New Intel Technologies Highlighted in SC16 Announcements

New Intel Technologies Highlighted in SC16 Announcements

By Unknown   21:39   No comments:

"Updates for Intel® Xeon® processors, Intel® HPC Orchestrator, Intel® Deep Learning Inference Accelerator and other forthcoming supercomputing technologies available soon"

SC16 revealed several important pieces of news for supercomputing experts. In case you missed it, here’s a recap of announced updates from Intel that will provide even more powerful capabilities to address HPC challenges like energy efficiency, system complexity, and the ability for simplified workload customization. In supercomputing, one size certainly does not fit all. Intel’s new and updated technologies take a step forward in addressing these issues, allowing users to focus more on their applications for HPC, not the technology behind it.

intellogoIn 2017, developers will welcome a next generation of Intel® Xeon® and Intel® Xeon Phi™ processors. As you would expect, these updates offer increased processor speed and more through improved technologies under the hood. The next generation Intel Xeon Phi processor (code name “Knights Mill”) will exceed its predecessor’s capability with up to four times better performance in deep learning scenarios1.

Of course, as developers know, the currently-shipping Intel Xeon Phi processor (formerly known as “Knights Landing”) is no slouch! Nine systems utilizing this processor now reside on the TOP500 list. Of special note are the Cori (NERSC) and Oakforest-PACS (Japan Joint Center for Advanced High Performance Computing) supercomputing systems with both claiming a spot among the Top 10.

The next-generation Intel Xeon processor (code name “Skylake”) is also expected to join the portfolio in 2017. Demanding applications involving floating point calculations and encryption will benefit from both Intel® Advanced Vector Instructions-512, and Intel® Omni-Path Architecture (Intel® OPA). These improvements will further streamline the processor’s capability, giving commercial, academic and research institutions another step forward against taxing workloads.

A third processing technology anticipated in 2017 enables an additional level of HPC customization. The combined hardware and software solution, known as Intel® Deep Learning Inference Accelerator, sports a field-programmable gate array (FPGA) at its heart. By maximizing industry standard frameworks like Intel® Distribution for Caffe* and Intel® Math Kernel Library for Deep Neural Networks too, the solution provides end users opportunity for even greater flexibility in their supercomputing applications.

intelcircleAt SC16, Intel also highlighted supplemental momentum for Intel® Scalable System Framework (Intel SSF). HPC is an essential tool for advances in health-related applications, and Intel SSF is taking a place center-stage as a mission-critical tool in those scenarios as Intel demonstrated in its SC16 booth. Dell* offers Intel SSF for supercomputing scenarios involving drug design and cancer research. Other applications like genomic sequencing create a challenge for any supercomputer. For this reason, Hewlett Packard Enterprise* (HPE) taps Intel SSF as a core component of the HPE Next Generation Sequencing Solution.

Additional performance isn’t the only thing supercomputing experts need, though. Feedback from HPC developers, administrators and end-users express the need for improved tools during critical phases of system setup and usage. Help is on the way. Now available, Intel® HPC Orchestrator based upon the OpenHPC software stack addresses that feedback. With over 60 features integrated, it assists with testing at full-scale, deployment scenarios, and simplified systems management. Currently available through Dell* and Fujitsu*, Intel HPC Orchestrator should provide added momentum for the democratization of HPC.

Demonstrating further momentum, Intel Omni-Path Architecture has seen quite an uptick in adoption since release nine months back. It is utilized in about 66 percent of TOP500 HPC systems utilizing 100Gbit interconnects.

With so many technical advancements on the horizon, 2017 is shaping up as a year for major changes in the HPC industry. We are excited see how researchers, developers and others will utilize the technologies to take their supercomputing systems to the next level of performance, and tackle problems which were impossible just a few years ago.

1 For more complete information about performance and benchmark results, visit www.intel.com/benchmarks

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The Core Technologies of Deep Learning

The Core Technologies of Deep Learning

By Unknown   06:30   No comments:

When the movie The Terminator was released in 1984, the notion of computers becoming self-aware seemed so futuristic that it was almost difficult to fathom. But just 22 years later, computers are rapidly gaining the ability to autonomously learn, predict, and adapt through the analysis of massive datasets. And luckily for us, the result is not a nuclear holocaust as the movie predicted, but new levels of data-driven innovation and opportunities for competitive advantage for a variety of enterprises and industries.

Artificial intelligence (AI) continues to play an expanding role in the future of high-performance computing (HPC). As machines increasingly become able to learn and even reason in ways similar to humans, we’re getting closer to solving the tremendously complex social problems that have always been beyond the realm of compute. Deep learning, a branch of machine learning, uses multi-layer artificial neural networks and data-intensive training techniques to refine algorithms as they are exposed to more data. This process emulates the decision-making abilities of the human brain, which until recently was the only network that could learn and adapt based on prior experiences.

