Exascale: Power Is Not the Problem!

By Andrew Jones

August 29, 2011

To build exascale systems, power is probably the biggest technical hurdle on the hardware side. In terms of getting to exascale computing, demonstrating the value of supercomputing to funders and the public is a more urgent challenge. But the top roadblock for realizing the potential benefits from exascale is software.

That title is probably controversial to most readers. It is likely that if you asked members of the supercomputing community what is the single biggest challenge for exascale computing, the most common answer would be “power.” It is widely reported, widely talked about, and in many places, generally accepted that finding a few orders of magnitude improvement in power consumption is the biggest roadblock on the way to viable exascale computing. Otherwise, the first exascale computers will require 60MW, 120MW or 200MW — pick your favorite horror figure. I’m not so convinced.

I’m not saying the power estimates for exascale computing are not a problem — they are — but they are not the problem. Because, in the end, it is just a money problem. For most in the community, the objection is not so much to the fact of 60-plus MW supercomputers. Instead, the objection is the resulting operating costs of 60-plus MW supercomputers. We simply don’t want to pay $60 million each year for electricity (or more precisely we don’t want to have to justify to someone else — e.g., funding agencies — that we need to pay that much). But why are we so concerned about large power costs?

Are we really saying, with our concerns over power, that we simply don’t have a good enough case for supercomputing — the science case, business case, track record of innovation delivery, and so on? Surely if supercomputing is that essential, as we keep arguing, then the cost of the power is worth it.

There are several large scientific facilities that have comparable power requirements, often with much narrower missions — remember that supercomputing can advance almost all scientific disciplines — for example, LHC, ITER, NIF, and SNS. And indeed, most of the science communities behind those facilities are also large users of supercomputing.

I occasionally say, glibly and deliberately provocatively, if the scientific community can justify billions of dollars, 100MW of power, and thousands of staff in order to fire tiny particles that most people have never heard of around a big ring of magnets for a fairly narrow science purpose that most people will never understand, then how come we can’t make a case for a facility needing only half of those resources that can do wonders for a whole range of science problems and industrial applications?

[There is a partial answer to that, which I have addressed on my HPC Notes blog to avoid distraction here.]

But secondly, and more importantly, the power problem can be solved with enough money if we can make the case. Accepting huge increases in budgets would also go a long way toward solving several of the other challenges of exascale computing. For example, resiliency could be substantially helped if we could afford comprehensive redundancy and other advanced RAS features; data movement challenges could be helped if we could afford huge increases in memory bandwidth at all levels of the system; and so on.

Those technical challenges would not be totally solved but they would be substantially reduced by money. I don’t mean to trivialize those technical challenges, but certainly they could be made much less scary if we weren’t worried about the cost of solutions.

So, the biggest challenge for exascale computing might not be power (or your other favorite architectural roadblock) but rather our ability to justify enough budget to pay for the power, or more expensive hardware, etc. However, beyond even that, there is a class of challenges for which money alone is not enough.

Assume a huge budget meant an exascale computer with good enough resiliency, plenty of memory bandwidth and every other needed architectural attribute was delivered tomorrow, and never mind the power bills. Could we use it? No. Because of a series of challenges that need not only money, but also lots of time to solve, and in most cases need research because we just don’t know the solutions.

I am thinking of the software related challenges.

Even if we have highly favorable architectures (expensive systems with lots of bandwidth, good resiliency, etc.) I think the community and most, if not all, of the applications are still years away from having algorithms and software implementations that can exploit that scale of computing efficiently.

There is a reasonable effort underway to identify the software problems that we might face in using exascale computing (e.g., IESP and EESI). However, in most cases we can only identify the problems; we still don’t have much idea about the solutions. Even where we have a good idea of the way forward, sensible estimates of the effort required to implement software capable of using exascale computing — OS, tools, applications, post-processing, etc. — is measured in years with large teams.

It certainly requires money, but it needs other scarce resources too, specifically time and skills. That involves a large pool of skilled parallel software engineers, scientists with computational expertise, numerical algorithms research and so on. Scarce resources like these are possibly even harder to create than money!

