Performance per watt

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In computing, performance per watt is a measure of the energy efficiency of a particular computer architecture or computer hardware. Literally, it measures the rate of computation that can be delivered by a computer for every watt of power consumed. This rate is typically measured by performance on the LINPACK benchmark when trying to compare between computing systems.

System designers building parallel computers, such as Google's hardware, pick CPUs based on their performance per watt of power, because the cost of powering the CPU outweighs the cost of the CPU itself.[1]


The performance and power consumption metrics used depend on the definition; reasonable measures of performance are FLOPS, MIPS, or the score for any performance benchmark. Several measures of power usage may be employed, depending on the purposes of the metric; for example, a metric might only consider the electrical power delivered to a machine directly, while another might include all power necessary to run a computer, such as cooling and monitoring systems. The power measurement is often the average power used while running the benchmark, but other measures of power usage may be employed (e.g. peak power, idle power).

For example, the early UNIVAC I computer performed approximately 0.015 operations per watt-second (performing 1,905 operations per second (OPS), while consuming 125 kW). The Fujitsu FR-V VLIW/vector processor system on a chip in the 4 FR550 core variant released 2005 performs 51 Giga-OPS with 3 watts of power consumption resulting in 17 billion operations per watt-second.[2][3] This is an improvement by over a trillion times in 54 years.

Most of the power a computer uses is converted into heat, so a system that takes fewer watts to do a job will require less cooling to maintain a given operating temperature. Reduced cooling demands makes it easier to quiet a computer. Lower energy consumption can also make it less costly to run, and reduce the environmental impact of powering the computer (see green computing). If installed where there is limited climate control, a lower power computer will operate at a lower temperature, which may make it more reliable. In a climate controlled environment, reductions in direct power use may also create savings in climate control energy.

Computing energy consumption is sometimes also measured by reporting the energy required to run a particular benchmark, for instance EEMBC EnergyBench. Energy consumption figures for a standard workload may make it easier to judge the effect of an improvement in energy efficiency.

Performance (in operations/second) per watt can also be written as operations/watt-second, or operations/joule, since 1 watt = 1 joule/second.

FLOPS per watt[edit]

Exponential growth of supercomputer performance per watt based on data from the Green500 list. The red crosses denote the most power efficient computer, while the blue ones denote the computer ranked#500.

FLOPS per watt is a common measure. Like the FLOPS (Floating Point Operations Per Second) metric it is based on, the metric is usually applied to scientific computing and simulations involving many floating point calculations.


As of June 2016, the Green500 list rates the two most efficient supercomputers highest – those are both based on the same manycore accelerator PEZY-SCnp Japanese technology in addition to Intel Xeon processors – both at RIKEN, the top one at 6673.8 MFLOPS/watt; and the third ranked is the Chinese-technology Sunway TaihuLight (a much bigger machine, that is the ranked 2nd on TOP500, the others are not on that list) at 6051.3 MFLOPS/watt.[4]

In June 2012, the Green500 list rated BlueGene/Q, Power BQC 16C as the most efficient supercomputer on the TOP500 in terms of FLOPS per watt, running at 2,100.88 MFLOPS/watt.[5]

In November 2010, IBM machine, Blue Gene/Q achieves 1,684 MFLOPS/watt.[6][7]

On 9 June 2008, CNN reported that IBM's Roadrunner supercomputer achieves 376 MFLOPS/watt.[8][9]

As part of Intel's Tera-Scale research project, the team produced an 80-core CPU that can achieve over 16,000 MFLOPS/watt.[10][11] The future of that CPU is not certain.

Microwulf, a low cost desktop Beowulf cluster of four dual-core Athlon 64 X2 3800+ computers, runs at 58 MFLOPS/watt.[12]

Kalray has developed a 256-core VLIW CPU that achieves 25,000 MFLOPS/watt. Next generation is expected to achieve 75,000 MFLOPS/watt.[13] However, in 2019 their latest chip for embedded is 80-core and claims up to 4 TFLOPS at 20 W.[14]

Adapteva announced the Epiphany V, a 1024-core 64-bit RISC processor intended to achieve 75 GFLOPS/watt,[15][16] while they later announced that the Epiphany V was "unlikely" to become available as a commercial product

US Patent 10,020,436, July 2018 claims three intervals of 100, 300, and 600 GFLOPS/watt.

