Petaflop In a Box

By Gary Johnson

June 6, 2012

As we move down the road toward exascale computing and engage in discussion of zettascale, one issue becomes increasingly obvious: we are leaving a large part of the HPC community behind. Most engineers and scientists compute, at best, at the terascale level, and these are the people using HPC to enhance our economic competitiveness. In addition to pushing the peak of HPC higher, should we also take steps to broaden its base and bring more of the community along with us? Could broad deployment of compact, power efficient petascale computers help accomplish this?

There has been, and continues to be, lots of discussion about exascale computing. What are the applications drivers for exascale computing? Is power the problem, or is it not the problem? When will we get to exascale – in 2018, 2020, or later? What is the plan for the US Department of Energy’s exascale computing program? Beyond that, zettascale computing is also in play. Will we never get there, or will it happen?

Amidst all of this controversy, there does seem to be a general consensus about the technical barriers to exascale, in particular:

  • Power consumption
  • Data movement
  • Hardware and Software Resiliency
  • Performance-oriented runtime systems software
  • Exposing and exploiting parallelism

Of these barriers, power consumption is a current issue that is exacerbated by moving to exascale. Arguably, the other barriers are more intrinsically exascale ones. Also, note that there are substantial software development requirements for dealing with these other exascale barriers. So, power consumption is a separable issue and may be dealt with at petascale, while leaving the intrinsic exascale barriers to be handled as such.

Despite what one reads in press releases, most scientists and engineers don’t compute at the petascale. Although our most powerful supercomputers are available for industrial use, only a very small fraction of the available time actually goes to industrial applications. A problem that surfaces in science circles more often than one might expect is the shortage of high-end cycles available for day-to-day work, rather than “hero runs” on petascale supercomputers. Everyday science and engineering is carried out largely on computers ranging from notebooks, operating at gigaflops, up through server racks, operating at teraflops. So, since most scientists and engineers compute at the terascale and below, their transition to petascale may be challenging , especially for those running legacy applications on legacy hardware.

Is there a way to help our scientists and engineers get to petascale sooner, while still travelling in the direction of exascale? We’re already computing (on a limited basis) at the petascale and if we extrapolate the historical trend, by about 2016 the bottom computer on the TOP500 list will be operating at a petaflop.

At first glance, that may not look so bad. We can just wait until 2016 and things will take care of themselves. The problem is that the majority of science and engineering is done below the level of that 500th computer and will still be sub-petascale.

If, in addition to pursuing exascale, we spin out petascale boxes as soon as possible, and in large numbers, then we can promote the development of scalable software to move beyond terascale; make petascale computing widely accessible; and help make the transition of the broad science and engineering community to petascale smoother and quicker. Also, when exascale does arrive, it will be more broadly embraced and used.

Our highest-end computing systems are very large and consume lots of power. To reach exascale in any credible way, machine footprints will need to shrink and power consumption will need to come down. Last year, John Kelly from IBM suggested that a byproduct of success in building an exaflop computer would be a petaflop in 1/3 of a rack. If we assume that DOE’s target of 20 megawatts (MW) power consumption for an exascale system is achieved and that 1/3 of a rack is about one cubic meter, then a petaflop in a cubic meter box would consume about 20 kilowatts (kW).

Such a system would consume about as much power as 4 electric clothes dryers. If we wanted to purchase a dedicated off-grid power supply for a petaflop box, we could find one on the internet for about $5,000. (Then we could measure flops/gallon!) On the US electric grid, the average price of 1 kWh in 2011 was 11.20 cents. So, one could operate the system continuously for a year at a power cost of about $20,000. These may be oversimplifications, but you get the point.

So far, so good. Now all we need to do is get a petaflop into that one cubic meter box.

Currently, one Blue Gene/Q cabinet has a volume of just over three cubic meters, holds hardware with a theoretical peak performance of just over 200 teraflops, and typically consumes about 65 kW. So using this technology as an example, to get a petaflop in a cubic meter we’d need to reduce the volume and power consumption by a factor of three and increase performance by a factor of five.

While these factors may be challenging, they certainly don’t seem impossible to achieve. Getting to exascale will require this sort of accomplishment, and a lot more. So, if we focus on getting a petaflop in a box within the next decade or hopefully sooner, we’ll be well on the way to exaflops systems, but without the additional, intrinsic, exascale barriers mentioned previously.

So, one could think of our petaflop box as one node in a thousand-node exascale system. Also note that, because of the expense of data movement at exascale, applications algorithms will probably be designed to minimize that data movement. So, most of the number crunching in exascale computers will probably take place within a “petascale radius.”

These compact petaflop machines would be useful in their own right and could be deployed as a tools for science and industry. Such a deployment could help move a lot more applications scientists and engineers from terascale to petascale computing. Furthermore, these petaflop systems could be shared by multiple users so that more people would be exposed to (at least) teraflop computing.

Right now, both federal policy and computer industry plans seem to be focused on getting to exascale while expecting only a small number of systems to be built and deployed. Clearly, there are scientific, economic and national security applications that require exascale computing. There is also a global competition to get to exascale. So, there doesn’t seem to be much room for doubt about fully engaging in the race to exascale.

However, there may be ways to have our cake and eat it too. There is widespread concern about our economic competitiveness and a common belief that HPC will play major roles in moving our industries forward. So, how about a phased deployment of lots of petascale systems – starting now?

Suppose we undertook, as a matter of national policy, to deploy something like 1,000 petaflop boxes over the next decade. The target system specifications would be as discussed here. But the early prototype systems could be larger, more power consumptive and hosted at “friendly” sites, as they are now. As progress is made toward the design targets, systems can be more broadly dispersed to sites hosted by applications industries, universities and commercial computing service providers.

As the full-scale deployment is approached, the petaflop boxes could be connected into an exascale cloud. This would provide a distributed national resource of interconnected petascale systems, of various architectures, to support new science and renewed economic growth. The user community for this national resource would consist of all those who could make the case for using it effectively.

The cost to the end users would be the same as the cost of using our interstate highway system, namely zero. The cost to the nation would be about the cost of one exascale system. If you think the cost might be higher, drop the deployment size to, say, 500 petaflop boxes. That would still be a large cloud, if not quite exascale.

By the way, exascale systems and exascale clouds are not ideas in competition. The each have their place. It appears that we could get them both as we move forward to the exascale. Also, if there were an exascale cloud, it should include those exascale systems as very powerful nodes, thus becoming a multi-exascale cloud.

What is currently missing is an open, thoughtful and vigorous discussion of petaflop boxes and exascale clouds, a discussion that could serve as a basis for policy formulation. We’ve seen this discussion take place over the past several years, on a global scale, for exascale systems, so it could also happen for broad deployment of petaflop boxes.

Might the petaflop in a box and/or exascale cloud be worthy national objectives? If so, do we have the will to pursue them? 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!

Russian and American Scientists Achieve 50% Increase in Data Transmission Speed

September 20, 2018

As high-performance computing becomes increasingly data-intensive and the demand for shorter turnaround times grows, data transfer speed becomes an ever more important bottleneck. Now, in an article published in IEEE Tra Read more…

By Oliver Peckham

IBM to Brand Rescale’s HPC-in-Cloud Platform

September 20, 2018

HPC (or big compute)-in-the-cloud platform provider Rescale has formalized the work it’s been doing in partnership with public cloud vendors by announcing its Powered by Rescale program – with IBM as its first named Read more…

By Doug Black

Democratization of HPC Part 1: Simulation Sheds Light on Building Dispute

September 20, 2018

This is the first of three articles demonstrating the growing acceptance of High Performance Computing especially in new user communities and application areas. Major reasons for this trend are the ongoing improvements i Read more…

By Wolfgang Gentzsch

HPE Extreme Performance Solutions

Introducing the First Integrated System Management Software for HPC Clusters from HPE

How do you manage your complex, growing cluster environments? Answer that big challenge with the new HPC cluster management solution: HPE Performance Cluster Manager. Read more…

IBM Accelerated Insights

Clouds Over the Ocean – a Healthcare Perspective

Advances in precision medicine, genomics, and imaging; the widespread adoption of electronic health records; and the proliferation of medical Internet of Things (IoT) and mobile devices are resulting in an explosion of structured and unstructured healthcare-related data. Read more…

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Gordon Bell Prize used Summit in their work. That’s impres Read more…

By John Russell

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Nvidia Accelerates AI Inference in the Datacenter with T4 GPU

September 14, 2018

Nvidia is upping its game for AI inference in the datacenter with a new platform consisting of an inference accelerator chip--the new Turing-based Tesla T4 GPU- Read more…

By George Leopold

DeepSense Combines HPC and AI to Bolster Canada’s Ocean Economy

September 13, 2018

We often hear scientists say that we know less than 10 percent of the life of the oceans. This week, IBM and a group of Canadian industry and government partner Read more…

By Tiffany Trader

Rigetti (and Others) Pursuit of Quantum Advantage

September 11, 2018

Remember ‘quantum supremacy’, the much-touted but little-loved idea that the age of quantum computing would be signaled when quantum computers could tackle Read more…

By John Russell

How FPGAs Accelerate Financial Services Workloads

September 11, 2018

While FSI companies are unlikely, for competitive reasons, to disclose their FPGA strategies, James Reinders offers insights into the case for FPGAs as accelerators for FSI by discussing performance, power, size, latency, jitter and inline processing. Read more…

By James Reinders

Update from Gregory Kurtzer on Singularity’s Push into FS and the Enterprise

September 11, 2018

Container technology is hardly new but it has undergone rapid evolution in the HPC space in recent years to accommodate traditional science workloads and HPC systems requirements. While Docker containers continue to dominate in the enterprise, other variants are becoming important and one alternative with distinctly HPC roots – Singularity – is making an enterprise push targeting advanced scale workload inclusive of HPC. Read more…

By John Russell

At HPC on Wall Street: AI-as-a-Service Accelerates AI Journeys

September 10, 2018

AIaaS – artificial intelligence-as-a-service – is the technology discipline that eases enterprise entry into the mysteries of the AI journey while lowering Read more…

By Doug Black

TACC Wins Next NSF-funded Major Supercomputer

July 30, 2018

The Texas Advanced Computing Center (TACC) has won the next NSF-funded big supercomputer beating out rivals including the National Center for Supercomputing Ap Read more…

By John Russell

IBM at Hot Chips: What’s Next for Power

August 23, 2018

With processor, memory and networking technologies all racing to fill in for an ailing Moore’s law, the era of the heterogeneous datacenter is well underway, Read more…

By Tiffany Trader

Requiem for a Phi: Knights Landing Discontinued

July 25, 2018

On Monday, Intel made public its end of life strategy for the Knights Landing "KNL" Phi product set. The announcement makes official what has already been wide Read more…

By Tiffany Trader

CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learni Read more…

By Rob Farber

ORNL Summit Supercomputer Is Officially Here

June 8, 2018

Oak Ridge National Laboratory (ORNL) together with IBM and Nvidia celebrated the official unveiling of the Department of Energy (DOE) Summit supercomputer toda Read more…

By Tiffany Trader

New Deep Learning Algorithm Solves Rubik’s Cube

July 25, 2018

Solving (and attempting to solve) Rubik’s Cube has delighted millions of puzzle lovers since 1974 when the cube was invented by Hungarian sculptor and archite Read more…

By John Russell

AMD’s EPYC Road to Redemption in Six Slides

June 21, 2018

A year ago AMD returned to the server market with its EPYC processor line. The earth didn’t tremble but folks took notice. People remember the Opteron fondly Read more…

By John Russell

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17

Altair

AMD @ SC17

AMD

ASRock Rack @ SC17

ASRock Rack

CEJN @ SC17

CEJN

DDN Storage @ SC17

DDN Storage

Huawei @ SC17

Huawei

IBM @ SC17

IBM

IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17

Intel

Lenovo @ SC17

Lenovo

Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17

Microsoft

Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17

Supericro

Tyan @ SC17

Tyan

Univa @ SC17

Univa

Sandia to Take Delivery of World’s Largest Arm System

June 18, 2018

While the enterprise remains circumspect on prospects for Arm servers in the datacenter, the leadership HPC community is taking a bolder, brighter view of the x86 server CPU alternative. Amongst current and planned Arm HPC installations – i.e., the innovative Mont-Blanc project, led by Bull/Atos, the 'Isambard’ Cray XC50 going into the University of Bristol, and commitments from both Japan and France among others -- HPE is announcing that it will be supply the United States National Nuclear Security Administration (NNSA) with a 2.3 petaflops peak Arm-based system, named Astra. Read more…

By Tiffany Trader

D-Wave Breaks New Ground in Quantum Simulation

July 16, 2018

Last Friday D-Wave scientists and colleagues published work in Science which they say represents the first fulfillment of Richard Feynman’s 1982 notion that Read more…

By John Russell

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

TACC’s ‘Frontera’ Supercomputer Expands Horizon for Extreme-Scale Science

August 29, 2018

The National Science Foundation and the Texas Advanced Computing Center announced today that a new system, called Frontera, will overtake Stampede 2 as the fast Read more…

By Tiffany Trader

Intel Announces Cooper Lake, Advances AI Strategy

August 9, 2018

Intel's chief datacenter exec Navin Shenoy kicked off the company's Data-Centric Innovation Summit Wednesday, the day-long program devoted to Intel's datacenter Read more…

By Tiffany Trader

GPUs Power Five of World’s Top Seven Supercomputers

June 25, 2018

The top 10 echelon of the newly minted Top500 list boasts three powerful new systems with one common engine: the Nvidia Volta V100 general-purpose graphics proc Read more…

By Tiffany Trader

The Machine Learning Hype Cycle and HPC

June 14, 2018

Like many other HPC professionals I’m following the hype cycle around machine learning/deep learning with interest. I subscribe to the view that we’re probably approaching the ‘peak of inflated expectation’ but not quite yet starting the descent into the ‘trough of disillusionment. This still raises the probability that... Read more…

By Dairsie Latimer

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This