Deep learning networks have grown so sophisticated they’ve begun to deliver even better performance than traditional machine learning approaches. One advantage of deep learning is that there is little need to "train" the system and define features that might be useful for modeling and prediction. With only basic labeling, machines can now learn these features independently as more data is introduced to the model. Deep learning has even begun to surpass the capabilities and speed of the human brain in many areas, including image, speech, or text classification, natural language processing, and pattern recognition.

The core technologies required for deep learning are very similar to those necessary for data-intensive computing and HPC applications. Here are a few technologies that are well-positioned to support deep learning networks.

Multi-core processors:

Deep learning applications require substantial amounts of processing power, and a critical element to the success and usability of deep learning comes with the ability to reduce execution times. Multi-core processor architectures currently dominate the TOP500 list of the most powerful supercomputers available today, with 91% based on Intel processors. Multiple cores can run numerous instructions at the same time, increasing the overall processing speed for compute-intensive programs like deep learning, while reducing power requirements, increasing performance, and allowing for fault tolerance.

The Intel® Xeon Phi™ Processor, which features a whopping 72 cores, is geared specifically for high-level HPC and deep learning. These many-core processors can help data scientists significantly reduce training times and run a wider variety of workloads, something that is critical to the computing requirements of deep neural networks.

Software frameworks and toolkits:

There are various frameworks, libraries, and tools available today to help software developers train and deploy deep learning networks, such as Caffe, Theano, Torch, and the HPE Cognitive Computing Toolkit. Many of these tools are built as resources for those new to deep learning systems, and aim to make deep neural networks available to those that might be outside of the machine learning community. These tools can help data scientists significantly reduce model training times and accelerate time to value for their new deep learning applications.

Deep learning hardware platforms:

Not every server can efficiently handle the compute-intensive nature of deep learning environments. Hardware platforms that are purpose-built to handle these requirements will offer the highest levels of performance and efficiency. New HPE Apollo systems contain a high ratio of GPUs to CPUs in a dense 4U form factor, which enables scientists to run deep learning algorithms faster and more efficiently while controlling costs.

Enabling technologies for deep learning is ushering in a new era of cognitive computing that promises to help us solve the world’s greatest challenges with more efficiency and speed than ever before. As these technologies become faster, more available, and easier to implement, deep learning technologies will secure their place in real-world applications – not in science fiction.

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Datacenter Efficiencies Through Innovative Cooling

Datacenter Efficiencies Through Innovative Cooling

By Unknown   04:04   No comments:

Datacenters that are designed for High Performance Computing  (HPC) applications are more difficult to design and construct than those that are designed for more basic enterprise applications.  Organizations that are creating these datacenters need to be aware of, and design for systems that are expected to run at their maximum or near maximum performance for the lifecycle of the servers. While enterprise datacenters can be designed for less server density and less heat generated per server due to the type of workloads, HPC centers must be designed for higher usage per server. For example, simulations in many domains may run at peak performance (depending on the algorithms) for weeks at a time, while enterprise applications may only need peak performance for short bursts, such as payroll computations.

Two main buckets of expenses are usually associated with the planning and implementation of a new datacenter, or the upgrade of an existing datacenter to accommodate the latest generation and performance of new server technology. The first and more generally understood one is the Capital Expense, or CAPEX for short. CAPEX is the amount that the organization will pay to purchase a new asset, especially a new physical asset. When creating or upgrading a new datacenter, the CAPEX is the amount paid for the computer servers, racks, storage systems, networking equipment, etc., which will usually be paid once, in the current fiscal year. Many organizations focus on this value in determining the cost of acquiring new systems. However, the recurring cost or Operational Expense (OPEX), over the life of the computer systems will traditionally be higher.  Leading data center operators both understand this and know that reducing OPEX can provide competitive advantage and release budget dollars for investment in more computing capacity.

Reducing OPEX

OPEX is the sum of all of the expenses that an organization will have to pay to keep the servers running. This includes, but is not limited to such items as electricity, cooling (which includes the electricity), construction financing (if new construction was required), and maintenance.

One of the main costs in operating a data center is the cooling of the servers. When servers that are being used for HPC applications are running at full utilization, the CPUs produce more heat, than when waiting for work to be done. While the performance per watt of CPUs has increased dramatically over the past few decades, HPC installations are built to deliver the maximum performance of the system to the end user.  Today’s modern two socket high end HPC servers can approach a 1,000 watt requirement.  The electricity required for this type of server (while needed for the server in order to run and perform as expected) also includes a significant requirement for the power that is needed in order to cool the servers. CPUs have an envelope of operating temperatures that must be met, or the CPU will likely fail, often with a cascading impact on cluster throughput and reliability.

