HPC Power Efficiency and the Green500

By Kirk W. Cameron

November 20, 2013

The first Green500 List was launched in November 2007 ranking the energy efficiency of supercomputers. Co-founder Kirk W. Cameron discusses the events that led to creation of the Green500 List, its maturation, and future directions.

An Early Supercomputer Efficiency “List”

In 2001 the notions of Green HPC and energy proportional computing were unknown. There was no tangible evidence that power was an issue in supercomputers. Vendors simply built large systems to customer specifications. Performance kept increasing exponentially and while performance efficiency was of interest, power efficiency was not.

My early work in Green HPC was inspired by the tradeoffs inherent to power and performance. I imagined how varying power modes might make supercomputers more efficient. I speculated as to how such technologies would change the way we compute in HPC. But, in the beginning this seemed like a solution looking for a problem. No one at the time believed power was or ever would be an issue in HPC.

I needed data. And lots of it if I was to convince a community power was important. The Top500 List provided a plethora of performance data, but nothing related to power. Many of the larger supercomputer systems posted their specifications online, but the information was spotty at best and it became obvious quickly that no one was measuring power. If I wanted to improve power efficiency in supercomputers, not only would I have to prove conclusively that a power problem existed, I would have to start measuring systems myself! As a software guy, this was daunting.

cameron_fig

Figure 1. Source: NSF Career Proposal Submission, K.W. Cameron, July 2003.

It seems almost comical now, but I spent 4 months obtaining the data for Figure 1. This “list” of power consumption for the top supercomputers from 6 different Top500 lists over ten years was the first of its kind. Perhaps the most striking feature is the exponential increase in raw power consumption of the top systems from 1993 to 2003. Moreover, despite separation by a decade of technological advances, the efficiency of the TMC CM-5 (~12 MFLOPS/watt) was more than double that of the Japanese Earth simulator (5.6 MFLOPS/watt).

The trends are clear and irrefutable. Supercomputer power was a liability and would soon limit scalability. Of course, it would be almost 4 years before the community at-large began to acknowledge supercomputer power was a fundamental constraint. Let’s just say I’ve learned to be patient.

Origins of the Green500 List

Particularly in those early years, I spent a lot of time considering data collection and power measurement. My team built infrastructures and designed tools and methodologies to accurately track power usage in HPC systems. We ported our framework again and again to learn as much as we could about the tradeoffs between power and performance on emergent systems. We also built the first power-scalable HPC system prototype.

Wu Feng approached me in 2006 with the notion of creating a list of power efficient supercomputers akin to the Top500. I was already a firm believer in the need for such data having spent 4 months creating a small list of power consumption for 6 supercomputers. Furthermore, I had spent the last three years designing several generations of power measurement toolkits. My group arguably had compiled the largest, most detailed repository of HPC power data and we had a vast amount of experience measuring HPC system power.

My primary role was to design the power measurement run rules for the first list. We knew that other benchmarking methodologies had suffered when the system could be gamed easily. Based on my experience measuring power, we wrote a set of run rules describing how to easily measure a single node and extrapolate the power for a supercomputer running Linpack. The rules were designed to encourage participation by enabling non-experts to report their own power data with minimal investment in time and money. For those not reporting, we would use the UL ratings (see Figure 1) to fully populate the list.

Ease of participation was paramount. The Linpack benchmark was not ideal, but the only benchmark most supercomputer users reported regularly. MFLOPS per Watt was not an ideal metric, but it was easy to report and would encourage energy efficient, high-performance solutions.

After 6 months of discussion we solicited participation from the broader community. About a year later, in November 2007, we released the first list. The launch of the first Green500 List was an event. As if scripted, just prior to launch, the power problem in data centers had become front-page news and rather suddenly many agreed that supercomputers needed to become more energy efficient.

Some embraced the list and touted high-ranked systems while deriding low-ranked systems. Some complained of being disenfranchised. Some ridiculed our methodology and metrics. Some took issue with the lack of community involvement or coordination with other lists, benchmarks, and government agencies.

The Green500 List Matures

While most of the early dialogue and press affirmed the need for the Green500 List, some valid criticisms led to significant improvements. For example, we released an updated list in early 2008 to include measured numbers from those that did not report to the first list. In succeeding lists, we limited the amount of information we track to focus exclusively on energy efficiency. Later, we obtained research funding to explore the potential use of other benchmarks and metrics.

