Peter ffoulkes

About Peter ffoulkes

http://www.OrionX.net

Peter is an expert in Cloud Computing, Big Data, and HPC markets, technology trends, and customer requirements which he blends to craft growth strategies and assess opportunities.

Posts by Peter ffoulkes:

For Enterprise Cloud, Oracle is the Only Choice Grounded in Reality

Peter ffoulkes /

June 14, 2017

Snapshot of Cloud Computing for Enterprise IT

There is a loudly expressed opinion that ‘the cloud’ is the future of computing and that companies such as Amazon, Google, and Microsoft will dominate the market, replacing established hardware and software vendors such as IBM, Dell, HPE, and Oracle. In some ways, this is true especially for startups, small companies, and perhaps even some midsize companies, but the situation for enterprise level deployments is very different.

For small companies, the use of cloud services minimizes the need for capital expenditure on infrastructure and the recruitment of IT specialists, accelerating their time to production, possibly at the expense of some customization.

By contrast, enterprise level organizations typically have a substantial reliance on established business critical applications. Many of these applications cannot easily be rewritten or re-deployed on public cloud architectures. Sometimes the systems they run on are not based on the x86 architecture, further complicating matters.

Existing enterprise applications, however, can and often need to be integrated with new cloud services to deliver a modern infrastructure as a cloud service while ensuring business continuity. This hybrid approach is an optimum combination of private and public cloud services. It leverages existing data center and infrastructure resources and combines elastic cloud services from external providers when needed. For businesses where regulation and compliance may affect where and how data may be located and accessed, or if large memory scale up data systems may be UNIX-based and are not well-suited or compatible with scale out Intel-based architectures, a hybrid cloud is particularly advantageous.

A Business Solution to the Cloud Problem

Enterprise customers are much more concerned with a business solution to their cloud problem than just a technical solution. Minimizing costly changes is valuable to them as long as the underlying technology has a reasonable and real roadmap. For them, a mixture of private and public cloud can address multiple requirements:

Application availability, without a big cost to port, migrate, and re-deploy
Governance, where ownership, location, and management of resources can be customized for different parts of a distributed cloud application
Modernization, driven by new technologies that enable new capabilities but remain “under the hood”

This approach focuses on business optimization, integrates and consolidates disparate systems, offers a flexible choice of on-premises, managed on-premises, or off-premises cloud.

For enterprise customers, this is a critical transformation. The service provider model is generally accepted as the way forward, but successfully navigating the journey requires the integration of existing business critical systems with new and agile applications. This requires differentiation at a business service level, it is not technology driven but must incorporate both the old and the new while optimizing capital costs and operational margins.

Market Landscape

Cloud computing means fewer but larger buyers of infrastructure technologies. Cloud service providers such as Amazon and Google buy standardized systems in large volumes. Other CPU and infrastructure vendors have doubled down on the capabilities of their architectures to support both legacy and modern systems.

Among established technologies, x86 continues to dominate at the CPU level but is unable to reach a large body of enterprise applications that were written for IBM, HP, and Oracle/Sun. HP has effectively moved to x86 and is not offering its own public cloud service. IBM has formed the OpenPower consortium as it attempts to build an ecosystem and is building its Softlayer cloud service, but relies on application software partners to complete the stack. This leaves Oracle in the unique position of offering chips-to-apps-to-cloud in one place supporting x86 as well as its own SPARC chip (with the SPARC Dedicated Compute Service). As the only UNIX public cloud service available from a major systems or cloud vendor, Oracle has a robust roadmap with everything available on-premises, managed-on-premises, and off-premises.

Comparing ‘Market Hype’ to Reality

For most enterprises, a successful transition to cloud service-based infrastructures is more important than a rapid transition. There are many market research reports that discuss the nature and pace of cloud adoption in ‘the Enterprise’, but one thing they have in common is the limited pace and scope of public cloud adoption. At the end of the day, it is business and workload considerations that drive decisions, not technology.

While the graph (same link as the image) is slightly dated (2015), the trends have not changed and the presented results are still indicative. The common trends demonstrate that dependence upon isolated on-premises systems – even if virtualized – is declining. Private cloud, whether on-premises or hosted is and will be a substantial part of the market, but the dominant and growing sector that seems destined to dominate is hybrid cloud, a combination of on-premises, hosted and public cloud services.