Datacenter design has focused in recent years on how to place racks and racks of servers in order to isolate and remove the heat that is produced by the servers, mainly the CPUs. Most servers today have been designed to have high speed and redundant fans in the back of the server, so that cool air can be pulled over the CPUS and heat sinks in order to cool them. This results in designing the data center to have hot aisles and cold aisles. For example, two rows of racks of systems may sit back to back. The cooler air from the front of the system is drawn over the hot chips into the hot aisle, and then powerful exhaust fans pull this hot air away from the hot aisles, and cooler air is returned to the cold aisle. Significant expense is required to contain the hot air in the hot aisle and to remove and cool the hot air.

An alternative is to provide cooling of the CPUs much closer to the CPU itself.  If a significant reduction in the hot air that is produced is achievable, then less Computer Room Air Conditioning (CRAC) is needed, reducing OPEX expenses. In addition, higher densities of the servers can be achieved, as less hot air is produced by each server into a given space (the hot aisle). The monetary effect of reducing power consumption is directly related to the overall OPEX. For example, reducing the power consumption by 50 %, can lead to a reduction of 20 + % in total data center power savings.

Asetek In-RackCDU D2C 

Asetek specializes in liquid cooling systems for data centers, servers, workstations, gaming and high performance PCs. For HPC installations, Asetek RackCDU D2C™ (Direct-to-Chip) applies liquid cooling technology directly on the chip itself. Because liquid is 4,000 times better at storing and transferring heat than air, Asetek’s solutions provide immediate and measurable benefits to large and small data centers alike. The Asetek solution consists of a plate that is attached to the top of the processor and provides cold liquid to the chip itself. The hot liquid is then pumped away from the CPU where the liquid can then be chilled. This reduces almost all of the heat from the airflow of the server, reducing the CRAC requirements, which reduces the OPEX accordingly.

While the exact OPEX savings will vary depending on CPU loads, electricity costs, number of servers, and server density per rack, using Asetek cooling products can significantly reduce the OPEX costs for small and large data centers. Asetek has designed a simple calculator to assist in computing the cost savings. It is well worth investigating innovative chip and server cooling solutions in order to keep an HPC datacenter running and producing results faster than previous generations of systems.

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Bright Computing Powers HPC Cluster at Oldenburg University

Bright Computing Powers HPC Cluster at Oldenburg University

By Unknown   03:57   No comments:

Today Bright Computing announced that Oldenburg University in Germany has once again chosen to renew its license agreement with Bright Computing.

Oldenburg first became a Bright Computing customer in 2011, choosing Bright Cluster Manager to administer its small HPC environment. In recent years the Oldenburg HPC system has grown to a considerable 600-node cluster to provide an increasing amount of compute power to various departments within the university. With a small IT team and limited administration resources available, when it came time to upgrade the university’s IT hardware, the IT Services team at Oldenburg took the decision to continue using the Bright infrastructure management technology to overarch the HPC environment.

“There were three compelling reasons for Oldenburg to choose to reinvest with Bright,” said Dr. Stefan Harfst, Oldenburg University. “Firstly, Bright helps you to get your HPC environment up and running very quickly. Secondly, Bright makes it incredibly easy to manage your HPC environment which takes a lot of pressure of the IT Services team. Thirdly, Bright is a very robust and reliable, so our team is free to focus on other tasks.”

During the evaluation process, Oldenburg considered a number of independent infrastructure management technologies, as well as some open source software. However, the IT Services team chose Bright for its superior set of features and functionality, acknowledging that investing in a tool rather than deploying freeware was easily justifiable.

Haroon Ibrahim, Account Director for Oldenburg at Bright Computing, added; “By Standardising on Bright, Oldenburg is no longer tied to a single hardware vendor and can now install any hardware it likes. Added to this, Bright will enable Oldenburg to continue to scale its cluster, and in the future expand into new areas such as big data.”

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Nor-Tech Introduces Portable HPC Clusters for the Oil & Gas Industry

Nor-Tech Introduces Portable HPC Clusters for the Oil & Gas Industry

By Unknown   03:51   No comments:

Oct. 11 — Nor-Tech (Northern Computer Technologies), the leading provider of HPC clusters and high performance workstations, just introduced the first portable HPC cluster for the oil and gas industry with a price point starting at $20,000. 