We’ve actively sought feedback from users as the list has matured. This has resulted in additional lists such as the Little Green500. While entry to the Green500 requires placing among the 500 fastest systems in the world, the Little Green500 broadens this definition to include systems as fast as the slowest supercomputer from the three previous Top500 lists. The goal of this list is to provide efficiency information to those that would deploy smaller systems.

While the Green500 was a bit isolated initially, it is now part of a thriving community of activists promoting energy efficiency. The Climate Savers Computing Initiative, The Green Grid, and the Energy Efficiency HPC Working Group are just a few of the proactive groups that ensure energy efficiency is now a first-class constraint in HPC design, procurement and management. For example, the Energy Efficiency HPC Working Group has been instrumental in identifying limitations in the Green500 measurement methodology. They have invested significant time and effort to isolate these limitations and suggest improvements to our methodologies that will likely be adopted in the future. They have also provided a conduit for opening discussions between the Department of Energy and vendors to establish standard practices for evaluating energy efficiency during the procurement process.

Legacy and Future of the Green500

The legacy of the Green500 is the establishment of a consistent, easy-to-follow set of power measurement run rules and the resulting data. Before the Green500 there was no widely accepted methodology for measuring supercomputer power, no way to track energy efficiency from year to year, and thus no way to encourage efficient design. The Green500 power measurement methodology has persisted nearly unchanged for almost 7 years laying the foundation for a standardized methodology for collecting supercomputer power data. The methodology can always be improved. For example, the Top500 has tweaked its run rules over the years to prevent gaming. However, the early establishment of a set of consistent, easy to follow run rules provided fairness and stability in the Green500 List’s critical infancy.

The stability of the run rules enables us to consistently analyze trends in efficiency data from year to year. These trends lead to a number of interesting observations.

I agree with Horst. Assuming its efficiency could be maintained, the TMC CM-5 system from 1993 would have landed in position #493 on the inaugural November 2007 Green500 List. This position is ahead of both the Earth Simulator (#497) and ASCI Q (#500). From 1993 to 2007 the MFLOPS/watt of the fastest systems went from 12 to 357. From 2007 to 2013 the MFLOPS/watt of the fastest systems went from 357 to 3208.

An exascale system in 20 MW will require 50,000 MFLOPS/watt. If efficiency trends continue as they did from 1993 to 2007, a 20MW exascale system is achievable in about 22 years (2035). The last 6 years saw tremendous efficiency improvements using accelerators. Assuming another efficiency boost from new technology equivalent to the gain from accelerators, an exascale system is achievable in 20 MW in about 9 years (2022). Most likely, we will see moderate gains placing us at exascale in 20 MW by about 2025. This is well beyond the goal of exascale by 2020 in 20MW.

The shell game. While the Green500 gives us loads of information we never had before, there is little information about the power budget of the components of a system. While knowing total power is helpful, knowing how the power is spent across the system is critical to acquisition decisions. Is the majority of the power budget used on the GPUs, the memory, the CPUs, the disks, the network? Most systems in the Green500 are designed from commodity parts assembled at scale. If we truly want to promote efficiency and enable people to make informed design decisions, we need more insight to the details of where power is spent in these larger systems. Is a system with lots of disk arrays more or less of a power hog than a system with lots of GPUs? I really have no idea. And I’ve been studying power for more than a decade.

Will HPC ever embrace power management? The benefit of power management is clear. Save energy. Work abounds showing energy savings can be achieved with little to no performance loss. Nonetheless, most supercomputers disable all power management. On the flip side, power management technologies such as Intel Turbo boost can increase performance maximally within thermal limits. In fact, the SuperMuc supercomputer in Munich, Germany was chastised by some in the community for enabling Turbo boost during their early benchmarking and thus potentially skewing their Linpack results.

Trying to adapt benchmarking methodologies to mitigate against gaming is welcome. Trying to adapt benchmarking methodologies to neutralize the effects of technologies that improve efficiency is counterproductive and I believe ultimately futile. Systems are gaining in complexity every day. They are larger, have more parts and parallelism, and more autonomy in every generation. Processors throttle themselves, and memories and GPUs will soon do the same. Power and performance will not be fixed between two successive runs in these types of dynamic, complex systems. We must develop evaluation methodologies that embrace complexity and non-determinism since they will eventually transcend our ability to adapt. Furthermore, in the long run, the complexity and non-determinism we are attempting to ignore will be essential to maximize performance. Only when we accept complexity and non-determinism as constants can we adopt power management in production systems.