For many enterprises, the challenge is to find a vendor that can offer all of those things with a viable business model. We live in very interesting times when the only global technology vendor that appears to be able to meet all the criteria is Oracle, with a viable alternative to Intel Architecture in the form of SPARC.

Peter ffoulkes

Peter is an expert in Cloud Computing, Big Data, and HPC markets, technology trends, and customer requirements which he blends to craft growth strategies and assess opportunities.

www.OrionX.net

What is the use of quantum computing without pictures or conversations?

Peter ffoulkes /

March 7, 2016

The world of quantum computing frequently seems as bizarre as the alternate realities created in Lewis Carroll’s masterpieces “Alice’s Adventures in Wonderland” and “Through the Looking-Glass”. Carroll (Charles Lutwidge Dodgson) was a well-respected mathematician and logician in addition to being a photographer and enigmatic author.

Has quantum computing’s time actually come or are we just chasing rabbits?

That is probably a twenty million dollar question by the time a D-Wave 2X™ System has been installed and is in use by a team of researchers. Publicly disclosed installations currently include Lockheed Martin, NASA’s Ames Research Center and Los Alamos National Laboratory.

Hosted at NASA’s Ames Research Center in California, the Quantum Artificial Intelligence Laboratory (QuAIL) supports a collaborative effort among NASA, Google and the Universities Space Research Association (USRA) to explore the potential for quantum computers to tackle optimization problems that are difficult or impossible for traditional supercomputers to handle. Researchers on NASA’s QuAIL team are using the system to investigate areas where quantum algorithms might someday dramatically improve the agency’s ability to solve difficult optimization problems in aeronautics, Earth and space sciences, and space exploration. For Google the goal is to study how quantum computing might advance machine learning. The USRA manages access for researchers from around the world to share time on the system.

Using quantum annealing to solve optimization problems

D-Wave’s quantum annealing technology addresses optimization and probabilistic sampling problems by framing them as energy minimization problems and exploiting the properties of quantum physics to identify the most likely outcomes or as a probabilistic map of the solution landscape.

Quantum annealer dynamics are dominated by paths through the mean field energy landscape that have the highest transition probabilities. Figure 1 shows a path that connects local minimum A to local minimum D.

Figure 2 shows the effect of quantum tunneling (in blue) to reduce the thermal activation energy needed to overcome the barriers between the local minima with the greatest advantage observed from A to B and B to C, and a negligible gain from C to D. The principle and benefits are explained in detail in the paper “What is the Computational Value of Finite Range Tunneling?”

The D-Wave 2X: Interstellar Overdrive – How cool is that?

As a research area, quantum computing is highly competitive, but if you want to buy a quantum computer then D-Wave Systems , founded in 1999, is the only game in town. Quantum computing is as promising as it is unproven. Quantum computing goes beyond Moore’s law since every quantum bit (qubit) doubles the computational power, similar to the famous wheat and chessboard problem. So the payoff is huge, even though it is expensive, unproven, and difficult to program.

The advantage of quantum annealing machines is they are much simpler to build than gate-model quantum computing machines. The latest D-Wave machine (the D-Wave 2X), installed at NASA Ames, is approximately twice as powerful (in a quantum, exponential sense) as the previous model at over 1,000 qubits (1,097). This compares with roughly 10 qubits for current gate-model quantum systems, so two orders of magnitude. It’s a question of scale, no simple task, and a unique achievement. Although quantum researchers initially questioned whether the D-Wave system even qualified as a quantum computer, albeit a subset of quantum computing architectures, that argument seems mostly settled and it is now generally accepted that quantum characteristics have been adequately demonstrated.

In a D-Wave system, a coupled pair of qubits (quantum bits) demonstrate quantum entanglement (they influence each other), so that the entangled pair can be in any one of four states resulting from how the coupling and energy biases are programmed. By representing the problem to be addressed as an energy map the most likely outcomes can be derived by identifying the lowest energy states.

A lattice of approximately 1,000 tiny superconducting circuits (the qubits) is chilled close to absolute zero to deliver quantum effects. A user models a problem into a search for the lowest point in a vast energy landscape. The processor considers all possibilities simultaneously to determine the lowest energy required to form those relationships. Multiple solutions are returned to the user, scaled to show optimal answers, in an execution time of around 20 microseconds, practically instantaneously for all intents and purposes.