Nor-Tech designed the cluster with the understanding that the oil and gas industry is looking for providers:

• With deep FEA/CFD/CAE experience and an engineering staff that understands the concepts and challenges.
• With knowledge of all software integration options and assurance of agnostic recommendations.
• That have the willingness to listen to clients, the ability to understand their needs, and the ability to incorporate those needs into the design.
• That can deliver a superior cost/performance ratio and a competitive total cost of ownership.
• That have an excellent reputation within the HPC community, and
• That offer a strong product lineup backed by quality service and prompt support.

Nor-Tech President and CEO David Bollig said, “Many businesses in the oil and gas industry in particular have been putting off upgrading from a workstation to a cluster even though they know it will dramatically reduce modeling time. Part of the reason is the price and the other part is the anticipated disruption. We’ve been working with clients in just this situation for many years and have perfected the transition process. Our goal is to have clients up and running with their new cluster within a couple hours of delivery. We also back that up with no wait time support.”

Nor-Tech’s cluster was developed with dual goals of mitigating risk and maximizing the speed of obtaining accurate, actionable results at a price within reach of even the smallest oil and gas companies. The cluster aggregates, processes, and interprets a terabyte or more of data originating in simulations, surveys, historical geological sources, etc.

Often, as organizations need more processing power than a single workstation can provide, they will add another workstation. It seems like a cost-saving strategy at first, but as they continue to add workstations, they soon exceed the price of a single cluster—which would also have about 3x the processing power.

“We always recommend the most cost-effective solution for the long term,” Bollig said. “So we do offer high end workstations that may make more sense for start-ups still struggling with desktops.”

About Nor-Tech

2016 HPCwire award finalist, Nor-Tech is renowned throughout the scientific, academic, and business communities for easy to deploy turnkey clusters and expert, no wait time support. All of Nor-Tech’s technology is made by Nor-Tech in Minnesota and supported by Nor-Tech around the world. In addition to HPC clusters, Nor-Tech’s custom technology includes workstations, desktops, and servers for a range of applications including CAE, CFD, and FEA. Nor-Tech engineers average 20+ years of experience and are responsible for significant high performance computing innovations. The company has been in business since 1998 and is headquartered in Burnsville, Minn. just outside of Minneapolis. To contact Nor-Tech call 952-808-1000/toll free: 877-808-1010 or visit http://www.nor-tech.com. For more information on Nor-Tech’s clusters visit: http://www.nor-tech.com/solutions/hpc.

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Volkswagen Moves HPC Workloads to Verne Global in Iceland

Volkswagen Moves HPC Workloads to Verne Global in Iceland

By Unknown   06:05   No comments:

Today Verne Global announced Volkswagen is moving more than 1 MW of high performance computing applications to the company’s datacenter in Iceland. The company will take advantage of Verne Global’s hybrid data center approach – with variable resiliency and flexible density – to support HPC applications in its continuous quest to develop cutting-edge cars and automotive technology.

"The hybrid data center solution of Verne Global gives us quick and easy capacity for our High-Performance Computing applications,” says Harald Berg, Head of IT Tools, Network and Data Center in the Volkswagen Group. “We were particularly impressed by the modular design of the data center that allows us to respond to increasing demands in a flexible manner.”

Volkswagen is committed to developing new processes and applications for the modern “digital factory” of today’s automotive industry. As more and more real-life factory operations become virtualized, Volkswagen is utilizing HPC applications for everything from shortening design cycles, traffic optimization, developing and improving the connected car and more.

To drive innovation in its manufacturing process, Volkswagen is taking advantage of Verne Global’s unique, hybrid data center approach. Verne Global is the data center industry’s only developer offering the ability to scale resiliency and density of both of its solutions, powerDIRECT and powerADVANCE. Companies, like Volkswagen, can now have greater flexibility to support their individual computing needs. While both solutions deliver highly optimized data center infrastructure, powerDIRECT enables IT organizations to meet the increasing demand for high and ultra-high density applications. powerADVANCE is a traditional Tier III data center solution with the highest possible specification enterprise-ready data center environment.

"Our expertise delivering data center solutions for discrete manufacturing allow companies such as those in the automotive sector to do more compute for less,” said Jeff Monroe, CEO of Verne Global. “We see our unique offering as the future of data center solutions and a means to support companies, like Volkswagen, as they drive towards innovation, forward-thinking design and operational efficiency.”

          

In this video from the HPC User Forum in Tucson, Jorge L. Balcells from Verne Global presents: Verne Global Datacenters for Forward Thinkers.

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CloudLightning Report Looks at Barriers to HPC in the Cloud

CloudLightning Report Looks at Barriers to HPC in the Cloud

By Unknown   23:21   No comments:

The CloudLightning Project in Europe has published preliminary results from a survey on Barriers to Using HPC in the Cloud.

"Cloud computing is transforming the utilization and efficiency of IT infrastructures across all sectors. Historically, cloud computing has not been used for high performance computing (HPC) to the same degree as other use cases for a number of reasons. This executive briefing is a preliminary report of a larger study on demand-side barriers and drivers of cloud computing adoption for HPC. A more comprehensive report and analysis will be published later in 2016. From June to August 2016, the CloudLightning project surveyed over 170 HPC discrete end users worldwide in the academic, commercial and government sectors on their HPC use, perceived drivers and barriers to using cloud computing, and uses of cloud computing for HPC."

As shown in Figure 2, trust in cloud computing would appear to be a significant barrier to adopting cloud computing for HPC workloads. Data management concerns dominate the responses. This is not surprising given the large number of bio-science and university and academic respondents within the sample. The main technical barriers relate to communication speeds. This reflects a perceived lack of cloud infrastructure capable of meeting the communications and I/O requirements of high-end technical computing. Government policy is again ranked low it would seem it is neither a driver nor a barrier. Unsurprisingly availability and capital expenditure are not barriers reflecting their positive impact on adoption.

According to the report, there is unlikely to be a full shift of high performance computing workloads to the cloud in the short term however there is evidence of demand to meet the capacity limitations of internal infrastructures including use cases for testing the viability of the cloud or specific software for various use cases. This is consistent with previous research.

"Funded by the European Commission’s Horizon 2020 Program for Research and Innovation, CloudLightning brings together eight project partners from five countries across Europe. The project proposes to create a new way of provisioning heterogeneous cloud resources to deliver services, specified by the user, using a bespoke service description language. Our goal is to address energy inefficiencies particularly in the use of resources and consequently to deliver savings to the cloud provider and the cloud consumer in terms of reduced power consumption and improved service delivery, with hyperscale systems particularly in mind."

Download The Report

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TYAN HPC Platforms Add Support for NVIDIA Tesla P100, P40 and P4 GPUs

TYAN HPC Platforms Add Support for NVIDIA Tesla P100, P40 and P4 GPUs

By Unknown   23:00   No comments:

TAIPEI, Taiwan, Sept. 21 — TYAN, an industry-leading server platform design manufacturer and subsidiary of MiTAC Computing Technology Corporation, announces support and availability of the NVIDIA Tesla P100, P40 and P4 GPU accelerators with the new NVIDIA Pascal architecture. Incorporating NVIDIA’s state-of-the-art technologies allows TYAN to offer the exceptional performance and data-intensive applications features to HPC users.

“Real-time, intelligent applications are transforming our world, thus our customers need an efficient compute platform to deliver responsive and cost-effective AI,” said Danny Hsu, Vice President of MiTAC Computing Technology Corporation’s TYAN Business Unit. “TYAN is pleased to work with NVIDIA to market FT77C-B7079 and TA80-B7071 servers with P100, P40 and P4 to market. The TYAN NVIDIA-based server platforms allow hyper-scale customers to deploy accurate, responsive AI solutions, and to reduce inference latency up to 45x. The high throughput and best in class efficiency of Pascal GPUs make it possible to process exploding volumes of data to offer cost effective, accurate AI applications.”

“The NVIDIA Pascal architecture is the computing engine for modern data centers. Powered by Pascal, Tesla GPUs offer massive leaps in performance and efficiency required by the ever increasing demand of AI applications,” said Roy Kim, Tesla Product Lead at NVIDIA. “We’re partnering with TYAN to deliver the accelerated solutions customers need to deploy HPC applications and AI services.”

TYAN HPC platforms with support for NVIDIA Tesla P100, P40, P4

4U/8 GPGPU FT77C-B7079 – Support up to 2x Intel Xeon E5-2600 v3/v4 (Broadwell-EP) processors, 24x DDR4 DIMM slots, 1x PCI-E x8 mezzanine slot for high-speed I/O option, 10x 3.5″/2.5″ hot-swap SATA 6Gb/s HDDs/SSDs, dual-port 10GbE/GbE LOM, and (2+1) 3,200W redundant power supplies with 80-Plus Platinum rated.

2U/4 GPGPU TA80-B7071 – Support up to 2x Intel Xeon E5-2600 v3/v4 (Broadwell-EP) processors, 16x DDR4 DIMM slots, 1x PCI-E x8 slot for high-speed I/O option, 8x 2.5″ hot-swap SAS or SATA 6Gb/s plus 2x 2.5″ internal SATA 6Gb/s HDDs/SSDs, dual-port 10GbE/GbE LOM, and (1+1) 1,600W redundant power supplies with 80-Plus Platinum rated.

About TYAN

TYAN, a leading server brand of MiTAC Computing Technology Corporation under the MiTAC Holdings Corporation (TSE:3706), designs, manufactures and markets advanced x86 and x86-64 server/workstation board and system products. The products are sold to OEMs, VARs, System Integrators and Resellers worldwide for a wide range of applications. TYAN enable customers to be technology leaders by providing scalable, highly-integrated and reliable products such as appliances for cloud service providers (CSP) and high-performance computing and server/workstation used in CAD, DCC, E&P and HPC markets. For more information, visit MiTAC Holdings Corporation’s website at http://www.mic-holdings.com  or TYAN’s website at http://www.tyan.com

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Intel's new PC, IoT chief brings fresh ideas to the veteran chip maker

Intel's new PC, IoT chief brings fresh ideas to the veteran chip maker

By Unknown   23:53   No comments:

Intel's second-in-command Venkata Renduchintala is feeling at home with his new company after he switched over from Qualcomm

 Venkata Renduchintala is president of Intel's Client and Internet of Things (IoT) businesses and Systems Architecture Group.

Intel is now more than just a PC company. At industry events, the company's keynotes feature drones flying around, robots walking on stage and musicians creating tunes from wearables. The chip maker is helping BMW build an autonomous car, will sell modems to Apple, and is leading the development of next-generation 5G cellular networks. For all these new markets, it will provide chip and data-center technologies.

The transformation is happening partly under the leadership of Venkata Renduchintala, president of the Client and Internet of Things (IoT) Businesses and Systems Architecture Group at Intel. As Intel's second-in-command, he helped cut struggling products like mobile CPUs and sharpened the company's focus on IoT, servers, and connectivity.

Hired from rival Qualcomm late last year, he's an outsider trying to rid Intel of its historical resistance to change. He's also bringing fresh ideas and wholesale changes to  Intel, which promises to bring a new dynamic to the Silicon Valley institution.

IDG News Service spoke with him on a range of topics including VR headsets, IoT, autonomous cars, competitors and the decision to cut products. This is an edited version of the discussion.

IDGNS: How have you settled into your new job? What drew you to Intel?

It's been a really interesting process of acclimation. It's a great mixture of feeling, like an organization where I think my experience and my interests can really help the journey [CEO] Brian [Krzanich] wants to undertake with the company. The scale at which Intel can play is probably going to be very difficult for others to match if you look across, client, networking and the data center groups. The goal is to be able to think as one Intel.

IDGNS: There have been questions on how you would fit into Intel, which has a closed culture and history of promoting executives internally. Many people hired from external companies haven't worked out.

One thing that's really important to understand is that Intel is a company of tremendous heritage. I'm not coming in to fix anything. I'm coming in hopefully to add another dimension and an important ingredient to the management team that Brian has at his disposal. It requires me to respect what Intel has been able to achieve and the caliber of the management team and the brands assimilated. I don't think Brian hired me to maintain the status quo. I think what he wanted was a strong ingredient of outside-in thinking complementing the original thoughts. I'm feeling very comfortable now in being able to feel like I've got a good bunch of colleagues who know where I'm coming from; we can speak straight to each other and we can actually have really good discussions of meritocracy.

IDGNS: You had to make some decisions on cutting products Intel has worked on for years as the company's priorities were reset. How tough was it?

When you come into a company you have a degree of objectivity that isn't tainted by your attachments to the genesis of certain projects. For me it was a fairly structured, objective discussion where you make decisions in a transparent and open manner. As long as you can walk people through your thinking, you can take what was very controversial and make it very logical. I'm passionate about technology but I'm also passionate about profitability and how the two are married in a seamlessly reinforcing way.

IDGNS: What's the reasoning behind cutting mobile processors to focus on modems?

First of all, we rationalized what we were spending our R&D on. We had a couple of mobile SoC products that I don't think were worthy to continue to conclusion. That doesn't mean to say we're no longer doing mobile platforms. On the mobile platform side, my commitment is to talk less and do more. When we have something to say we'll talk about it.

On the modem side, it's a fundamental technology and this is where I think it comes down to being as indelible for us as our competence in CPU or GPU. We've set ourselves up with a very interesting road-map, but more importantly, we've established a degree of credibility, relevance and importance as a key technology partner with a number of key players in the industry that I think is really important.

IDGNS: What are your top priorities and goals?

I have three uber-level goals. One is to continue to drive our client computing business to a position of stable profitability in the face of a slowly declining [market]. I think we're doing well in that area. The second is to grow and scale our IoT business from something that's very interesting to something that's really substantial in the longer term. The IoT business for us is a microcosm of the entire company coming together -- we're creating a type of all-for-one, one-for-all mentality. The third is to maintain a degree of vibrancy in the technology leadership of our entire systems architecture organization. It's developing all the core technologies that really moves the competitive needle forward.

IDGNS: Intel's untethered mixed-reality headset called Project Alloy was big news at IDF. What are the expectations from Alloy and how are things going?

The whole point of having tetherless VR is a big deal. Everything we're doing in Alloy we're going to open-source. We can take VR and evolve it from the very rudimentary definitions today of [VR] in a smart phone that you clip into some kind of visor. You can move it to a capable, embedded PC that's driving two to three teraflops of computing and generate a really immersive experience. That was really it -- taking ideas out from the lab, productizing them, solving all those problems of integration, figuring out how RealSense and depth camera fits into all of that, figuring out how to do merged reality,  and saying "now go scale the ecosystem."

IDGNS: Is the VR headset the new PC?

I think it's another very interesting growth opportunity for the PC. I think it can generate a specific class of products in its own right. It will generate different segmentation points and probably a custom piece of silicon built on the PC platform that amplify the use case. So we're very excited about the whole VR space.

IDGNS: Intel hasn't given up on Moore's Law, though many believe it is reaching its end. How is Intel preparing for a future when manufacturing reaches atomic scale, and how will chips look beyond Kaby Lake?

Nobody inside Intel is coming anywhere near the kind-of-like fatalistic conclusions about where Moore's Law is. Intel has had a stellar track record in delivering node generation like clockwork. Maybe we've moved from a two-year to a two-and-a-half-year cadence, but we already see light at the end of the tunnel. We will continue to drive process technology and nobody is calling timeout on anything. We're working hard on 7-nanometer, we're talking about pathfinding for 5-nanometer. All of that is in the throes. We made a great announcement on Kaby Lake -- that's using an evolution of 14-nanometer transistor geometry that gave a substantially improved user experience compared to Skylake. We're going to continue to do more of that as we continue to drive process leadership.

IDGNS: Are you happy with your current chip line-up -- Kaby Lake for PCs, and Atom for IoT?

We have a competent portfolio of products. I'm in no way shape or form concluding they are complete and aren't going to be benefited from augmentation. For me I think it's really wanting to understand the use cases a lot more. I don't see an IoT strategy for Intel being one where everything is delivered by Intel. It's integrating a number of different technologies that could be indigenous to Intel, or could be created by other companies, but managed in a way where people could look at Intel as somebody providing the overarching framework of integration.

IDGNS: IoT is a big part of Intel's future. What's the strategy for that market?

That's a significant business. I think we're just starting. As you see the advent of autonomous driving vehicles, you see robots and drones start to ship in scale: those are very high value opportunities for us. We characterize our IoT interests into three verticals: industrial, transportation and retail -- all of them have an end-to-end dimension where we're providing a client environment, the networking infrastructure and the data analytics platform that drives all of that through industry partnerships.

IDGNS: Would in any way the ARM foundry deal help Intel achieve its goals in IoT and other areas? Would you be open to the idea of taking an ARM CPU license, as an example?

Open to? Yes. My view is fairly straightforward -- that Intel's IoT plan has to not only be able to harmoniously integrate Intel-based microprocessors and MCUs, it has to be able to aggregate and harmoniously integrate a plethora of different types of MCUs, whether it be ARM-based, MIPS-based, or proprietary MCUs. All of them have the ability to monitor, sense data that they want to get on to an information highway of some kind. Our ability to [support] many different client environments is going to be a necessity in any vertical IoT strategy we have. There are many areas in the ARM ecosystem where Intel can pragmatically play in for its own benefit. I'm a big believer in paying respect to established ecosystems.

IDGNS: Self-driving cars are a big deal for Intel. Could you talk about projects in the pipeline?

Our goal is to provide the type of computing power that dwarfs anything that exists in a car today, but basically make it mainstream. What we're doing on our Xeon Phi processor for machine learning and deep learning, what we're doing in computer vision and also supplemented by radar and lidar. Being able to aggregate that data, generate intelligence, make decisions on it with assistance from machine and deep learning algorithms -- that's all happening as we speak.

IDGNS: How do you see the autonomous car market evolving?

I see the first explosive area to be in the urban transportation environment where  services like Uber and Lyft will evolve and develop. There's going to be a lot of experimentation and path-finding to do in addition to technology creation. We're probably talking about a decade away. Stamina to invest is going to be really important;  those that have the stamina to stay the course are going to win big.

IDGNS: Nvidia is approaching the automotive markets aggressively with its GPUs, how will you compete?

I have a great deal of respect for Nvidia. But every time I think of Nvidia, I think about Californian wine where they can make great wine but it contains only one grape -- great Cabernet Sauvignon or a great Chardonnay. I love French wines and French wines are blends where you need to be great at growing Cabernet, great at growing Merlot, great at growing Cabernet Franc. The art is in the mixture. That's the benefit Intel has. We have GPU, we have CPU, we have custom silicon, we have embedded storage, we have FPGA. Nvidia's going to basically say "I've got GPUs and I've got GPUs and I've got GPUs." Great strategy, but it doesn't give anywhere near the extensibility, flexibility and scalability that Intel is able to offer.