The Future. Accelerators are here to say, but most computational scientists I know refuse to use them. I’m not sure which group will blink first, the hardware designers or the users. Perhaps the middleware folks will come to the rescue and make accelerators more programmable. In any case, I think we’ll see accelerators dominate the Green500 List until they are replaced by a new technology or abandoned by all.

In every talk I’ve seen by Intel and NVidia, the consensus seems to be we are still really in the first generation of accelerators with several significantly advanced generations to come. These next generations are faster, have more parallelism, more on-board memory, more power management, and are more tightly integrated with the board. This means above all more complexity. These systems will be even harder to program and evaluate. They will likely show modest efficiency gains in the Green500, but they will not match the percentage gains from the first generation placing exascale beyond the 2020 goal.

W

While we co-founders have provided a consistent vision, biannual installments of the Green500 List are the work of an army of dedicated students, researchers, and passionate crusaders for energy efficiency. Without selfless adoption by a much broader community, the Green500 List would have been a fleeting anecdote.

It’s been more than twelve years since I started down the Green HPC path. I honestly thought after four to five years we would have exhausted all the interesting problems in HPC efficiency. The Green500 List’s impact has greatly exceeded my expectations. The introduction of a stable and fair methodology to track efficiency has withstood nearly 7 years of scrutiny and highlighted the insatiable need for ongoing research. What I failed to appreciate in the beginning was that power efficiency as a problem would transform and perpetuate with every new generation of supercomputer. Like the challenges of performance, reliability, and security, power efficiency is here to stay.

About the Author

Kirk W. Cameron is a Professor of Computer Science and a Faculty Fellow in the College of Engineering at Virginia Tech. Prof. Cameron is a pioneer and leading expert in Green Computing. Cameron is the Green IT columnist for IEEE Computer, Green500 co-founder, founding member of SPECPower, EPA consultant, Uptime Institute Fellow, and co-founder of power management software startup company MiserWare. His power measurement and management software tools are used by nearly half a million people in more than 160 countries. Accolades for his work include NSF and DOE Career Awards, the IBM Faculty Award, and being named Innovator of the Week by Bloomberg Businessweek Magazine. Prof. Cameron received the Ph.D. in Computer Science from Louisiana State University (2000) and B.S. in Mathematics from the University of Florida (1994).

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!

Nvidia Shuts Out RISC-V Software Support for GPUs 

September 23, 2022

Nvidia is not interested in bringing software support to its GPUs for the RISC-V architecture despite being an early adopter of the open-source technology in its GPU controllers. Nvidia has no plans to add RISC-V support for CUDA, which is the proprietary GPU software platform, a company representative... Read more…

Microsoft Closes Confidential Computing Loop with AMD’s Milan Chip

September 22, 2022

Microsoft shared details on how it uses an AMD technology to secure artificial intelligence as it builds out a secure AI infrastructure in its Azure cloud service. Microsoft has a strong relationship with Nvidia, but is also working with AMD's Epyc chips (including the new 3D VCache series), MI Instinct accelerators, and also... Read more…

Nvidia Introduces New Ada Lovelace GPU Architecture, OVX Systems, Omniverse Cloud

September 20, 2022

In his GTC keynote today, Nvidia CEO Jensen Huang launched another new Nvidia GPU architecture: Ada Lovelace, named for the legendary mathematician regarded as the first computer programmer. The company also announced tw Read more…

Nvidia’s Hopper GPUs Enter ‘Full Production,’ DGXs Delayed Until Q1

September 20, 2022

Just about six months ago, Nvidia’s spring GTC event saw the announcement of its hotly anticipated Hopper GPU architecture. Now, the GPU giant is announcing that Hopper-generation GPUs (which promise greater energy eff Read more…

NeMo LLM Service: Nvidia’s First Cloud Service Makes AI Less Vague

September 20, 2022

Nvidia is trying to uncomplicate AI with a cloud service that makes AI and its many forms of computing less vague and more conversational. The NeMo LLM service, which Nvidia called its first cloud service, adds a layer of intelligence and interactivity... Read more…

AWS Solution Channel

Shutterstock 1194728515

Simulating 44-Qubit quantum circuits using AWS ParallelCluster

Dr. Fabio Baruffa, Sr. HPC & QC Solutions Architect
Dr. Pavel Lougovski, Pr. QC Research Scientist
Tyson Jones, Doctoral researcher, University of Oxford

Introduction

Currently, an enormous effort is underway to develop quantum computing hardware capable of scaling to hundreds, thousands, and even millions of physical (non-error-corrected) qubits. Read more…