The D-wave system cabinet – “The Fridge”– is a closed cycle dilution refrigerator. The superconducting processor itself generates no heat, but to operate reliably must be cooled to about 180 times colder than interstellar space, approximately 0.015° Kelvin.

Environmental considerations: Green is the color

To function reliably, quantum computing systems require environments that are not only shielded from the Earth’s natural environment, but would be considered inhospitable to any known life form. A high vacuum is required, a pressure 10 billion times lower than atmospheric pressure, and shielded to 50,000 times less than Earth’s magnetic field. Not exactly a normal office, datacenter, or HPC facility environment.

On the other hand, the self-contained “Fridge” and servers consume just 25kW of power (approximately the power draw of a single heavily populated standard rack) and about three orders of magnitude (1000 times) less power than the current number one system on the TOP500, including its cooling system. Perhaps a more significant consideration is that power demand is not anticipated to increase significantly as it scales to several thousands of qubits and beyond.

In addition to doubling the number of qubits compared with the prior D-Wave system, the D-Wave 2X delivers lower noise in qubits and couples, delivering greater confidence in achieved results.

So much for the pictures, what about the conversations?

Now that we have largely moved beyond the debate of whether a D-Wave system is actually a quantum machine or not, then the question “OK, so what now?” could bring us back to chasing rabbits, although this time inspired by the classic Jefferson Airplane song, “White Rabbit”:

“One algorithm makes you larger
And another makes you small
But do the ones a D-Wave processes
Do anything at all?”

That of course, is where the conversations begin. It may depend upon the definition of “useful” and also a comparison between “conventional” systems and quantum computing approaches. Even the fastest supercomputer we can build using the most advanced traditional technologies can still only perform analysis by examining each possible solution serially, one solution at a time. This makes optimizing complex problems with a large number of variables and large data sets a very time consuming business. By comparison, once a problem has been suitably constructed for a quantum computer it can explore all the possible solutions at once and instantly identify the most likely outcomes.

If we consider relative performance then we begin to have a simplistic basis for comparison, at least for execution times. The QuAIL system was benchmarked for the time required to find the optimal solution with 99% probability for different problem sizes up to 945 variables. Simulated Annealing (SA), Quantum Monte Carlo (QMC) and the D-Wave 2X were compared. Full details are available in the paper referenced previously. Shown in the chart are the 50^th, 75^th and 85^th percentiles over a set of 100 instances. The error bars represent 95% confidence intervals from bootstrapping.

This experiment occupied millions of processor cores for several days to tune and run the classical algorithms for these benchmarks. The runtimes for the higher quantiles for the larger problem sizes for QMC were not computed because the computational cost was too high.

The results demonstrate a performance advantage to the quantum annealing approach by a factor of 100 million compared with simulated annealing running on a single state of the art processor core. By comparison the current leading system on the TOP500 has fewer than 6 million cores of any kind, implying a clear performance advantage for quantum annealing based on execution time.

The challenge and the next step is to explore the mapping of real world problems to quantum machines and to improve the programming environments, which will no doubt take a significant amount of work and many conversations. New players will become more visible, early use cases and gaps will become better defined, new use cases will be identified, and a short stack will emerge to ease programming. This is reminiscent of the early days of computing or space flight.

A quantum of solace for the TOP500: Size still matters.

Even though we don’t expect to see viable exascale systems this decade, and quite likely not before the middle of the next, we won’t be seeing a Quantum500 anytime soon either. NASA talks about putting humans on Mars sometime in the 2030s and it isn’t unrealistic to think about practical quantum computing as being on a similar trajectory. Recent research at the University of New South Wales (UNSW) in Sidney, Australia demonstrated that it may be possible to create quantum computer chips that could store thousands, even millions of qubits on a single silicon processor chip leveraging conventional computer technology.

Although the current D-Wave 2X system is a singular achievement it is still regarded as being relatively small to handle real world problems, and would benefit from getting beyond pairwise connectivity, but that isn’t really the point. It plays a significant role in research into areas such as vision systems, artificial intelligence and machine learning alongside its optimization capabilities.

In the near term, we’ve got enough information and evidence to get the picture. It will be the conversations that become paramount with both conventional and quantum computing systems working together to develop better algorithms and expand the boundaries of knowledge and achievement.

References and attributions:

Paper on What is the Computational Value of Finite Range Tunneling? http://arxiv.org/pdf/1512.02206v4.pdf

Paper on Benchmarking a quantum annealing processor with the time-to-target metric: http://www.dwavesys.com/sites/default/files/ttt_arxiv.pdf

White rabbit from Sir John Tenniel’s illustrations of “Alice’s Adventures in Wonderland” by Lewis Carroll

“White Rabbit” Lyrics copyright Jefferson Airplane/ Grace Slick

Peter ffoulkes – Partner at OrionX.net

Peter ffoulkes

Peter is an expert in Cloud Computing, Big Data, and HPC markets, technology trends, and customer requirements which he blends to craft growth strategies and assess opportunities.

www.OrionX.net

Benchmarks, Schmenchmarks: Big Iron Data and the November 2015 TOP500

Peter ffoulkes /

November 16, 2015

There has been much discussion in recent years as to the continued relevance of the High Performance Linpack (HPL) benchmark as a valid measure of the performance of the world’s most capable machines, with some supercomputing sites opting out of the TOP500 completely such as National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign that houses the Cray Blue Waters machine. Blue Waters is claimed to be the fastest supercomputer on a university campus, although without an agreed benchmark it is a little hard to verify such a claim easily.

At a superficial level it appears as though any progress in the upper echelons of the TOP500 may have stalled with little significant change in the TOP10 in recent years and just two new machines added to that elite group in the November 2015 list: Trinity at 8.10 Pflop/s and Hazel Hen at 5.64 Pflop/s, both Cray systems increasing the company’s share of the TOP10 machines to 50%. The TOP500 results are available at http://www.top500.org.

A new benchmark – the High Performance Conjugate Gradients (HPCG) – introduced two years ago was designed to be better aligned with modern supercomputing workloads and includes system features such as high performance interconnects, memory systems, and fine grain cooperative threading. The combined pair of results provided by HPCG and HPL can act as bookends on the performance spectrum of any given system. As of July 2015 HPCG had been used to rank about 40 of the top systems on the TOP500 list.

The November TOP500 List: Important Data for Big Iron
Although being complemented by other benchmarks, the HPL-based TOP500 is still the most significant resource for tracking the pace of super computing technology development historically and for the foreseeable future.
The first petascale machine, ‘Roadrunner’, debuted in June 2008, twelve years after the first terascale machine – ASCI Red in 1996. Until just a few years ago 2018 was the target for hitting the exascale capability level. As 2015 comes to its close the first exascale machine seems much more likely to debut in the first half of the next decade and probably later in that window rather than earlier. So where are we with the TOP500 this November, and what can we expect in the next few lists?

Highlights from the November 2015 TOP500 List on performance:
– The total combined performance of all 500 systems had grown to 420 Pflop/s, compared to 361 Pflop/s last July and 309 Pflop/s a year previously, continuing a noticeable slowdown in growth compared to the previous long-term trend.

– Just over 20% of the systems (a total of 104) use accelerator/co-processor technology, up from 90 in July 2015

– The latest list shows 80 petascale systems worldwide making a total of 16% of the TOP500, up from 50 a year earlier, and significantly more than double the number two years earlier. If this trend continues the entire TOP100 systems are likely to be petascale machines by the end of 2016.

So what do we conclude from this? Certainly that the road to exascale is significantly harder than we may have thought, not just from a technology perspective, but even more importantly from a geo-political and commercial perspective. The aggregate performance level of all of the TOP500 machines has just edged slightly over 40% of the HPL metric for an exascale machine.

Most importantly, increasing the number of petascale-capable resources available to scientists, researchers, and other users up to 20% of the entire list will be a significant milestone. From a useful outcome and transformational perspective it is much more important to support advances in science, research and analysis than to ring the bell with the world’s first exascale system on the TOP500 in 2018, 2023 or 2025.

Architecture
As acknowledged by the introduction of HPCG, HPL and the TOP500 performance benchmark are only one part of the HPC performance equation.

The combination of modern multi-core 64 bit CPUs and math accelerators from Nvidia, Intel and others have addressed many of the issues related to computational performance. The focus on bottlenecks has shifted away from computational strength to data-centric and energy issues, which from a performance perspective influence HPL results but are not explicitly measured in the results. However, from an architectural perspective the TOP500 lists still provide insight into the trends in an extremely useful manner.