Green500 List[edit]

The Green500 list ranks computers from the TOP500 list of supercomputers in terms of energy efficiency, typically measured as LINPACK FLOPS per watt.[17][18]

As of November 2012, an Appro International, Inc. Xtreme-X supercomputer (Beacon) topped the Green500 list with 2499 LINPACK MFLOPS/W.[19] Beacon is deployed by NICS of the University of Tennessee and is a GreenBlade GB824M, Xeon E5-2670 based, eight cores (8C), 2.6 GHz, Infiniband FDR, Intel Xeon Phi 5110P computer.[20]

As of June 2013, the Eurotech supercomputer Eurora at Cineca topped the Green500 list with 3208 LINPACK MFLOPS/W.[21] The Cineca Eurora supercomputer is equipped with two Intel Xeon E5-2687W CPUs and two PCI-e connected NVIDIA Tesla K20 accelerators per node. Water cooling and electronics design allows for very high densities to be reached with a peak performance of 350 TFLOPS per rack.[22]

As of November 2014, the L-CSC supercomputer of the Helmholtz Association at the GSI in Darmstadt Germany topped the Green500 list with 5271 MFLOPS/W and was the first cluster to surpass an efficiency of 5 GFLOPS/W. It runs on Intel Xeon E5-2690 Processors with the Intel Ivy Bridge Architecture and AMD FirePro S9150 GPU Accelerators. It uses in rack watercooling and Cooling Towers to reduce the energy required for cooling.[23]

As of August 2015, the Shoubu supercomputer of RIKEN outside Tokyo Japan topped the Green500 list with 7032 MFLOPS/W. The then-top three supercomputers of the list used PEZY-SC accelerators (GPU-like that use OpenCL)[24] by PEZY Computing with 1024 cores each and 6–7 GFLOPS/W efficiency.[25][26]

As of June 2019, DGX SaturnV Volta, using "NVIDIA DGX-1 Volta36, Xeon E5-2698v4 20C 2.2GHz, Infiniband EDR, NVIDIA Tesla V100", tops Green500 list with 15,113 MFLOPS/W, while ranked only 469th on Top500.[27] It's only a little bit more efficient than the much bigger Summit ranked 2nd while 1st on Top500 with 14,719 MFLOPS/W, using IBM POWER9 CPUs while also with Nvidia Tesla V100 GPUs.

Top 10 positions of GREEN500 in November 2019[28]
Rank Performance
per Watt
Name Model
Processors, Interconnect
Vendor Site
Country, year
1 16.876 A64FX prototype Fujitsu A64FX
Fujitsu A64FX 48C 2GHz, Tofu interconnect D
Fujitsu Numazu
  Japan, 2018
2 16.256 NA-1 ZettaScaler-2.2
Xeon D-1571 16C 1.3GHz, Infiniband EDR, PEZY-SC2 700Mhz
PEZY Computing K.K. JAMSTEC Yokohama Institute for Earth Sciences, Yokohama
  Japan, 2019
3 15.771 AiMOS IBM Power System AC922
IBM POWER9 20C 3.45GHz, Dual-rail Mellanox EDR Infiniband, NVIDIA Volta GV100
IBM Rensselaer Polytechnic Institute, Troy,
 United States, 2018
4 15.574 Satori IBM Power System AC922
IBM POWER9 20C 2.4GHz, Infiniband EDR, NVIDIA Tesla V100 SXM2
IBM MIT/MGHPCC, Holyoke, Massachusetts,
 United States,2018
5 14.719 Summit IBM Power System AC922
IBM POWER9 22C 3.07GHz, NVIDIA Volta GV100, Dual-rail Mellanox EDR Infiniband
IBM Oak Ridge National Laboratory, Oak Ridge, Tennessee
  United States, 2018
6 14.423 AI Bridging Cloud Infrastructure (ABCI) Primergy CX2570 M4
Xeon Gold, Tesla V100 SXM2,Infiniband EDR
Fujitsu Joint Center for Advanced High Performance Computing, Kashiwa
  Japan, 2018
7 14.131 MareNostrum P9 CTE IBM Power System AC922
IBM POWER9 22C 3.1GHz, Dual-rail Mellanox EDR Infiniband, NVIDIA Tesla V100
IBM Barcelona Supercomputing Center, Barcelona,
8 13.704 TSUBAME3.0 SGI ICE XA
IP139-SXM2, Xeon E5-2680v4 14C 2.4GHz, Intel Omni-Path, NVIDIA Tesla P100 SXM2
Hewlett-Packard Tokyo Institute of Technology, Tokyo,
9 13.065 PANGEA III IBM Power System AC922
A III - IBM Power System AC922, IBM POWER9 18C 3.45GHz, Dual-rail Mellanox EDR Infiniband, NVIDIA Volta GV100
IBM Total S.A., Pau,
10 12.723 Sierra IBM Power System AC922
- IBM Power System AC922, IBM POWER9 22C 3.1GHz, NVIDIA Volta GV100, Dual-rail Mellanox EDR Infiniband
IBM Lawrence Livermore National Laboratory, Livermore,
  United States, 2018