IDGNS: How will 5G influence changes in the way devices are made and work?

5G is as much about the transformation of the network and the infrastructure as it is the client environment. [There is] going to be an even greater demand from mobile broadband bandwidth, people are going to want tens of gigabytes per second, if not hundreds of gigabytes per second. We're going to see much greater pervasiveness of client devices. If you talk about autonomous vehicles or delivering health services over a mobile network, you need to be able to make life or death decisions based on that. The network has to transform and the data center becomes a much higher order entity that's focused on massive data analytics that orchestrates that entire network.

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IBM and NVIDIA Move to Corner the Enterprise Market for AI

IBM and NVIDIA Move to Corner the Enterprise Market for AI

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A number of coming technologies will undoubtedly change the world as we know it. Two came to light last week while I was trying, and failing, to enjoy an infrequent vacation. One is a power storage technology that has high capacity and doesn’t catch fire or explode like lithium ion batteries. The other, and far more important, is artificial intelligence (AI), which has the potential to change our lives for the better or worse, but dramatically either way.

An alliance between two of the most powerful companies in this race, IBM and NVIDIA, was announced last week around a small, intelligent, rack-mounted server called the Power System S822LC. This was part of a three-server launch last week and I think the implications are really interesting. NVIDIA is naturally very excited about this.

Let’s explore why this partnership between two powerhouses could be really interesting.

Think

The word that IBM has connected with itself for much of my life is “Think,” and when it announced Watson, it put itself on a path to make that connection a reality. But Watson, as powerful as it is, is an intellectual baby when it comes to where the industry wants to go. Intelligent machines -- computers that can learn, adapt, and then make decisions based on data -- represent the future of computing and, some argue, the future of the human race.

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This makes for an impressive potential world impact and the firm, or firms, that get this right first will likely own the next age of computing. IBM, with Watson, got the initial lead, but Watson is expensive to buy and expensive to train.

That’s why this isn’t a one-company effort. It can’t be; it will require a team.

NVIDIA 

Now, while IBM was working on large-scale AI, NVIDIA has been working on packaged intelligence as a technology. Its Drive PX, CX, and DGX-1 platforms are designed to make cars intelligent. However, DGX-1 goes well beyond this in that it forms the basis for the learning that other platforms can use in production. In short, you train the DGX-1 and it trains, at scale, everything else it feeds. This is close, in concept, to being able to manufacture things (initially cars) that come off the line with all of the knowledge they need to operate. If we were talking people, this would be like having a kid that starts out at birth knowing everything you know.

Now we just need to put the parts together.

IBM + NVIDIA

If we combine the two companies, we get the potential for not only a system that is far less expensive to buy but one that is far less expensive to train. The result may potentially be a system that is far smarter than Watson, far more capable than the DGX-1, and able to move both companies to the next tier.

OpenPOWER

The market is currently largely x86, and Intel dominates. Only one non-x86 platform has the potential to address this AI opportunity near term, and that is OpenPOWER, largely because it is backed by IBM and, unlike ARM, it is in production for servers of this class. It is also a technology shared by a variety of vendors, making it more attractive to customers like Google, which is aggressive with AI and particularly favors open systems.

When you combine IBM, NVIDIA and OpenPOWER, you get something unique and potentially very powerful in this race to intelligent computing.

Wrapping Up: Power of the Partnership

In the end, the eventual success of this effort will likely be directly attributable to how well IBM and NVIDIA partner over time. A similar partnership between IBM, Intel and Microsoft created the PC market. If IBM and NVIDIA can do better (that earlier partnership fell apart), then the potential for both firms to own this next technology wave is unmatched. If not, then we’ll just have another story about big firms failing to meet their potential.

For now, IBM and NVIDIA have the inside track, but it’s early in the race. While this new line of servers is a great start, as both companies know, it matters far less who leads a race at the beginning than who leads a race at the end.

Rob Enderle is President and Principal Analyst of the Enderle Group, a forward-looking emerging technology advisory firm.  With over 30 years’ experience in emerging technologies, he has provided regional and global companies with guidance in how to better target customer needs; create new business opportunities; anticipate technology changes; select vendors and products; and present their products in the best possible light. Rob covers the technology industry broadly. Before founding the Enderle Group, Rob was the Senior Research Fellow for Forrester Research and the Giga Information Group, and held senior positions at IBM and ROLM. Follow Rob on Twitter @enderle, on Facebook and on Google+.

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