Observations from the June 2015 TOP500 List on system interconnects:
– After two years of strong growth from 2008 to 2010, InfiniBand-based TOP500 systems plateaued at 40% of the TOP500 while compute performance grew aggressively with the focus on hybrid, accelerated systems.

– The uptick in InfiniBand deployments June 2015 to over 50% of the TOP500 list for the first time does not appear to be the start of a trend, with Gigabit Ethernet systems increasing to over a third (36.4%) of the TOP500, up from 29.4%.

The TOP500: The November List and What’s Coming Soon
Although the TOP10 on the list have shown little change for a few years, especially with the planned upgrade to Tianhe 2 blocked by the US government, there are systems under development in China that are expected to challenge the 100 Pflop/s barrier in the next twelve months. From the USA, Coral initiative is expected to significantly exceed the 100 Pflop/s limit – targeting the 100 to 300 Pflop/s with two machines from IBM and one from Cray, but not before the 2017 time frame. None of these systems are expected to deliver more than one third of exascale capability.

Vendors: A Changing of the Guard
The USA is still the largest consumer of TOP500 systems with 40% of the share by system count, which clearly favors US vendors, but there are definite signs of change on the wind.

Cray is the clear leader in performance with a 24.9 percent share of installed total performance, roughly the same as a year earlier. Each of the other leading US vendors is experiencing declining share with IBM in second place with a 14.9 percent share, down from 26.1 percent last November. Hewlett Packard is third with 12.9 percent, down from 14.5 percent twelve months ago.

These trends are echoed in the system count numbers. HP has the lead in systems and now has 156 (31%), down from 179 systems (36%) twelve months ago. Cray is in second place with 69 systems (13.8%), up from 12.4% last November. IBM now has 45 systems (9%), down from 136 systems (27.2%) a year ago.

On the processor architecture front a total of 445 systems (89%) are now using Intel processors, slightly up from 86.2 percent six months ago, with IBM Power processors now at 26 systems (5.2%), down from 38 systems (7.6%) six month ago.

Geo-Political Considerations
Perhaps the single most interesting insight to come out of the TOP500 has little to do with the benchmark itself, and the geographical distribution data which in itself underscores the importance of the TOP500 lists in tracking trends.

The number of systems in the United States has fallen to the lowest point since the TOP500 list was created in 1993, down to 201 from 231 in July. The European share has fallen to 107 systems compared to 141 on the last list and is now lower than the Asian share, which has risen to 173 systems, up from 107 the previous list and with China nearly tripling the number of systems to 109. In Europe, Germany is the clear leader with 32 systems, followed by France and the UK at 18 systems each. Although the numbers for performance-based distributions vary slightly, the percentage distribution is remarkably similar.

The USA and China divide over 60% of the TOP500 systems between them, with China having half the number and capability of the US-based machines. While there is some discussion about how efficiently the Chinese systems are utilized in comparison to the larger US-based systems, that situation is likely to be temporary and could be addressed by a change in funding models. It is abundantly clear that China is much more serious about increasing funding for HPC systems while US funding levels continue to demonstrate a lack of commitment to maintaining development beyond specific areas such as high energy physics.

TOP500: “I Have Seen The Future and it Isn’t Exascale”
The nature of HPC architectures, workloads and markets are all in flux in the next few years, and an established metric such as the TOP500 is an invaluable tool to monitor the trends. Even though HPL is no longer the only relevant benchmark, the emergence of complementary benchmarks such as HPCG serves to enhance the value of the TOP500, not to diminish it. It will take a while to generate a body of data equivalent to the extensive resource that has been established by the TOP500.

What we can see from the current trends shown in the November 2015 TOP500 list is that although an exascale machine will be built eventually, it almost certainly will not be delivered this decade and possibly not until half way through the next decade. Looking at the current trends it is also very unlikely that the first exascale machine will be built in the USA, and much more likely that it will be built in China.

Over the next few years the TOP500 is likely to remain the best tool we have to monitor geo-political and technology trends in the HPC community. If the adage coined by the Council on Competitiveness, “To out-compute is to out-compete” has any relevance, the TOP500 will likely provide us with a good idea of when the balance of total compute capability will shift from the USA to China.

Peter ffoulkes – Partner at OrionX.net

Peter ffoulkes

Peter is an expert in Cloud Computing, Big Data, and HPC markets, technology trends, and customer requirements which he blends to craft growth strategies and assess opportunities.

www.OrionX.net

OpenStack Cloud: “And then there were five!”

Peter ffoulkes /

September 1, 2015

The IT industry is arguably going through its biggest transformation since the introduction of the IBM PC in the early 1980s, and a cloud service based approach is the likely destination for most enterprise workloads. On August 26th and 27th Mirantis hosted the “second annual” OpenStack Silicon Valley event at the Computer History Museum in Mountain View to a standing room only audience.

Setting the scene for the conference, Alex Freedland, Co-Founder and President of Mirantis, pointed out the market momentum gained by OpenStack in five short years, displacing other potential cloud platforms and propelling it very firmly to being one of just five viable cloud computing platforms for enterprise deployment: Amazon Web Services, Google Cloud Platform, Microsoft Azure, VMware, and the OpenStack Community and Partner Ecosystem.

On the surface this might seem to be a rather bold claim for such a young open source technology offering as OpenStack, but based on my end-user studies at 451 Research prior to joining OrionX, I find the claim entirely credible. It is important to remember that despite all of the hype surrounding cloud computing, most enterprises are still in the early stages of cloud adoption and that it is a complex transition. Although the technology platforms to build a cloud are critical to success, there are many more significant factors that will contribute to success. First and foremost, the shift to a cloud-based approach to delivering IT services is a huge cultural transition and with such transitions also come substantial business risk. This was one of the key findings of my own research, and also the main theme of the talk by James Staten, Chief Strategist of the Cloud and Enterprise division at Microsoft and until recently Vice President & Principal Analyst for cloud computing at Forrester Research.

Another factor in a successful transition is choosing the right partners for technology and business solutions. It is here that OpenStack gains enormous credibility, not so much from the current level of maturity of the OpenStack technology platform but from the massive amount of investment in both money and developers that is being poured into the effort to make OpenStack successful, something that simply cannot be matched by any single company on the planet today. There is still much to be done to turn OpenStack into a true enterprise-grade, production-ready cloud platform but it is also clear that OpenStack is funded for success, which is one very important consideration for any enterprise making a significant bet on the foundation for its future IT services.

There are too many proof points to mention that underpin the level of activity and initiatives supporting the momentum behind OpenStack, but a few recent and noteworthy ones are:

On Monday August 24^th, Mirantis benefited from a $100 million investment led by Intel Capital as part of Intel’s Cloud for All initiative
Also as part of the Cloud for All initiative, on July 23^rd Rackspace and Intel announced the OpenStack Innovation Center, designed to accelerate the testing and deployment of scale-out OpenStack environments.
Even the IRS likes OpenStack! On August 26^th, after a lengthy process, including an initial denial and an appeal, the OpenStack Foundation was granted 501c6 tax exempt status, a clear recognition of the positive impact that the Foundation is having on the cloud computing industry.

So, are we all off to see the wizard, the wonderful wizard of OS [OpenStack]?

Possibly, but not quite so fast! There are still lions and tigers and bears, Oh my! There is still much work to do to bring OpenStack, and indeed all the other credible cloud platforms up to production enterprise deployment standards, and this will take time even if the money and the will is there, which it appears to be for OpenStack. Here are just a few of the initiatives outlined at the OpenStack Silicon Valley summit that point to what is in the pipeline to make OpenStack more enterprise ready:

Core Improvements:

Strategic Investments

The long and winding road

Perhaps the biggest issue of all to be addressed is the current scarcity of OpenStack expertise and developers. They seem to be worth more than their weight in gold or Bitcoins, and they also seem to know it! These are some of the driving forces behind initiatives such as Intel’s Cloud for All, and why a company like Mirantis is gaining recognition for its expertise, and investment that enhances its ability to influence the future of cloud computing.

The journey to “the cloud” is a long and twisty one, and there will be more than one destination, but for now OpenStack seems likely to be one of the five most viable destinations. From here it appears to be more real than it does to be a mirage.

Peter ffoulkes, Partner OrionX

Peter ffoulkes

Peter is an expert in Cloud Computing, Big Data, and HPC markets, technology trends, and customer requirements which he blends to craft growth strategies and assess opportunities.