Waiting for Exascale

By Gary Johnson

January 22, 2013

By current estimates, we’re about a decade away from having exascale computing capability. That’s a pretty long time, especially in our world of HPC. What will the world be like in 2022? What form will exascale computing take when it’s real? These are difficult questions to answer. Never before has the HPC community focused so intensively on a machine so far beyond its grasp. Nevertheless, stalwart cadres around the globe are drafting strategies, plans, and roadmaps to get from here to exascale.

So, what about the rest of us? Are there useful things we could do while waiting – or instead of waiting – for exascale? Perhaps there are. Let’s take a look at a few possibilities.

Education

Education is usually the last item on wish lists. We’ve put it first because, over the next decade, it will make or break US competitiveness. Also, we are talking about education here, as distinct from training. We have no real idea about what skills will be needed in the workforce a decade from now. However, it is safe to say that if we cultivate education and people learn how to learn, then they will be able to adapt to the future environment and acquire the skills they need to accomplish the tasks at hand.

For example, how about thinking through fundamental reformulations of our problems and creating ones that are intrinsically massively parallel – or developing completely new applications in social sciences, art, history, or music?

To focus more narrowly, our exascale workforce is now (at best) in high school. How can we ensure that they’ll be ready to exploit these wonderful new machines we’re intent on building? Perhaps we could exploit the budding MOOC (Massive Open Online Course) movement (e.g. Coursera, Udacity, edX) by providing free, open and web-accessible educational resources specifically tailored to the learning needs of our future exascalers.

To provide some rewards and recognition for those who would create these exaMOOCs, the federal agencies currently funding exascale research could expand their efforts to also include creation of exascale educational resources. Since many of those we wish to engage live in the academic “publish or perish” world, we could create appropriate high-prestige publication venues – ones that would duly impress academic promotion and tenure committees.

eScience

Are your high-end computers under your desk? Probably not. How often do you go “kick the tires” of your high-end computing resources? Probably seldom, if ever. So, from your perspective, where are they? If you don’t care where they are because anywhere will do as long as they are accessible to you, then you’re doing eScience. Nonetheless, most of us HPCers seem to think that eScience isn’t what we do and isn’t of interest to us.

In observational, experimental, and computational sciences, device environments are evolving, ranging from hand-held equipment up through unique national and international resources. Doing science means bringing any and all parts of these environments into play to accomplish your objectives. Thus, all of science is becoming eScience.

Accommodating this model of work will require shifting our view of capability computing from one that is facility-centric to one that is focused on the scientists and engineers we call our “end users.” Because of the financial interests associated with large user facilities, this shift will be difficult. However, there are a couple of steps that we could take now.

Federal agencies currently fund computational research by providing money for labor and processor cycles, in the form of an allocation, from public-funded facilities to cover computing needs. This funding model could be gradually shifted – we’ve got a decade to get it done before exascale arrives – to one that provides money to cover both labor and computing costs.

Such an arrangement would free “end users” to compute wherever it works best for them. They could buy their own computers, pool resources with colleagues for purchase of group computers, buy cycles in the cloud, or cash their money back in at those federally funded computing facilities. To the extent that the computing marketplace is rational and efficient, such a funding model should function well and provide the funding agencies with valuable feedback on what their “end users” really want. For further thoughts on this topic, see Jailbreaking HPC.

The other step toward a user-centric exascale environment is answering the question: Is there an (exascale) app for that? What we mean by this question is: Will the power of the connected exascale environment be readily accessible to users through their personal computing devices? By 2022, the answer needs to be “yes.” So, let’s start cultivating that point of view now and develop some terascale and petascale connected apps for personal computing devices, e.g., smart phones, tablets and whatever else may arrive as we continue down the path to exascale.

Big Data

The year 2012 saw exascale stall and “big data” surge. The ascendance of big data will continue, and this is especially true for unstructured data along with its analysis and visualization tools. It is simply too important to the economy and national security for this not to happen.

We in HPC have tended to ignore this area but, as discussed in a previous article (see Big Data Is HPC – Let’s Embrace It), it’s time to broaden our understanding of HPC and bring big data into the fold. Embracing it will open HPC up to a slew of new and interesting applications. It will also help us prepare for dealing with the data that exascale simulations will produce.

Broad HPC Deployment

Among those working at the leading edge of HPC, petascale computing is seen as a done deal. Making exascale happen is where the action is. We tend to forget that most of the computing world is operating at terascale or below.

HPC is a tool. Ultimately, its success must be measured through its adoption and use. Focusing so strongly on a performance target that is a factor of a million higher than the performance level currently experienced by the majority of HPC users may not be a good idea.

Certainly, the high end of computing needs to advance and due attention should be given to making that happen. Just as certainly, pushing the peak higher will not be useful unless we broaden the HPC base. We need to bring more people into active HPC use and we need to help users migrate upward in performance from terascale apps to petascale and beyond.

How might this be accomplished? Through the sort of educational activities mentioned above; by committing strongly to the development of new apps (see Meet the Exascale Apps), rather than just continuing to port the same old legacy apps into environments for which they were not designed and to which they are unsuited. And by making petascale computing ubiquitous (see Petaflop In a Box).

Computing in Industry

“Enhancing our economic competitiveness” is a standard item on the short list of justifications for pushing the HPC performance envelope. This argument would be more credible if we actually focused on enhancing industrial use of HPC. We already do that, you say? Well, if so, why is there such broad acceptance of the idea that there is a “missing middle” in HPC? Why are so many industrial users computing at the terascale and below?

We can’t have federal agencies intervening in industry’s business, you say? That would constitute the government setting industrial policy, and that would be just plain wrong. Well, what about NASA? Concerted efforts by NASA transformed the aerospace industry into one that makes heavy use of HPC, and is much better off for it. Could the Department of Energy do the same for the energy industry? Might that not be helpful to the economy?

Engagement

Will HPC still be an exclusive club a decade from now? In any case, should it be? Might we not be better off to engage as many people as possible in our enterprise? We think the answers are: no, no, and yes.

The more that people are engaged in computing, the better they will understand it – and support it. There are already signs that suggest broad engagement of the public in computing is feasible. Think of science activities like the Christmas Bird Count, NASA’s Zooniverse, or Foldit, just to name a few. There is clearly a cognitive surplus out there, ready to be useful.

Perhaps HPC and computational science should take greater advantage of this. This movement has been called Citizen Science. We think that has a nice ring. Between now and exascale, let’s get major citizen involvement in computational science and HPC.

How might his be done? Several possibilities come to mind. We could try crowd sourcing some software and hardware development. On the software side, crowd sourcing has already gained some popularity (e.g., TopCoder). Given the widespread availability of components, like GPUs, prototyping platforms (e.g., Arduino & Raspberry Pi), and other components (e.g., Adafruit Industries), hardware development doesn’t need to be just a spectator sport.

Those who choose not to participate in crowd sourcing might like to try crowd funding. The general public currently funds creative projects of many types through sites like Kickstarter, Indegogo and Petridish. Perhaps there’s room for sites that focus on topics related to HPC.

We could make use of hand-held devices and the growing “Internet of things” to develop new applications – making measurements and gathering data in a broadly dispersed fashion and then moving it through the HPC environment to be further processed, analyzed, and visualized. Depending on the application, this could involve active communication up through our highest end computing resources.

One interesting example of what might be accomplished is Quake Catchers, a concept developed by seismologists at the University of California Riverside that uses the general public’s laptop and smart phone accelerometers to improve earthquake warning systems. Another recent one involves using smart phones to predict the weather (see Your Android Phone Could Help Scientists Predict Your Weather). The areas of fitness, wellness and heath care are replete with additional opportunities.

Infrastructure

Between now and exascale, we need to do a whole lot of infrastructure development and build out. No matter what we’ll be calling the cloud by then, everything will be in it, from personal devices and Internet-enabled things up through those exascale computers. Robustness, connectivity and communications bandwidth will be keys to the success of this environment.

In a recent report card for America’s infrastructure, the American Society of Civil Engineers (ASCE) gave the US an overall grade of D. The ASCE didn’t specifically address computing and communications infrastructure, but the Technology CEO Council asserts that “The national information and telecommunications infrastructure currently deployed for today’s technological applications is not robust enough to support the technological advancements of the future.” Clearly there are lots of things we need to start doing now in order to have the infrastructure necessary to exploit exascale later.

Success Metrics

In moving forward with HPC there is also a lot of rethinking we need to devote to our success metrics. Kudos go to Bill Kramer at the National Center for Supercomputing Applications (NCSA) for taking the courageous step of opening the conversation on this topic (see Top problems with the TOP500 and Blue Waters Opts Out of TOP500). More recently, Bill Gropp, also from NCSA, has joined this conversation (see 2013:Time to stop talking about Exascale).

Alongside the TOP500, the Green500 and Graph 500 lists have gained in popularity in recent years and other possibilities have been suggested (see HPC Lists We’d Like to See), but the success metric issue remains an open one. Computers are tools and we need to measure their success by how well they enable discovery and solve problems. We’re not there yet, but maybe we can get a bit closer before 2022.

Let’s Not Wait

Exascale computing may be a decade away, but there’s a lot to accomplish to be ready to exploit it. We’ve explored a few options here. We make no claim that these constitute the right agenda for the coming decade, nor do we suggest that we’ve given an exhaustive to-do list. Our intention is rather to open the conversation about what we should do while “waiting” for exascale. So, let us know what you think.

About the Author

Gary M. Johnson is the founder of Computational Science Solutions, LLC, whose mission is to develop, advocate, and implement solutions for the global computational science and engineering community.

Dr. Johnson specializes in management of high performance computing, applied mathematics, and computational science research activities; advocacy, development, and management of high performance computing centers; development of national science and technology policy; and creation of education and research programs in computational engineering and science.

He has worked in academia, industry and government. He has held full professorships at Colorado State University and George Mason University, been a researcher at United Technologies Research Center, and worked for the Department of Defense, NASA, and the Department of Energy.

He is a graduate of the U.S. Air Force Academy; holds advanced degrees from Caltech and the von Karman Institute; and has a Ph.D. in applied sciences from the University of Brussels.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a community infrastructure in support of machine learning research Read more…

By John Russell

DARPA Continues Investment in Post-Moore’s Technologies

July 24, 2017

The U.S. military long ago ceded dominance in electronics innovation to Silicon Valley, the DoD-backed powerhouse that has driven microelectronic generation for decades. With Moore's Law clearly running out of steam, the Read more…

By George Leopold

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in 2017 with scale-up production for enterprise datacenters and Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

Trinity Supercomputer’s Haswell and KNL Partitions Are Merged

July 19, 2017

Trinity supercomputer’s two partitions – one based on Intel Xeon Haswell processors and the other on Xeon Phi Knights Landing – have been fully integrated are now available for use on classified work in the Nationa Read more…

By HPCwire Staff

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a comm Read more…

By John Russell

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

Women in HPC Luncheon Shines Light on Female-Friendly Hiring Practices

July 13, 2017

The second annual Women in HPC luncheon was held on June 20, 2017, during the International Supercomputing Conference in Frankfurt, Germany. The luncheon provid Read more…

By Tiffany Trader

Satellite Advances, NSF Computation Power Rapid Mapping of Earth’s Surface

July 13, 2017

New satellite technologies have completely changed the game in mapping and geographical data gathering, reducing costs and placing a new emphasis on time series Read more…

By Ken Chiacchia and Tiffany Jolley

Intel Skylake: Xeon Goes from Chip to Platform

July 13, 2017

With yesterday’s New York unveiling of the new “Skylake” Xeon Scalable processors, Intel made multiple runs at multiple competitive threats and strategic Read more…

By Doug Black

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

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

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This