Power is a problem for exascale computing, and with current budget expectations is probably the biggest technical challenge for the hardware. In terms of getting to exascale computing, demonstrating the value of increased investment in supercomputing to funders and the public/media is probably a more urgent challenge. But the top roadblock for achieving the hugely beneficial potential output from exascale computing is software. There are many challenges to do with the software ecosystem that will take years, lots of skilled workers, and sustained/predictable investment to solve.

That “sustained/predictable” is important. Ad-hoc research grants are not an efficient way to plan and conduct a many-year, many-person, community-wide software research and development agenda. Remember that agenda will consume a non-trivial portion of the careers of many of the individuals involved. And when the researchers start out on this necessary software journey, they need confidence that funding will be there all the way to production deployment and ongoing maintenance many years into the future.

About the Author

Andrew is Vice-President of HPC Services and Consulting at the Numerical Algorithms Group (NAG). He was originally a researcher using HPC and developing related software, later becoming involved in leadership of HPC services. He is also interested in exascale, manycore, skills development, broadening usage, and other future concerns of the HPC community.

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!

UCSD, AIST Forge Tighter Alliance with AI-Focused MOU

January 18, 2018

The rich history of collaboration between UC San Diego and AIST in Japan is getting richer. The organizations entered into a five-year memorandum of understanding on January 10. The MOU represents the continuation of a 1 Read more…

By Tiffany Trader

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Tennessee), Satoshi Matsuoka (Tokyo Institute of Technology), Read more…

By John Russell

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown and Spectre security updates on the performance of popular H Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

HPE and NREL Take Steps to Create a Sustainable, Energy-Efficient Data Center with an H2 Fuel Cell

As enterprises attempt to manage rising volumes of data, unplanned data center outages are becoming more common and more expensive. As the cost of downtime rises, enterprises lose out on productivity and valuable competitive advantage without access to their critical data. Read more…

Fostering Lustre Advancement Through Development and Contributions

January 17, 2018

Six months after organizational changes at Intel's High Performance Data (HPDD) division, most in the Lustre community have shed any initial apprehension around the potential changes that could affect or disrupt Lustre Read more…

By Carlos Aoki Thomaz

UCSD, AIST Forge Tighter Alliance with AI-Focused MOU

January 18, 2018

The rich history of collaboration between UC San Diego and AIST in Japan is getting richer. The organizations entered into a five-year memorandum of understandi Read more…

By Tiffany Trader

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

Fostering Lustre Advancement Through Development and Contributions

January 17, 2018

Six months after organizational changes at Intel's High Performance Data (HPDD) division, most in the Lustre community have shed any initial apprehension aroun Read more…

By Carlos Aoki Thomaz

When the Chips Are Down

January 11, 2018

In the last article, "The High Stakes Semiconductor Game that Drives HPC Diversity," I alluded to the challenges facing the semiconductor industry and how that may impact the evolution of HPC systems over the next few years. I thought I’d lift the covers a little and look at some of the commercial challenges that impact the component technology we use in HPC. Read more…

By Dairsie Latimer

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

Momentum Builds for US Exascale

January 9, 2018

2018 looks to be a great year for the U.S. exascale program. The last several months of 2017 revealed a number of important developments that help put the U.S. Read more…

By Alex R. Larzelere

ANL’s Rick Stevens on CANDLE, ARM, Quantum, and More

January 8, 2018

Late last year HPCwire caught up with Rick Stevens, associate laboratory director for computing, environment and life Sciences at Argonne National Laboratory, f Read more…

By John Russell

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

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

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Leading Solution Providers

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

Tensors Come of Age: Why the AI Revolution Will Help HPC

November 13, 2017

Thirty years ago, parallel computing was coming of age. A bitter battle began between stalwart vector computing supporters and advocates of various approaches to parallel computing. IBM skeptic Alan Karp, reacting to announcements of nCUBE’s 1024-microprocessor system and Thinking Machines’ 65,536-element array, made a public $100 wager that no one could get a parallel speedup of over 200 on real HPC workloads. Read more…

By John Gustafson & Lenore Mullin

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

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