GPU efficiency[edit]

Graphics processing units (GPU) have continued to increase in energy usage, while CPUs designers have recently focused on improving performance per watt. High performance GPUs may draw large amount of power and hence, intelligent techniques are required to manage GPU power consumption.[29] Measures like 3DMark2006 score per watt can help identify more efficient GPUs.[30] However that may not adequately incorporate efficiency in typical use, where much time is spent doing less demanding tasks.[31]

With modern GPUs, energy usage is an important constraint on the maximum computational capabilities that can be achieved. GPU designs are usually highly scalable, allowing the manufacturer to put multiple chips on the same video card, or to use multiple video cards that work in parallel. Peak performance of any system is essentially limited by the amount of power it can draw and the amount of heat it can dissipate. Consequently, performance per watt of a GPU design translates directly into peak performance of a system that uses that design.

Since GPUs may also be used for some general purpose computation, sometimes their performance is measured in terms also applied to CPUs, such as FLOPS per watt.


While performance per watt is useful, absolute power requirements are also important. Claims of improved performance per watt may be used to mask increasing power demands. For instance, though newer generation GPU architectures may provide better performance per watt, continued performance increases can negate the gains in efficiency, and the GPUs continue to consume large amounts of power.[32]

Benchmarks that measure power under heavy load may not adequately reflect typical efficiency. For instance, 3DMark stresses the 3D performance of a GPU, but many computers spend most of their time doing less intense display tasks (idle, 2D tasks, displaying video). So the 2D or idle efficiency of the graphics system may be at least as significant for overall energy efficiency. Likewise, systems that spend much of their time in standby or soft off are not adequately characterized by just efficiency under load. To help address this some benchmarks, like SPECpower, include measurements at a series of load levels.[33]

The efficiency of some electrical components, such as voltage regulators, decreases with increasing temperature, so the power used may increase with temperature. Power supplies, motherboards, and some video cards are some of the subsystems affected by this. So their power draw may depend on temperature, and the temperature or temperature dependence should be noted when measuring.[34][35]

Performance per watt also typically does not include full life-cycle costs. Since computer manufacturing is energy intensive, and computers often have a relatively short lifespan, energy and materials involved in production, distribution, disposal and recycling often make up significant portions of their cost, energy use, and environmental impact.[36][37]

Energy required for climate control of the computer's surroundings is often not counted in the wattage calculation, but it can be significant.[38]

Other energy efficiency measures[edit]

SWaP (space, wattage and performance) is a Sun Microsystems metric for data centers, incorporating energy and space:

Where performance is measured by any appropriate benchmark, and space is size of the computer.[39]

See also[edit]

Energy efficiency benchmarks
  • Average CPU power (ACP) – a measure of power consumption when running several standard benchmarks
  • EEMBC – EnergyBench
  • SPECpower – a benchmark for web servers running Java (Server Side Java Operations per Joule)

Notes and references[edit]

  1. ^ Power could cost more than servers, Google warns, CNET, 2006
  2. ^ "Fujitsu Develops Multi-core Processor for High-Performance Digital Consumer Products" (Press release). Fujitsu. 2020-02-07. Archived from the original on 2019-03-25. Retrieved 2020-08-08.
  3. ^ FR-V Single-Chip Multicore Processor:FR1000 Archived 2015-04-02 at the Wayback Machine Fujitsu
  4. ^ "Green500 List for June 2016".
  5. ^ "The Green500 List". Green500. Archived from the original on 2012-07-03.
  6. ^ "Top500 Supercomputing List Reveals Computing Trends". IBM... BlueGene/Q system .. setting a record in power efficiency with a value of 1,680 Mflops/watt, more than twice that of the next best system.
  7. ^ "IBM Research A Clear Winner in Green 500". 2010-11-18.
  8. ^ "Government unveils world's fastest computer". CNN. Archived from the original on 2008-06-10. performing 376 million calculations for every watt of electricity used.
  9. ^ "IBM Roadrunner Takes the Gold in the Petaflop Race". Archived from the original on 2008-06-13.
  10. ^ "Intel squeezes 1.8 TFlops out of one processor". TG Daily. Archived from the original on 2007-12-03.
  11. ^ "Teraflops Research Chip". Intel Technology and Research.
  12. ^ Joel Adams. "Microwulf: Power Efficiency". Microwulf: A Personal, Portable Beowulf Cluster.
  13. ^ "MPPA MANYCORE - Many-core processors - KALRAY - Agile Performance".
  14. ^ "Kalray announces the Tape-Out of Coolidge on TSMC 16NM process technology". Kalray. 2019-07-31. Retrieved 2019-08-12.
  15. ^ Olofsson, Andreas. "Epiphany-V: A 1024-core 64-bit RISC processor". Retrieved 6 October 2016.
  16. ^ Olofsson, Andreas. "Epiphany-V: A 1024 processor 64-bit RISC System-On-Chip" (PDF). Retrieved 6 October 2016.
  17. ^ "The Green 500".
  18. ^ "Green 500 list ranks supercomputers". iTnews Australia. Archived from the original on 2008-10-22.
  19. ^ "University of Tennessee Supercomputer Sets World Record for Energy Efficiency". National Institute for Computational Sciences News. University of Tennessee & Oak Ridge National Laboratory. Retrieved 21 November 2012.
  20. ^ "Beacon - Appro GreenBlade - Green500 list". Retrieved 21 November 2012.
  21. ^ "Eurotech Eurora, the PRACE prototype deployed by Cineca and INFN, scores first in Green500 list". Cineca. Cineca. Retrieved 28 June 2013.
  22. ^ "Eurora - Aurora Tigon - Top500 list". Retrieved 28 June 2013.
  23. ^ "The Green500 List - November 2014". Archived from the original on 2015-02-22.
  24. ^ Hindriksen, Vincent (2015-08-02). "The knowns and unknowns of the PEZY-SC accelerator at RIKEN". StreamHPC. Retrieved 2019-10-21.
  25. ^ Tiffany, Tiffany (August 4, 2015). "Japan Takes Top Three Spots on Green500 List". HPCWire. Retrieved 8 January 2016.
  26. ^ "PEZY & ExaScaler Step Up on the Green500 List with Immersive Cooling". InsideHPC. September 23, 2015. Retrieved 8 January 2016.
  27. ^ "June 2019 | TOP500 Supercomputer Sites". Retrieved 2019-08-12.
  28. ^ "November 2019". Retrieved 2019-12-13.
  29. ^ Mittal, Sparsh; Vetter, Jeffrey S. (July 2014). "A Survey of Methods for Analyzing and Improving GPU Energy Efficiency". ACM Computing Surveys. