illinois. The presence of Blue Waters during this period created a total of $20.9 million in local and state taxes and $41.8 million in federal taxes. The full operation and maintenance has generated a total impact of nearly $380.4 million, $227.3 million of which is due to research grants awarded to faculty with Blue Waters computing allocations.

The resulting overall direct economic impact sums to $1.08B for Illinois along with all the other benefits to the state, the university and nation the Blue Waters Project brings.

“I applaud NCSA’s for its successfully stewardship of Illinois’ financial support that has enabled this best-in-class education program and resources for Illinois students and researchers,” said Rep. Kelly Burke (D-36), chair of the Illinois House of Representatives Higher Education Appropriations Committee.

The report’s executive summary, including methodology, is publicly available for download here.

About the National Center for Supercomputing Applications

The National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign provides supercomputing and advanced digital resources for the nation’s science enterprise. At NCSA, University of Illinois faculty, staff, students, and collaborators from around the globe use advanced digital resources to address research grand challenges for the benefit of science and society. NCSA has been advancing one third of the Fortune 50 for more than 30 years by bringing industry, researchers, and students together to solve grand challenges at rapid speed and scale.

About NCSA’s Blue Waters Project

Blue Waters Petascale Supercomputer is one of the most powerful supercomputers in the world, and is the fastest supercomputer on a university campus. Blue Waters uses hundreds of thousands of computational cores to achieve peak performance of more than 13 quadrillion calculations per second. Blue Waters has more memory and faster data storage than any other open system in the world. Scientists and engineers across the country use the computing and data power of Blue Waters to tackle a wide range of challenges. Recent advances that were not possible without these resources include computationally designing the first set of antibody prototypes to detect the Ebola virus, simulating the HIV capsid, visualizing the formation of the first galaxies and exploding stars, and understanding how the layout of a city can impact supercell thunderstorms.

The Blue Waters sustained-petascale computing project which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.

Source: NCSA

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ACM Recognizes Visionaries Who Changed the World through Technology

Wed, 05/10/2017 - 11:50

NEW YORK, May 10, 2017 – ACM, the Association for Computing Machinery, today honored the dedication, talent and achievements of four luminaries of the international computing community. Working in diverse areas, the 2016 award recipients were selected by their peers for longstanding efforts that have had far-reaching impact. This year’s ACM award recipients made contributions in areas including computer science education, technology in the developing world, preserving and sharing computing history, and supporting women in the computing field. They will be formally honored at the ACM Awards Banquet on June 24 in San Francisco.

The 2016 Award Recipients include:

Owen Astrachan, recipient of the Karl V. Karlstrom Outstanding Educator Award for three decades of innovative computer science pedagogy and inspirational community leadership in broadening the appeal of high school and college introductory computer science courses. Astrachan, a Professor at Duke University, is known as “Mr. AP” because of the central role he has played in the Advanced Placement Computer Science exam taken by high school students. From 1985 to 1989, he served on the committee that writes the AP CS exam, and from 1989 to 1994 he was the Chief Reader, the person in charge of grading the exam. Over his three decades of involvement, Astrachan also played a critical role as the exam’s language changed from Pascal to C++, and later to Java, the language it is given in today.

His broad knowledge of the field, and the respect he garnered within the computer science education community, made him a natural candidate to be the Principal Investigator (project lead), in a 10-year National Science Foundation-funded effort to develop an AP CS Principles course and exam. An important goal of the AP CS Principles exam is to encourage participation in computer science by traditionally underrepresented student communities. The first AP CS Principles courses were offered in the fall of 2016 and the first exam was administered on May 5, 2017 to over 50,000 students—the largest first-year AP exam administration ever.

Astrachan has also made important contributions in several other areas of computer science pedagogy at the K-12/pre-college and college level. Many regard Tapestry, his introductory textbook for C++, as one of the best in the field. His extensive publications and talks on subjects ranging from object-oriented programming to software engineering instruction have also been highly influential. His role in advancing understanding computer science at every level has been strongly influenced by the community of students, teachers, and educators from whom he has learned and with whom he has shared so much.

The Karl V. Karlstrom Outstanding Educator Award is presented annually to an outstanding educator who is appointed to a recognized educational baccalaureate institution.  The recipient is recognized for advancing new teaching methodologies; effecting new curriculum development or expansion in Computer Science and Engineering; or making a significant contribution to the educational mission of ACM. Those with 10 years or less teaching experience are given special consideration. A prize of $10,000 is supplied by Pearson Education.

Ken Banks, recipient of the Eugene L. Lawler Award for developing FrontlineSMS, using mobile technology and text messaging to empower people to share information, organize aid, and reconnect communities during crises. A self-described “mobile anthropologist,” Banks has a gift for building technology that benefits humanity. As someone who was writing code and tinkering with computers since he was 13, Banks instinctively saw an opportunity to harness the world’s most-used communication platform—mobile messaging—to help people in the developing world.  In 2005, he designed, coded and launched FrontlineSMS, a mobile messaging platform that allows people to subscribe to groups, receive alerts, and establish communication hubs. FrontlineSMS played an important role in the 2007 Nigerian presidential election, where it was used to monitor, identify and curtail violence and harassment at polling places. Because of FrontlineSMS’s built-in flexibility, it quickly became a standard platform deployed in 170 countries by countless organizations. It has been used to help family farmers in Laos, train rural medics in Ecuador, double the amount of patients receiving tuberculosis care in Malawi, and monitor disease outbreaks across Africa.

Banks continues his work as an Entrepreneur-in-Residence at CARE International. To achieve CARE’s goal of raising more people above the $2 per day extreme poverty level, Banks is leading the development of a new mobile phone application for Village Savings and Loan Associations. Along with his colleagues at CARE, he is also investigating how technology might be used to advance gender equality in Rwanda. His two books The Rise of the Reluctant Innovator and Social Entrepreneurship and Innovation recount his experiences—and those of other global social innovators—and serve as guides for those who are inspired to leverage technology for positive social change.

The Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informaticsrecognizes an individual or group who has made a significant contribution through the use of computing technology. It is given once every two years, assuming that there are worthy recipients. The award is accompanied by a prize of $5,000.

Leonard Jay Shustek, recipient of the ACM Distinguished Service Award for the establishment and success of the Computer History Museum, the world’s leading institution in exploring the history of computing and its impact on society.  Shustek has helped bring to the world the story of how the greatest innovation of our time has come to be. In 1995, after retiring from the network diagnostic company he co-founded, Shustek began teaching computer architecture at Stanford University. He soon realized that students were as interested in computer history as they were in computer architecture. Instead of returning to Stanford, he began a quest that would ultimately lead him to acquire a group of artifacts from The Computer Museum in Boston, with an eye toward forming a new computer history museum in the heart of Silicon Valley.

Today, thanks to the leadership, vision and tenacity of Shustek, the Computer History Museum (CHM) is acknowledged as the world’s most important museum chronicling the rise of computing and its impact on society. With a staff of 75 serving 200,000 visitors each year, CHM has realized Shustek’s founding goal of an organization that would be “built to last.” CHM is housed in a complex comprised of a 119,000-square-foot building for exhibits and hands-on labs; a 25,000-square-foot climate-controlled warehouse for papers and artifacts; and a new 50,000-square-foot research center for scholars and archival work. Throughout the museum’s growth and development, Shustek has engaged in a range of activities, from leading the museum in raising $125 million to tracking down vintage code related to operating systems no longer in use.

For the general public, signature exhibitions like “Revolution” translate the history of computing into an experience that the average person can not only appreciate, but enjoy. For the computing field, CHM’s role as the world’s major repository of artifacts and historic preservation allows innovators to access the past, in order to move into the future.

The ACM Distinguished Service Award is presented on the basis of value and degree of services to the computing community. The contribution should not be limited to service to the Association, but should include activities in other computer organizations and should emphasize contributions to the computing community at large.

Valerie Barr, recipient of the Outstanding Contribution to ACM Award for reinventing ACM-W, increasing its effectiveness in supporting women in computing worldwide and encouraging participation in ACM. Barr, a Professor at Union College, has been uniquely effective in turning good ideas about how to increase the participation of women in computing into tangible programs that yield measurable results.  When she first joined the Association for Computing Machinery’s Council on Women in Computing (ACM-W) in 2005, she launched a scholarship program. Barr and others believed that if more young women could attend major computer research conferences, they would be encouraged to continue in the field. Since its inception in 2006, the program has expanded the horizons of numerous young women internationally and has continued to grow. Because of Barr’s adeptness at conveying her vision to funders, the program is 100% supported by industry contributions. Last year, the ACM-W Scholarship Program distributed $40,000 over 40 awards.

ACM-W’s dedicated and hard-working volunteers share a central goal of bringing women together for mentoring, networking, and other career-enhancing activities. Since becoming the Chair of ACM-W in 2012, Barr has been a driving force in more than tripling the number of ACM-W chapters around the world, from 50 to 180 today. One strategy that led to this growth was the introduction of special networking events in which colleges and universities with ACM-W chapters would invite students from neighboring colleges and encourage them to establish ACM-W chapters on their own campuses.   ACM-W Councils in Europe and India oversee activities in their respective regions.  Another notable area of growth during Barr’s tenure as Chair has been a significant increase in ACM-W Celebrations, small conferences in which women from specific geographic regions come together for career fairs, industry panels and technical presentations. Celebrations events have expanded to Cuba and the Philippines, and one event is tailored specifically for community college students. Last year, 25 ACM-W Celebrations took place around the world.

ACM-W members especially look forward to Connections, a monthly newsletter Barr instituted that is sent to 36,000 people each month and is considered a “must read.”

The Outstanding Contribution to ACM Award recognizes outstanding service contributions to the Association. Candidates are selected based on the value and degree of service overall, and may be given to up to three individuals each year.

About ACM

ACM, the Association for Computing Machinery, is the world’s largest educational and scientific computing society, uniting educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. ACM strengthens the computing profession’s collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.

Source: ACM

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Cancer Treatments Get a Supercomputing Boost

Wed, 05/10/2017 - 11:14

AUSTIN, May 10, 2017 — Radiation therapy shoots high-energy particles into the body to destroy or damage cancer cells. Over the last century, the technologies used have constantly improved and it has become a highly effective way to treat cancer. However, physicians must still walk a fine line between delivering enough radiation to kill tumors, while sparing surrounding healthy tissue.

“Historically, radiation has been a blunt tool,” said Matt Vaughn, Director of Life Science Computing at the Texas Advanced Computing Center. “However, it’s become ever more precise because we understand the physics and biology of systems that we’re shooting radiation into, and have improved our ability to target the delivery of that radiation.”

The science of calculating and assessing the radiation dose received by the human body is known as dosimetry – and here, as in many areas of science, advanced computing plays an important role.

Improving Radiation Therapy with Real-Time Imaging

Current radiation treatments rely on imaging from computed tomography (CT) scans taken prior to treatment to determine a tumor’s location. This works well if the tumor lies in an easily detectable and immobile location, but less so if the area is moving, as in the case of lung cancer.

Illustration of the MR-linac. The inner ring is the MRI bore which performs the imaging of the patient. The outer ring is the gantry on which the linear accelerator (linac) that produces the radiation for treatment is mounted. The linac gantry can rotate completely around. In the center is the bed where the patient would lie. [Courtesy: Elekta]

At the University of Texas MD Anderson Cancer Center, scientists are tackling the problem of accurately attacking tumors using a new technology known as an MR-linac that combines magnetic resonance (MR) imaging with linear accelerators (linacs). Developed by Elekta in cooperation with UMC Utrecht and Philips, the MR-linac at MD Anderson is the first of its kind in the U.S.

MR-linacs can image a patient’s anatomy while the radiation beam is being delivered. This allows doctors to detect and visualize any anatomical changes in a patient during treatment. Unlike CT or other x-ray based imaging modalities, which provide additional ionizing radiation, MRI is harmless to healthy tissue.

The MR-linac method offers a potentially significant improvement over current image-guided cancer treatment technology. However, to ensure patients are treated safely, scientists must first correct for the influence of the MRI’s magnetic field on the measurements used to calibrate the radiation dose being delivered.

Researchers use software called Geant4 to simulate radiation within the detectors. Originally developed by CERN to simulate high energy particle physics experiments, the MD Anderson team has adapted Geant4 to incorporate magnetic fields into their computer dosimetry model.

“Since the ultimate aim of the MR-linac is to treat patients, it is important that our simulations be very accurate and that the results be very precise,” said Daniel O’Brien, a postdoctoral fellow in radiation physics at MD Anderson. “Geant4 was originally designed to study radiation at much higher energies than what is used to treat patients. We had to perform tests to make sure that we had the accuracy that we needed.”

Using the Lonestar supercomputer at the Texas Advanced Computing Center (TACC), the research team simulated nearly 17 billion particles of radiation per detector to get the precision that they needed for their study.

In August 2016, they published magnetic field correction factors in Medical Physics for six of the most-used ionization chamber detectors (gas-filled chambers that are used to ensure the dose delivered from a therapy unit is correct). They are now working on verifying these results experimentally.

“The MR-linac is a very promising technology but it also presents many unique challenges from a dosimetry point of view,” O’Brien said. “Over time, our understanding of these effects has improved considerably, but there is still work to be done and resources like TACC are an invaluable asset in making these new technologies safe and reliable.”

“Our computer simulations are important because their results will serve as the foundation to extend current national and international protocols to perform calibration of conventional linacs to MR-linacs,” said Gabriel Sawakuchi, assistant professor of Radiation Physics at MD Anderson. “However, it is important that our results be validated against measurements and independent simulations performed by other groups before used clinically.”

(The project was partially funded by Elekta, a Swedish company that provides radiation therapy equipment and clinical management for the treatment of cancer and brain disorders.)

Read the full release at https://www.tacc.utexas.edu/-/targeted-high-energy-cancer-treatments-get-a-supercomputing-boost

Source: Aaron Dubrow, TACC

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DOE HPC4Mfg Program Funds 13 New Projects

Tue, 05/09/2017 - 20:57

May 9, 2017 — Today, the U.S. Department of Energy announced nearly $3.9 million for 13 projects designed to stimulate the use of high performance supercomputing in U.S. manufacturing. The Office of Energy Efficiency and Renewable Energy (EERE) Advanced Manufacturing Office’s High Performance Computing for Manufacturing (HPC4Mfg) program enables innovation in U.S. manufacturing through the adoption of high performance computing (HPC) to advance applied science and technology relevant to manufacturing. HPC4Mfg aims to increase the energy efficiency of manufacturing processes, advance energy technology, and reduce energy’s impact on the environment through innovation.

The 13 new project partnerships include application of world-class computing resources and expertise of the national laboratories including Lawrence Livermore National Laboratory, Oak Ridge National Laboratory, Lawrence Berkley National Laboratory, National Renewable Energy Laboratory, and Argonne National Laboratory. These projects will address key challenges in U.S. manufacturing proposed in partnership with companies and improve energy efficiency across the manufacturing industry through applied research and development of energy technologies.

Each of the 13 newly selected projects will receive up to $300,000 to support work performed by the national lab partners and allow the partners to use HPC compute cycles.

The 13 projects selected for awards are led by:

  • 7AC Technologies
  • 8 Rivers Capital
  • Applied Materials, Inc.
  • Arconic Inc.*
  • Ford Motor Company
  • General Electric Global Research Center*
  • LanzaTech
  • Samsung Semiconductor, Inc.
  • Sierra Energy
  • The Timken Company
  • United Technologies Research Corporation

*Awarded two projects

Read more about the individual projects.

The Advanced Manufacturing Office (AMO) recently published a draft of its Multi-year Program Plan that identifies the technology, research and development, outreach, and crosscutting activities that AMO plans to focus on over the next five years. Some of the technical focus areas in the plan align with the high-priority, energy-related manufacturing activities that the HPC4Mfg program also aims to address.

Led by Lawrence Livermore National Laboratory, with Lawrence Berkeley National Laboratory and Oak Ridge National Laboratory as strong partners, the HPC4Mfg program has a diverse portfolio of small and large companies, consortiums, and institutes within varying industry sectors that span the country. Established in 2015, it currently supports 28 projects that range from improved turbine blades for aircraft engines and reduced heat loss in electronics, to steel-mill energy efficiency and improved fiberglass production.

Source: U.S. Department of Energy/HPC4Mfg

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Dell EMC Adds Omni-Path Support to PowerEdge C6320p

Tue, 05/09/2017 - 15:10

A recent update by Dell EMC adds support for Intel’s high performance Omni-Path fabric on the PowerEdge C6320p modular server intended for HPC workloads. Notice of the change came in a blog post today by Steve Cochran of Dell EMC’s HPC group.

“This update is a processoronly change, which means that changes to the PowerEdge C6320p motherboard were not required,” wrote Cochran on the blog (Dell EMC HPC System for Research – Keeping it Fresh). “New purchases of the PowerEdge C6320p server can be configured with KNL or KNL-F processors. For customers utilizing Omni-Path as a fabric, the KNL-F processor will improve cost and power efficiencies, as it eliminates the need to purchase and power discrete Omni-Path adapters.”

Late last year, Dell EMC introduced PowerEdge C6320p Server “which delivers a high performance processor node based on the Intel Xeon Phi processor (KNL).” The C6320p is optimized for HPC workloads, supporting highly parallelized processes with up to 72 out-of-order cores in a compact half-width 1U package. Highspeed fabric options include InfiniBand or Omni-Path, ideal for data intensive computational applications, such as life sciences, and weather simulations.

As seen in the figure below (taken from the blog, click to enlarge), the integrated fabric option eliminates the dependency on dual x16 PCIe lanes on the motherboard and allows support for a denser configuration, with two QSFP connectors on a single carrier circuit board. For continued support of both processors, the PowerEdge C6320p server will retain the PCIe signals to the PCIe slots. Inserting the KNL-F processor will disable these signals, and expose a connector supporting two QSFP ports carried on an optional adapter using the same PCIe x16 slot for power.

Cochran reports that additional improvements to the PowerEdge C6320p server include support for 64GB LRDIMMs, bumping memory capacity to 384GB, and support for the LSI 2008 RAID controller via the PCIe x4 mezzanine slot.

Dell EMC c6230p diagram

Dell EMC offers several HPC solutions optimized for customer usage and priorities.  Domainspecific HPC compute solutions from Dell EMC include the following scalable options:

  • HPC System for Life Sciences – A customizable and scalable system optimized for the needs of researchers in the biological sciences.
  • HPC System for Manufacturing – A customizable and scalable system designed and configured specifically for engineering and manufacturing solutions including design simulation, fluid dynamics, or structural analysis.
  • HPC System for Research – A highly configurable and scalable platform for supporting a broad set of HPCrelated workloads and research users.

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Two Cray Supercomputers Join Living Computers Museum

Tue, 05/09/2017 - 15:02

SEATTLE, May 9, 2017 — Living Computers: Museum + Labs adds two of the most important supercomputers ever created to its permanent collection. The Cray-1 supercomputer goes on display at Living Computers beginning Tuesday, May 9th and will be joined by the Cray-2 supercomputer later this year. Living Computers intends to recommission the Cray-2 and make it available to the public.

The world’s first supercomputer, the 1965 Control Data Corporation 6000 series, was designed by the brilliant engineer Seymour Cray and represented a threefold increase in computing power. Living Computers, which has the world’s only operating CDC 6500, wished to add to their collection by obtaining Cray’s next invention, the Cray-1 supercomputer. Released in 1976 from Cray Research Inc. in Seymour’s hometown of Chippewa Falls Wisconsin, the Cray-1 was a fixture at elite labs and universities. Originally priced between $5M and $8M, over 80 Cray-1s were sold. The Cray-1 Serial #12 was bought by the University of Minnesota with a custom maroon and gold color scheme before it was transferred to the spin-off Minnesota Supercomputing Center. This computer was just moved from Cray’s St. Paul office to Seattle, WA – the current headquarters of Cray Inc. – to be placed on display at Living Computers.

As a follow-up to the Cray-1, Seymour designed a liquid cooled computer made from dense stacks of circuit boards immersed in coolant, thus, the Cray-2 (nicknamed “Bubbles”) was born. Setting the new standard in supercomputer performance at 1.9 gigaflops, the Cray-2 held the title of fastest computer in the world from its release in 1985 until 1990. Living Computers has acquired the world’s most complete and advanced Cray-2, a 4 Core 512MW version. This is the last Seymour Cray-designed computer to be operating and was taken out of service at the Minnesota Super Computing Center in 1992.

“I honestly can’t overstate how important these two supercomputers are to computing history, and I am thrilled to be adding them to our collection,” says Lath Carlson, Executive Director of Living Computers. “Bringing these milestones back from the depths of storage has been an incredible journey, and we look forward to making them available to the public.” Living Computers will also host a private reception for the Cray User Group on May 9th to celebrate the new home of these Cray supercomputers.

“The Cray-1 and the Cray-2 supercomputers are an integral part of our company’s history, and played a critical role in the establishment of the supercomputing industry and the development of Cray as a company,” said Barry Bolding, chief strategy officer at Cray. “Supercomputing is more relevant than ever in today’s world of big data and AI, and a clear understanding of our past empowers our visions for future technologies. Living Computers serves an important function for computing technology enthusiasts, and we couldn’t be happier that these two pioneering pieces of Cray history will be on display for all to see.”

About Living Computers: Museum + Labs

Living Computers: Museum + Labs provides a one-of-a-kind, hands-on experience with computer technology from the 1960s to the present. LCM+L honors the history of computing with the world’s largest collection of fully restored and usable supercomputers, mainframes, minicomputers and microcomputers. A new main gallery offers direct experiences with robotics, virtual reality, artificial intelligence, self-driving cars, big data, the Internet of Things, video-game making, and digital art. The main floor also features education labs for learning new skills. Learn more at LivingComputers.org. Come in. Geek out.

About Cray Inc.

Global supercomputing leader Cray Inc. (Nasdaq:CRAY) provides innovative systems and solutions enabling scientists and engineers in industry, academia and government to meet existing and future simulation and analytics challenges. Leveraging more than 40 years of experience in developing and servicing the world’s most advanced supercomputers, Cray offers a comprehensive portfolio of supercomputers and big data storage and analytics solutions delivering unrivaled performance, efficiency and scalability. Cray’s Adaptive Supercomputing vision is focused on delivering innovative next-generation products that integrate diverse processing technologies into a unified architecture, allowing customers to meet the market’s continued demand for realized performance. Go to www.cray.comfor more information.

Source: Cray

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Envenio & Pointwise Join Forces On-Demand

Tue, 05/09/2017 - 11:34

May 9, 2017 — Envenio and Pointwise, Inc. have signed a co-licensing agreement to offer both products, EXN/Aero and Pointwise, on-demand, on HPC cloud-host Nimbix. This represents a step-change in the way CFD engineers can access and utilize high performance meshing & simulation tools.

Why On-Demand?

In a previous blog, Envenio discussed the limitations facing engineering consultants in their day-to-day lives. Most notably, these limitations affected areas of growth, their ability to compete, and cash-flow, all factors that can directly influence project decisions and business growth. Traditional commercial tools and traditional compute resources have previously been inflexible and expensive, restricting flexibility of consultants and potentially limiting the services they can offer clients. Costly annual licensing subscriptions are now a thing of the past, thanks to new on-demand pay-as-you-go tools.

By being able to access on-demand meshing, simulation tools and post-processing tools, engineers can expand their service offering, exceed client expectations, and keep up with their competition. With these engineering simulation tools, consultants enjoy a significant reduction in meshing time and simulation solve time. Being able to list end-to-end CFD services in marketing and proposal materials also enables consultants to compete with larger organizations. Moreover, engineers are able to:

  • Upsell existing clients on larger or more complex models
  • Offer a higher through-put of meshes and simulations to prospective clients
  • Offer shorter turn-around times on simulations
  • Upsell existing customers on high resolution meshes / models
  • Supplement their existing meshing and simulation workflows using the on-demand tools for additional or more challenging runs
  • Bid more competitively on projects
  • Provision compute resources on the fly to scale with customer demands

In instances where a client’s needs increase, the use of affordable on-demand cloud CFD tools adds a level of flexibility, enabling consultants to instantly access high performance meshing and simulation resources.

While open source software is a good option for consultants, the lack of urgent technical support can be troublesome.

Financial restrictions are particularly common when it comes to meshing tools and CFD software, and operating a CFD tool on more than 4 CPUs can become eye-wateringly expensive. In some cases, vendors charge over $1,000 per additional core, and it’s worth noting that even fellow “on-demand” providers still require annual license subscriptions in some cases. This is not the case with Envenio, and users protect their cash flow by paying for usage only every 30 days.

With on-demand CFD tools being so in-demand, the alliance between Envenio and Pointwise, Inc. marks an exciting time. If you are interested in a free trial of the on-demand platform, click here.

About Pointwise, Inc. 

Pointwise, Inc. is solving the top problem facing computational fluid dynamics (CFD) today – quickly generating high-fidelity meshes. The company’s Pointwise software generates structured, unstructured, overset and hybrid meshes; runs on Windows, Linux, and Mac, and has a scripting language, Glyph, that can automate CFD meshing. Large manufacturing firms and research organizations worldwide have relied on Pointwise as their complete CFD preprocessing solution since 1994.

More information about Pointwise is available at www.pointwise.com.

Pointwise is a registered trademark of Pointwise, Inc. in the USA and the EU. Pointwise Glyph, T-Rex and Let’s Talk Meshing are trademarks of Pointwise, Inc. All other trademarks are property of their respective owner.

The Envenio & Pointwise, Inc. Agreement

This agreement will enable consultants to access both EXN/Aero and Pointwise on-demand. By making software more accessible, engineers are free to progress their research and expand their service offering, increasing the widespread use of CFD. Furthermore, if talented individuals have access to modern software, the potential for innovation across a number of sectors is increased, spelling an exciting future ahead.

“We are delighted to have teamed up with Pointwise, Inc.,” says VP of Envenio, Scott Walton. “We share many of the same values, and are absolutely passionate about ensuring engineering consultants have access to the best CFD tools. This alliance reflects our overall mission to break down barriers and limitations that can prohibit the work of consultants, providing them with flexible access to software that has historically only been available to the largest corporations and organizations. We have used Pointwise internally for over 7 years and believe it is the highest performance meshing tool available” he added.

“This is our first foray into cloud-based preprocessing so we are happy to have an experienced team like Envenio by our side,” says Dr. Rick Matus, Pointwise executive vice president. “At first we were skeptical about getting the graphics performance needed for a highly interactive product like Pointwise while working with a remote cloud-server. Our testing with Envenio in the Nimbix environment showed very good results. Users should enjoy the flexibility of on-demand meshing combined with Pointwise’s ease-of-use and high-quality. We are excited to join Envenio in providing this to the engineering community.”

The High Performance Engineer
Combining a powerful CFD meshing tool and solver with the ability to access it on the cloud and the ability to pay only for what you use enables you to compete, grow, and better manage your cash-flow. Many users run a test case to get a sense of the interface, workflow, and speed up, if you are interested please follow this link. For more information on pricing, visit our pricing page.

Source: Envenio

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ScaleMatrix Announces GPU-as-a-Service Solution

Tue, 05/09/2017 - 11:21

SAN DIEGO, Calif., May 9, 2017 — ScaleMatrix, a leading provider of customer-premise and hosted cloud services for HPC and Big Data workloads delivered on the industry’s leading Dynamic Density Control (DDC) enclosure platform, today announced a GPU-as-a-Service solution with the dense Graphics Processing Unit (GPU) power to deliver unprecedented efficiency gains at an affordable price point for today’s demanding HPC, Deep Learning and Artificial Intelligence (AI) workloads.

“We are excited to offer our customers this GPU-as-a-Service option to support their deep learning, AI and HPC needs,” said Chris Orlando, Co-Founder of ScaleMatrix.  Orlando continues, “This solution provides organizations a better, faster and less expensive alternative to Amazon P2 Instance at a cost-effective price/performance point that will help them accelerate their workloads and be competitive in today’s business environment.”

With GPU-as-a-Service, ScaleMatrix supplies the servers as a bare-metal cloud offering with flexible payment options and short-term use models for GPUs on premium servers.  The solution delivers dedicated direct access with the compliance and security users needs without the cloud or virtualization overhead. The system specifications range from 4-8 GPUs, with up to dual Intel Xeon processor E5-2630 v4, between 64-128GB RAM and a 1TB SSD NVMe for the primary drive, and 4TB SATA as a secondary drive.  The GPU-as-a-Service solutions start as low as $1,464 per month and scale to meet the needs of the consumer’s growing demand for intense GPU compute processing.

In addition, ScaleMatrix delivers up to a 30% improvement on Power Usage Effectiveness (PUE) which is critical to managing the energy efficiency of computing equipment – the lower the PUE rating, the better. With GPU-as-a-Service, ScaleMatrix offers technical expertise in the areas of HPC, Deep/Machine Learning and AI with our Matrix Totalcare service that provides seasoned data center experts who take the guesswork out of remote hands support and ensure timely responses to all technical inquiries, big or small.

ScaleMatrix is demonstrating this new solution at the GPU Technology Conference (GTC 2017) being held May 9-11 at the San Jose Convention Center. Visit us in Booth #528 to learn more about GPU-as-a-Service and our Dynamic Density Control platform that solves density, efficiency, and cooling challenges for demanding compute- and data-intensive applications.

Source: ScaleMatrix

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