Why Google Is Driving Compute Diversity

Why Google Is Driving Compute Diversity

By Unknown   22:15   No comments:

In the ideal hyperscaler and cloud world, there would be one processor type with one server configuration and it would run any workload that could be thrown at it. Earth is not an ideal world, though, and it takes different machines to run different kinds of workloads.

In fact, if Google is any measure – and we believe that it is – then the number of different types of compute that needs to be deployed in the datacenter to run an increasingly diverse application stack is growing, not shrinking. It is the end of the General Purpose Era, which began in earnest during the Dot Com Boom and which started to fade a few years ago even as Intel locked up the datacenter with its Xeons, and the beginning of the Cambrian Compute Explosion era, which got rolling as Moore’s Law improvements in compute hit a wall. Something had to give, and it was the ease of use and volume economics that come from homogeneity enabled by using only a few SKUs of the X86 processor.

Bart Sano is the lead of the platforms team inside of Google, which has been around almost as long as Google itself, but Sano has only been at the company for ten years. Sano reports to Urs Hölzle, senior vice president of technical infrastructure at the search engine and cloud giant, and is responsible for the designs the warehouse-scale computers, including the datacenters themselves, everything inside of them including compute and storage, and the network hardware and homegrown software that interconnects them.

As 2016 wound down, Google made several hardware announcements, including bringing GPUs from Nvidia and AMD to Cloud Platform, and also that it would be getting out “Skylake” Xeon processors on Cloud Platform ahead of the official Intel launch later this year and that certain machine learning services on Cloud Platform are running on its custom Tensor Processing Unit (TPU) ASICs or GPUs. In the wake of these announcements, The Next Platform sat down with Sano to have a chat about Google’s hardware strategies, and more specifically about how the company leverages the technology it has created for search engines, ad serving, media serving, and other aspects of the Google business for the public cloud.

Timothy Prickett Morgan: How big of a deal are the Skylake Xeons? We think they might be the most important processor to come out of Intel since the “Nehalem” Xeon 5500s way back in 2009.

Bart Sano: We are really excited about deploying Skylake, because it is a material difference for our end customers, who are going to benefit a lot from the higher performance, the virtualization it provides in the cloud environment, and the computational enhancements that it has in the instruction set for SIMD processing for numerical computations. Skylake is an important improvement for the cloud. Again, I think the broader context here is that Google is really committed to working on and providing the best infrastructure not only for Google, but also to the benefit of our cloud. We are trying to ensure that cloud customers benefit from all of the efforts inside of Google, including machine learning running on GPUs and TPUs.”

TPM: Google was a very enthusiastic user of AMD Opterons the first time around, and I have seen the motherboards because Urs showed them to me, but to my way of thinking about this, 2017 is one of the most interesting years for processing and coprocessing that we have seen in a long, long time. It is a Cambrian Explosion of sorts. So the options are there, and clearly Google has the ability to design and have others build systems and put your software on lots of different things. It is obvious that Skylake is the easiest thing for Google to endorse and move quickly to. But has Google made any commitment to any of these other architectures? We know about Power9 and the work Google is doing there, but has Google said it will add Zen Opterons into the mix, or is it just too early for that?

Bart Sano: I can say that we are committed to the choice of these different architectures, including X86 – and that includes AMD – as well as Power and ARM. The principle that we are investing in heavily is that competition breeds innovation, which directly benefits our end customers. And you are right, this year is going to be a very interesting year. There are a lot of technologies coming out, and there will be a lot of interesting competition.

TPM: It is easy for me to conceive of how some of these other technologies might be used by Google itself, but it is harder for me to see how you get cloud customers on board at an infrastructure level with some of these alternatives like Power and ARM because they have to get their binaries ported and tuned for them. What is the distinction between the timing for a technology that will be used by Google internally and one that will be used by Cloud Platform?

Bart Sano: Our end goal is that whatever technology that we are going to bring forward to the benefit of Google we will bring to bear on the cloud. We view cloud as just another product pillar of Google. So if something is available to ads or search or whatever, it will be available to cloud. Now, you are right, not all of the binaries will be highly optimized, but as it relates to Google’s binaries, our intention is to make all of these architectures equally competitive. You are right, there is obviously a lag in the porting efforts on this software. But our ultimate goal is to get all of them on equal footing.”

TPM: We know that Intel has already had early release on the Skylake Xeons, and we assume that some HPC shops and other hyperscalers and cloud builders like Google have early access to these processors already. So my guess is that you have been playing in the labs with Skylakes since maybe September or October last year, tops. When do you deploy internally at Google with Skylake and when do you deploy to the cloud?

Bart Sano: I can’t speak to the specifics internally, but what I can say is that the cloud will have Skylake in early 2017. That is all that I can really say with precision. But you would assume that we have had these in the labs and we will do a lot of testing before we made an announcement.”

TPM: My guess is that Intel will launch in June or July, and that you can’t have them much before March in production on GCP, and that January or February of this year is just not possible. . . .

Bart Sano: “We could make some bets.” [Laughter]

TPM: Are you doing special SKUs of Skylake Xeons, or do you use stock CPUs.

Bart Sano: I can’t talk about SKUs and such, but what I can say is that we have Skylake. [Laughter]

TPM: AMD is obviously pleased that Google has endorsed its GPUs as accelerators. What is the nature of that deal?

Bart Sano: It is about choice, and what architecture is best for what workloads. We think there are cases where the AMD will provide a good choice for our end customers. It is always good to have those options, and not everything fits onto one architecture, whether it is Intel, AMD, Nvidia or even our own TPUs. You said it best in that this is an explosion of diversity. Our position is that we should have as many of these architectures as possible as options for our customers and let competition choose which one is right for different customers.

“The cloud infrastructure is not remarkably different from the internal Google infrastructure – and it should not be because we are trying to leverage the cost structures of both together and the lessons we learn from the Google businesses.”

TPM: Has Google developed its own internal framework that spans all of these different compute elements, or do you have different frameworks for each kind of compute? There is CUDA for Nvidia GPUs, obviously, and you can use ROCm from AMD to do CL or move CUDA onto its Polaris GPUs. There is TensorFlow for deep learning, and other frameworks. My assumption is that Google is smart about this, so prove me right.

Bart Sano: It is a challenge. For certain workloads, we can leverage common pipes. But for the end customers, there is an issue in that there are different stacks for the different architectures, and it is a challenge. We do have our own internal ways to try to get commonality so we are able to run more efficiently, at least from a programmer perspective it is all taken care of by the software. I think that what you are pointing out is that if cloud customers have binaries and they need to run them, we have to be able to support that. That diversity is not going to go away.

We are trying to get the marketplace to get more standardization in that area, and in certain domains we are trying to abstract it out so it is not as big of an issue – for instance, with TensorFlow for machine learning. If that is adopted and we have Intel support TensorFlow, then you don’t have that much of a problem. It just becomes a matter of compilation and it is much more common.

TPM: What is Google’s thinking about the Knights family of processors and coprocessors, especially the current “Knights Landing” and the future “Knights Crest” for machine learning and “Knights Hill” for broader-based HPC?

Bart Sano: Like I said, we have a basic tenet that we do not turn anything away and that we have to look at every technology. That is why choice is so important. We want to choose wisely, because whatever we put into the infrastructure is going to go to not only our own internal customers, but the end customers of our cloud products. We look at all of these technologies and assess them according to total cost of ownership. Whether it is for search, ads, geo, or whatever internally or for the cloud, we are constantly assessing all technologies.

TPM: How do you manage that? Urs told me that Google has three different server designs each year coming out of the labs into production, and servers stay in production for several years. It seems to me that if you start adding more different kinds of compute, it will be more complex and expensive to build and support all of this diversity of machinery. If you have an abstraction layer in the software and a build process that lets applications be deployed to any type of compute, that makes it easier. But you still have an increasing number of type and configuration of machines. Doesn’t this make your manufacturing and supply chain more complex, too?

Bart Sano: You are right, having all of these different SKUs makes it difficult to handle. In any infrastructure, you have a mix of legacy versus current versus new stuff, and the software has to abstract that. There are layers in the software stack, including Borg internally or Kubernetes on the cloud as well as others.

TPM: You can do a lot in the hardware, too, right? An ARM server based on ThunderX from Cavium has a similar BIOS and baseboard management controller as a Xeon server, and ditto for a “Zaius” Power9 machine like the one that Google is creating in conjunction with Rackspace Hosting. You can get the form factors the same, and then you are differentiating in other aspects of the system such as memory bandwidth or capacity. But we have to assume that the number of servers that Google is supporting is still growing as more types of compute are added to the infrastructure.

Bart Sano: The diversity is growing, and when we helped found the OpenPower consortium, we knew what that meant. And the implications are a heterogeneous environment and much more operational complexity. But this is the reality of the world that we are entering. If we are to be the solution to more than just the products of Google, we have to support this diversity. We are going into it with our eyes wide open.

TPM: Everybody assumes that Google has lots of Nvidia GPU for accelerating machine learning and other workloads, and now you have Radeon GPUs from AMD. Have you been doing this for a long time internally and now you are just exposing it on Cloud Platform?

Bart Sano: We have actually been doing the GPUs and the TPUs for a while, and we are now exposing it to the cloud. What became apparent is that the cloud customer wanted them. That was the question: would people want to come to the cloud for this sort of functionality.

The cloud infrastructure is not remarkably different from the internal Google infrastructure – and it should not be because we are trying to leverage the cost structures of both together and the lessons we learn from the Google businesses.

TPM: With the GPUs and TPUs, are you exposing them at an infrastructure level, where customers can address them directly like they would internally on their own iron, or are they exposed at a platform level, where customers buy a Google service that they just pour data into and run and they never get under the hood to see?

Bart Sano: The GPUs are exposed at more of an infrastructure level, where you have to see them to run binaries on them. It is not like a platform, and customers can pick whether they want Nvidia or AMD GPUs. They will be available attached to Compute Engine virtual machines, and for the Cloud Machine Learning services. For those who want to interact at that level, they can. We provide support for TPUs at a higher level, with our Vision API for image search service or Translation API for language translation, for example. They don’t really interact with TPUs, per se, but with the services that run on them.

TPM: What design does Google use to add GPUs to its servers? Does it look like an IBM Power Systems LC or Nvidia DGX-1 system? Do you have a different way of interconnecting and pooling GPUs than what people currently are doing? Have you adopted NVLink for lashing together GPUs?

Bart Sano: I would say that GPUs require more interconnection and cluster bandwidth. I can’t say that we are the same as the examples you are talking about, but what I can say is that we match the configuration so the GPUs are not starved for memory bandwidth and communication bandwidth. We have to architect these systems so they are not starved. As for NVLink, I can’t go into details like that.

TPM: I presume that there is a net gain with this increasing diversity. There is more complexity and lower volumes of any specific server type, but you can precisely tune workloads for specific hardware. We see it again and again that companies are tuning hardware to software and vice versa because general purpose, particularly at Google’s scale, is not working anymore. Unit costs rise, but you come out way ahead. Is that the way it looks to Google?

Bart Sano: That is the driver behind why we are providing these different kinds of computation. As for the size of the jump in price/performance, it is really different for different customers.

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Server CPU Predictions For 2017

Server CPU Predictions For 2017

By Unknown   22:05   No comments:

2017 promises to be an exciting year for servers and the competitiveness of compute offerings. This year will see this scope of impact not only include enterprise datacenters and the public cloud, but extend to the emergence of "edge computing". Edge computing is defined as compute required to deal with data at or near the point of creation. Among other things, these devices will include the “ocean” of remote, smart sensors, commonly included in internet of things (IoT) discussions.

Here is a list of a few things to we’ll see concerning specific CPUs.

It should come as no surprise that Intel INTC -0.16% continues to dominate (>99%) the server market but is under enormous pressure on all fronts. Xeon and its evolution continue to be their compute vanguard. Xeon-Phi (and now the addition of Nervana) make up their engines for high-performance computing / machine learning. Phi has seen some success, but it isn’t clear yet how Nervana offerings will materialize.

Advanced Micro Devices AMD -0.57% (AMD) has their best shot in years for fielding an Intel competitor that just about everyone (except perhaps Intel) is eager to see. If the AMD Zen server CPU is simply good enough (meaning, it shows up, works and has at least some performance value), it will take market share simply by being an x86 competitor. AMD is encouraged by early indicators. They also have their ATI GPGPU technology which will provide additional opportunities.

ARM Holdings will continue to dominate the mobile and embedded device space, but the fight is hard in these segments. The more likely opportunity for ARM expansion will be at the "edge" and not so much in the server space. The death of Vulcan by Avago, the acquisition of Applied Micro Circuits (APM) and their plan to find a place for X-Gene leaves the Cavium CAVM +0.69% ThunderX, the "yet to be launched" Qualcomm QCOM -0.02% Centriq CPU and a few other very focused ARM initiatives still standing. After years of "This is the Year for ARM Servers", the outlook could be better, and if AMD produces a plausible Intel competitor (capable of running x86 software), it will put extreme pressure on whole ARM server CPU initiative.

OpenPOWER seems on the other hand to have a lot of momentum but to date has not significantly impacted the x86 server market. 2017 may end on a different note. OpenPOWER‘s (IBM IBM -1.27%) willingness to embrace NVIDIA NVDA -0.74% (the darling of the machine learning segment) and embed an NV-Link interface is going to play well with much of AI and HPC communities. By the end of the year, we will have seen some interesting OpenPOWER offerings emerge based on advanced silicon process technology from a variety of sources, and 2018 may see a whole different story. Especially if an embrace from Google GOOGL -0.14%, who has been flirting with OpenPOWER for a while now, materializes and creates a tipping point.

The real challenge to all CPUs is the way they do work. Their philosophy is built on the principle that data must come into the chip, be operated on by the chip, with results or even new data being pushed out of the chip. This whole process creates a natural bottleneck that we've flirted with for decades. As the magnitude and scope of data increases, something has got to give, and a favorite candidate is more parallelism. So far, this has favored GPGPUs or accelerators.

At the bigger-picture business level for datacenters and the public cloud, the real question is not so much which CPU (in fact, the business folks probably couldn't care less), but the economics of private, public or hybrid solutions. It is safe to say enterprise computing will not disappear any time soon, and while there is much activity, the implementations and economics of hybrid solutions have proven to be difficult. According to Gartner, by 2020 more compute power will have been sold by IaaS and PaaS cloud providers than sold and deployed into enterprise datacenters. The fact that companies (especially smaller ones) are either being born in or moving to the cloud at a rapid pace is undeniable. However, NOT all are seeing the expected saving materialize from this move. 2017 will certainly see some careful thinking and maybe even some rethinking of strategy.

The explosion of data at the edge is simply going to change data processing as we know it and will create a variety of computing problems that are difficult to do in the cloud (even though the results may end up there). However, they may not be in the enterprise datacenters as we know them either, and we may find them “stuck” all over the place. For more than sixty years, we have seen compute follow the data. First from the original mainframe datacenter to the desktop, to departmental servers, into enterprise datacenters, and now significantly into the cloud. It is my opinion, that If you plan to put just your data into the cloud, economics (the cost of network usage) will drive your compute there sooner or later. You want to consider this carefully based on your actual needs and usage. There might be a better overall business outcome, depending on your size and ability to operate, in your own datacenter.

The major emerging source of data is at the edge and will drive the need for much compute there. By the way, all the CPUs mentions here should be able do the edge reasonably well so … Game on again!

Disclosure: My firm, Moor Insights & Strategy, like all research and analyst firms, provides or has provided research, analysis, advising, and/or consulting to many high-tech companies in the industry including Advanced Micro Devices, Applied Micro Circuits, ARM Holdings, IBM, Intel, NVIDIA and Qualcomm. I do not hold any equity positions with any companies cited in this column.

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Call for Participation: HPC Advisory Council Stanford Conference in February

Call for Participation: HPC Advisory Council Stanford Conference in February

By Unknown   21:45   No comments:

"We invite submissions introducing a wide range of topics, levels and considerations in HPC architectures, applications and usage – from fundamentals to the latest advances and hot topic areas. Submissions can be proposed as papers or presentation only (without papers). Each submission should indicate any unique criteria and requirements along with scheduling and format preferences in your proposal. Sessions can be defined as technical sessions, workshop(s) and/or as part of a ‘mini’ series of quick take tutorials."

“Over two days we’ll delve into a wide range of interests and best practices – in applications, tools and techniques and share new insights on the trends, technologies and collaborative partnerships that foster this robust ecosystem. Designed to be highly interactive, the open forum will feature industry notables in keynotes, technical sessions, workshops and tutorials. These highly regarded subject matter experts (SME’s) will share their works and wisdom covering everything from established HPC disciplines to emerging usage models from old-school architectures and breakthrough applications to pioneering research and provocative results. Plus a healthy smattering of conversation and controversy on endeavors in Exascale, Big Data, Artificial Intelligence, Machine Learning and much much more!”

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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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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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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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