Chasing 1000X: The Future of Supercomputing Is Unbalanced

By Andrew Jones

October 10, 2012

Our supercomputing community is the world of 1000X. That is how we introduce ourselves – “think thousands of times more powerful than your laptop.” We proudly proclaim our thousands of cores, nodes, kilowatts, gigabytes, cables, and so on.

We even measure our progress by 1000X: the terascale barrier (“smashed” according to the tone of the accompanying press release) then a 1000X to the petascale barrier (“shattered” as the marketing machine informed us) and now chasing 1000X to the exascale barrier (which will be “cataclysmically destroyed” I presume).

These barriers are fun, but nonsense. Obviously 1.01 petaflops of computing power is not disruptively more capable than 0.99 petaflops, nor is it qualitatively more technically challenging. So perhaps the better description is “crept past the terascale marker” and “sauntered by the petascale signpost?”

As a community we have, for several decades now, developed technologies and deployed systems that have grown through the real challenges identified for each 1000X increment. And we have done this so effectively that each “barrier” is very soft by the time we come to deploy systems of that size. That doesn’t undermine the technical challenges involved in each case, nor the efforts of those who have mastered those challenges. But they have mostly been solved by aggressive evolution incited by the occasional disruptive kick in the behind.

Even though we use 1000X as our badge of meaningful advance, we can be very narrow in how we apply that across the breadth of our empire. We mostly tie it to speed or size of the machines – a thousand times faster or bigger. We are starting to grow group behavior for some other uses, for example, 1000X more power efficient. But we are still focusing on the machines.

Performance is fundamental to the value proposition of high performance computing, whether 10X or 1000X. And even in the case of 1000X performance, there is much more we can explore than we do now. The obvious opportunity is to recognize that such performance is most effectively obtained not from hardware alone, but also from innovation in algorithms and software implementation.

I regularly write on this topic at my blog and speak on this topic at conferences and private events. But at one recent IDC HPC User Forum the conversation turned to one of my other favorite themes on what we can do better.

There is much more to our community that we should look at for step change, innovation and leadership than purely performance. Why do we not target the same 1000X in other areas? Think of the benefits of supercomputing, at any scale, being a thousand-fold easier to use. Not benefits to the existing hardcore tweakers of MPI, since these folks don’t need (or even desire?) easy to use. They need performance and the flexibility of direct access to the capabilities.

What about the benefits to everyone else? And I said users. Not programmers. Not all users of HPC are programmers (a working assumption that supercomputer centers often default to). Many users of HPC just run applications. Someone else has done the programming for them, either in-house development teams or codes from commercial providers or other research groups, etc.

How different is our ease-of-use experience with other computing technology? Think of your laptop for office tasks; your tablet computer for consuming Web and media content; and your smartphone for processing emails. Compare those user experiences with HPC.

We take something with a thousand times the compute power and make it harder – even arcane – to use! A significant portion of the computer power on those consumer devices is applied to the user interaction experience. Surely, with a few spare tens of teraflops to play with – only a few percent of our petascale supercomputers – we can come up with a more human-friendly interaction than batch scripts. No, it won’t be more efficient. Tough!

Our community has chased efficiency in utilization of the compute resource arguably way beyond its cost-benefit pay-off and into the realm of limiting the potential of the systems/services for flexibility in use cases and attractiveness to new users.
And that brings me to another 1000X: the growth of HPC to a thousand times more users. Pick your favorite label of the year: personal supercomputing (possibly self-contradictory), missing middle (who wants to be someone else’s middle?), HPC for the masses (are we talking revolutions?), democratization of HPC (in my view the worst label of the lot – HPC shouldn’t be worrying about democracy or not), and so on. This user growth is how the technology, or rather, its core proposition, can benefit society and the economy with much more immediate, widespread and direct impact.

Driving the 500 fastest supercomputers in the world to a thousand times their performance does deliver value to the economy and society. Not just through the computing technology advances they inspire and require, but especially through the scientific, medical and engineering advances their use enables. But each new group of engineers and scientists that are able to exploit effective modeling and simulation in their research and design can invigorate their contribution to the economy.

Multiply these individual effects by 1000X and we might see light shining into the knowledge economy that is the dream of politicians the world over. Creating and sustaining a high-tech economy doesn’t happen by a handful of leadership supercomputers used by the few. It happens by doing that and also enabling 1000X more companies to use HPC techniques. Both upward and outward are needed.

Our existing HPC community has to play its role in this. We cannot just focus on driving the fastest machines a thousand times faster. Critically, we have to give equal peer recognition to those who focus on driving the use of the technology a thousand times broader and a thousand times easier to use.

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!

AI in the News: Rao in at Intel, Ng out at Baidu, Nvidia on at Tencent Cloud

March 26, 2017

Just as AI has become the leitmotif of the advanced scale computing market, infusing much of the conversation about HPC in commercial and industrial spheres, it also is impacting high-level management changes in the industry. Read more…

By Doug Black

Scalable Informatics Ceases Operations

March 23, 2017

On the same day we reported on the uncertain future for HPC compiler company PathScale, we are sad to learn that another HPC vendor, Scalable Informatics, is closing its doors. Read more…

By Tiffany Trader

‘Strategies in Biomedical Data Science’ Advances IT-Research Synergies

March 23, 2017

“Strategies in Biomedical Data Science: Driving Force for Innovation” by Jay A. Etchings is both an introductory text and a field guide for anyone working with biomedical data. Read more…

By Tiffany Trader

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 assets. Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

HFT Firms Turn to Co-Location to Gain Competitive Advantage

High-frequency trading (HFT) is a high-speed, high-stakes world where every millisecond matters. Finding ways to execute trades faster than the competition translates directly to greater revenue for firms, brokerages, and exchanges. Read more…

Google Launches New Machine Learning Journal

March 22, 2017

On Monday, Google announced plans to launch a new peer review journal and “ecosystem” Read more…

By John Russell

Swiss Researchers Peer Inside Chips with Improved X-Ray Imaging

March 22, 2017

Peering inside semiconductor chips using x-ray imaging isn’t new, but the technique hasn’t been especially good or easy to accomplish. Read more…

By John Russell

LANL Simulation Shows Massive Black Holes Break ‘Speed Limit’

March 21, 2017

A new computer simulation based on codes developed at Los Alamos National Laboratory (LANL) is shedding light on how supermassive black holes could have formed in the early universe contrary to most prior models which impose a limit on how fast these massive ‘objects’ can form. Read more…

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. 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 assets. 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. Read more…

By John Russell

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 campaign. 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

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

New Japanese Supercomputing Project Targets Exascale

March 14, 2017

Another Japanese supercomputing project was revealed this week, this one from emerging supercomputer maker, ExaScaler Inc., and Keio University. The partners are working on an original supercomputer design with exascale aspirations. Read more…

By Tiffany Trader

Nvidia Debuts HGX-1 for Cloud; Announces Fujitsu AI Deal

March 9, 2017

On Monday Nvidia announced a major deal with Fujitsu to help build an AI supercomputer for RIKEN using 24 DGX-1 servers. Read more…

By John Russell

HPC4Mfg Advances State-of-the-Art for American Manufacturing

March 9, 2017

Last Friday (March 3, 2017), the High Performance Computing for Manufacturing (HPC4Mfg) program held an industry engagement day workshop in San Diego, bringing together members of the US manufacturing community, national laboratories and universities to discuss the role of high-performance computing as an innovation engine for American manufacturing. Read more…

By Tiffany Trader

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Leading Solution Providers

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. 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 campaign. Read more…

By John Russell

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. 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. Read more…

By John Russell

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

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