Microsoft/NVIDIA Solution Channel

Shutterstock 1166887495

Improving Insurance Fraud Detection using AI Running on Cloud-based GPU-Accelerated Systems

Insurance is a highly regulated industry that is evolving as the industry faces changing customer expectations, massive amounts of data, and increased regulations. A major issue facing the industry is tracking insurance fraud. Read more…

Nvidia Targets Computers for Robots in the Surgery Rooms

September 20, 2022

Nvidia is laying the groundwork for a future in which humans and robots will be collaborators in the surgery rooms at hospitals. The company announced a computer called IGX for Medical Devices, which will be populated in robots, image scanners and other computers and medical devices involved in patient care close to the point... Read more…

Nvidia Shuts Out RISC-V Software Support for GPUs 

September 23, 2022

Nvidia is not interested in bringing software support to its GPUs for the RISC-V architecture despite being an early adopter of the open-source technology in its GPU controllers. Nvidia has no plans to add RISC-V support for CUDA, which is the proprietary GPU software platform, a company representative... Read more…

Nvidia Introduces New Ada Lovelace GPU Architecture, OVX Systems, Omniverse Cloud

September 20, 2022

In his GTC keynote today, Nvidia CEO Jensen Huang launched another new Nvidia GPU architecture: Ada Lovelace, named for the legendary mathematician regarded as Read more…

Nvidia’s Hopper GPUs Enter ‘Full Production,’ DGXs Delayed Until Q1

September 20, 2022

Just about six months ago, Nvidia’s spring GTC event saw the announcement of its hotly anticipated Hopper GPU architecture. Now, the GPU giant is announcing t Read more…

NeMo LLM Service: Nvidia’s First Cloud Service Makes AI Less Vague

September 20, 2022

Nvidia is trying to uncomplicate AI with a cloud service that makes AI and its many forms of computing less vague and more conversational. The NeMo LLM service, which Nvidia called its first cloud service, adds a layer of intelligence and interactivity... Read more…

Nvidia Targets Computers for Robots in the Surgery Rooms

September 20, 2022

Nvidia is laying the groundwork for a future in which humans and robots will be collaborators in the surgery rooms at hospitals. The company announced a computer called IGX for Medical Devices, which will be populated in robots, image scanners and other computers and medical devices involved in patient care close to the point... Read more…

Survey Results: PsiQuantum, ORNL, and D-Wave Tackle Benchmarking, Networking, and More

September 19, 2022

The are many issues in quantum computing today – among the more pressing are benchmarking, networking and development of hybrid classical-quantum approaches. Read more…

HPC + AI Wall Street to Feature ‘Spooky’ Science for Financial Services

September 18, 2022

Albert Einstein famously described quantum mechanics as "spooky action at a distance" due to the non-intuitive nature of superposition and quantum entangled par Read more…

Analog Chips Find a New Lease of Life in Artificial Intelligence

September 17, 2022

The need for speed is a hot topic among participants at this week’s AI Hardware Summit – larger AI language models, faster chips and more bandwidth for AI machines to make accurate predictions. But some hardware startups are taking a throwback approach for AI computing to counter the more-is-better... Read more…

AWS Takes the Short and Long View of Quantum Computing

August 30, 2022

It is perhaps not surprising that the big cloud providers – a poor term really – have jumped into quantum computing. Amazon, Microsoft Azure, Google, and th Read more…

The Final Frontier: US Has Its First Exascale Supercomputer

May 30, 2022

In April 2018, the U.S. Department of Energy announced plans to procure a trio of exascale supercomputers at a total cost of up to $1.8 billion dollars. Over the ensuing four years, many announcements were made, many deadlines were missed, and a pandemic threw the world into disarray. Now, at long last, HPE and Oak Ridge National Laboratory (ORNL) have announced that the first of those... Read more…

US Senate Passes CHIPS Act Temperature Check, but Challenges Linger

July 19, 2022

The U.S. Senate on Tuesday passed a major hurdle that will open up close to $52 billion in grants for the semiconductor industry to boost manufacturing, supply chain and research and development. U.S. senators voted 64-34 in favor of advancing the CHIPS Act, which sets the stage for the final consideration... Read more…

Top500: Exascale Is Officially Here with Debut of Frontier

May 30, 2022

The 59th installment of the Top500 list, issued today from ISC 2022 in Hamburg, Germany, officially marks a new era in supercomputing with the debut of the first-ever exascale system on the list. Frontier, deployed at the Department of Energy’s Oak Ridge National Laboratory, achieved 1.102 exaflops in its fastest High Performance Linpack run, which was completed... Read more…

Chinese Startup Biren Details BR100 GPU

August 22, 2022

Amid the high-performance GPU turf tussle between AMD and Nvidia (and soon, Intel), a new, China-based player is emerging: Biren Technology, founded in 2019 and headquartered in Shanghai. At Hot Chips 34, Biren co-founder and president Lingjie Xu and Biren CTO Mike Hong took the (virtual) stage to detail the company’s inaugural product: the Biren BR100 general-purpose GPU (GPGPU). “It is my honor to present... Read more…

Newly-Observed Higgs Mode Holds Promise in Quantum Computing

June 8, 2022

The first-ever appearance of a previously undetectable quantum excitation known as the axial Higgs mode – exciting in its own right – also holds promise for developing and manipulating higher temperature quantum materials... Read more…

AMD’s MI300 APUs to Power Exascale El Capitan Supercomputer

June 21, 2022

Additional details of the architecture of the exascale El Capitan supercomputer were disclosed today by Lawrence Livermore National Laboratory’s (LLNL) Terri Read more…

Tesla Bulks Up Its GPU-Powered AI Super – Is Dojo Next?

August 16, 2022

Tesla has revealed that its biggest in-house AI supercomputer – which we wrote about last year – now has a total of 7,360 A100 GPUs, a nearly 28 percent uplift from its previous total of 5,760 GPUs. That’s enough GPU oomph for a top seven spot on the Top500, although the tech company best known for its electric vehicles has not publicly benchmarked the system. If it had, it would... Read more…

Leading Solution Providers

Contributors

Exclusive Inside Look at First US Exascale Supercomputer

July 1, 2022

HPCwire takes you inside the Frontier datacenter at DOE's Oak Ridge National Laboratory (ORNL) in Oak Ridge, Tenn., for an interview with Frontier Project Direc Read more…

AMD Opens Up Chip Design to the Outside for Custom Future

June 15, 2022

AMD is getting personal with chips as it sets sail to make products more to the liking of its customers. The chipmaker detailed a modular chip future in which customers can mix and match non-AMD processors in a custom chip package. "We are focused on making it easier to implement chips with more flexibility," said Mark Papermaster, chief technology officer at AMD during the analyst day meeting late last week. Read more…

Intel Reiterates Plans to Merge CPU, GPU High-performance Chip Roadmaps

May 31, 2022

Intel reiterated it is well on its way to merging its roadmap of high-performance CPUs and GPUs as it shifts over to newer manufacturing processes and packaging technologies in the coming years. The company is merging the CPU and GPU lineups into a chip (codenamed Falcon Shores) which Intel has dubbed an XPU. Falcon Shores... Read more…

Nvidia, Intel to Power Atos-Built MareNostrum 5 Supercomputer

June 16, 2022

The long-troubled, hotly anticipated MareNostrum 5 supercomputer finally has a vendor: Atos, which will be supplying a system that includes both Nvidia and Inte Read more…

UCIe Consortium Incorporates, Nvidia and Alibaba Round Out Board

August 2, 2022

The Universal Chiplet Interconnect Express (UCIe) consortium is moving ahead with its effort to standardize a universal interconnect at the package level. The c Read more…

Using Exascale Supercomputers to Make Clean Fusion Energy Possible

September 2, 2022

Fusion, the nuclear reaction that powers the Sun and the stars, has incredible potential as a source of safe, carbon-free and essentially limitless energy. But Read more…

Is Time Running Out for Compromise on America COMPETES/USICA Act?

June 22, 2022

You may recall that efforts proposed in 2020 to remake the National Science Foundation (Endless Frontier Act) have since expanded and morphed into two gigantic bills, the America COMPETES Act in the U.S. House of Representatives and the U.S. Innovation and Competition Act in the U.S. Senate. So far, efforts to reconcile the two pieces of legislation have snagged and recent reports... Read more…

India Launches Petascale ‘PARAM Ganga’ Supercomputer

March 8, 2022

Just a couple of weeks ago, the Indian government promised that it had five HPC systems in the final stages of installation and would launch nine new supercomputers this year. Now, it appears to be making good on that promise: the country’s National Supercomputing Mission (NSM) has announced the deployment of “PARAM Ganga” petascale supercomputer at Indian Institute of Technology (IIT)... Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire