Is HPC so specialized that the lessons learned from large-scale infrastructure (at all layers) are not transferrable to mirrored challenges in large-scale enterprise settings?

Put another way, are the business-critical problems that companies tackle really so vastly different than the associated hardware and software issues that large supercomputing centers have already faced and in many areas, overcome? Granted, there is already a significant amount of HPC to be found in enterprise datacenters worldwide in a number of areas—oil and gas, financial services, the life sciences, government and more. But as everything in technology seems bent on convergence, is there not a wider application for HPC-driven technologies in an expanding set of markets?

This is the first part of a series of focused pieces around these framing questions about HPC’s map into the wider world.  The sections of our extended special feature will target HPC-to-enterprise lessons in terms of hardware and infrastructure; software and applications; management at scale; cloud computing; big data; accelerators and more. But to kick things off, we wanted to build consensus around some of the main themes and ideas behind any movement that’s happening (or needs to) as HPC lessons trickle into the scale, efficiency, performance and data-conscious world of the modern enterprise.

In some circles, HPC is viewed from afar as an academic-only landscape, dotted with rare peaks representing actual enterprise use. Of course, those inside supercomputing know that this portrait is limited—that HPC has a strong foothold in the areas mentioned above, and tremendous potential to reshape new areas that either thought HPC was out of reach or are using HPC but simply don’t use the term. What is needed is a comprehensive view of how HPC can be broadly useful to critical segments enterprise IT…and that’s what we ntend to offer over the next couple of weeks.

The answer about whether or not there are a multitude of lessons HPC can teach the wider enterprise world, at least according to those we’ve spoken with for our the series on this subject, is resounding and positive. If there’s any disagreement, it’s on how those lessons translate, which are truly unique in the HPC experience, and of course, which hold the most promise for improved productivity, competitiveness or even application area.

Addison Snell, CEO of Intersect360 Research, whose research group follows the overlap between enterprise and HPC, made some parallels to put the question in context. “Traditionally, one of the characteristics that separated HPC from enterprise computing was that HPC featured jobs that would run to completion, and there would be a benefit in completing them faster, such as running a weather forecast, simulating a crash test, or searching for proteins that fit together with a given molecule.” However, he says by contrast, enterprise environments are designed to run in steady state (email systems, CRM databases, etc.). “HPC purchases would tend to be driven by performance, with relatively faster adoption of new technologies, while enterprise computing was driven by reliability and new technology adoption with slower technology adoption.”

“Early adopters and bellwethers in high performance computing are always the first to encounter new challenges as they push the limits of computation and data management,” Herb Schultz from IBM’s Technical Computing and Analytics group argued.  He says that many of the challenges faced in the world of high performance computing “later come to haunt the broader commercial IT community.” “How first movers respond to challenges with new technologies and improved techniques establishes a proven foundation that the next waves of users can exploit.”

As Fritz Ferstl, CTO at Univa told us, there are essentially three “divisions” of in the HPC industry. There are the national labs and big science organizations; enterprise commercial HPC (as found in the expected verticals, including oil and gas, financial services, life sciences, etc.); and there is “a third not often recognized as HPC but rather as data-centric analysis, also known as big data.”

Ferstl says that while the lab-level HPC category is “specific in that its leading edge requires tightly coupled architectures with the densest network interconnects, which drive up cost and complexity. They are geared toward running few ultra-large applications that demand aggregate memory and would take unacceptable amounts of runtime if not executed on such large systems.” One step away from this is the commercial sectors that rely on HPC for their competitive edge. Of these, Ferstl notes whether its new reservoirs of oil and gas being explored, next generation products like cars or airplanes being designed and tested, or innovative drugs being discovered, “there would be no progress in any of these cases and many more if it wasn’t for HPC as a key instrument for investigation, design, development, experimentation and validation.”

But final on his list—and crucial to the enterprise transition (and HPC’s lessons to teach it) is the heavy subject of data. What’s really driving this forward motion of HPC tech into the enterprise is that buzzword we just can’t get away from these days. Some might argue that the trend has actually been one of the best things that’s happened for HPC’s ability to propel into the wider enterprise world.

Snell commented that, “today, especially with big data analytics, more companies are encountering performance-sensitive applications that run to completion—at least in terms of iterations.” He said his research has revealed that new categories of non-HPC enterprise users are emerging, all of whom are considering performance and scalability as top purchase criteria. “In some cases,” he said, “these enterprises can be just as likely to explore new technologies as HPC users have been for years.”

Some argue that in general, aside from being a question of data pressures, business need, and competitive edge, the real lessons HPC can teach are about talent and R&D capability. As Paul Dlugosch, Automata product director at Micron described, “One of the first lessons that come to mind is that people matter. While the HPC industry often celebrates our accomplishments on the basis of technical and performance benchmarks, the cost of achieving those benchmarks are often not discussed.  The cost of system and semiconductor development can be easy enough to quantify.  It is far more difficult, though, to determine the ‘use’ cost of advanced technologies. “While the raw power of our semiconductors and systems is immense it is the organic part of the system, the human being– that is emerging as a significant bottleneck,” said Dlugosch.

“Fully exploiting the parallelism that exists in many high performance computing systems continues to absorb incredible amounts of human resources,” he argued. “Given the large scale of commercial/enterprise data centers, it is just as important to pay close attention to this human factor.  The HPC industry is certainly aware of this problem and is developing new architectures, tools and methodologies to improve human productivity. As commercial and enterprise data centers grow in capability and scale it will become just as important to consider the productivity of the humans involved in system programming, management and scaling.”

It should be noted that on any level of this question, it’s not a clear matter of teaching from the top to bottom. While HPC has solved a number of problems in some of the most challenging data and compute environment, especially in terms of scale, data movement, application complexity and elsewhere, there are elements that can filter from the enterprise setting to HPC—even that “big national lab” variety Ferstl describes.

There is general agreement that there are multiple lessons that high performance computing can carry into mainstream enterprise environments, no matter what vertical is involved. But on the flipside, there has been general agreement that many innovations are spinning out of the new class of enterprise environments—that the web scale companies with their bare-bones hardware running open source, natively developed, and purpose-built, nimble applications—have something to offer the supercomputing world as well.

Jason Stowe, CEO of HPC cloud company, Cycle Computing put it best when he told us, “We in HPC pay attention to the fastest systems in the world: the fastest CPUs, interconnects, and benchmarks. From petaflops to petabytes, we [in HPC] publish and analyze these numbers unlike any other industry…While we’ll continue to measure things like LINPACK, utilization, and queue wait times, we’re now looking at things like Dollars per Unit Science, and Dollar per Simulation, which ironically, are lessons that has been learned from enterprise.”

From the people who power both enterprise and HPC systems to the functional elements of the machines and how they differ, there are just as many new questions that emerge from the first—what can HPC lend to large-scale business operations?

Stay tuned over the next two weeks as this series expands and hones in on specific issues and topics that influence how enterprises will look to HPC for answers to solving scale, data, management and other challenges.

CONTINUE to PART II — “HPC Roots Feed Big Data Branches”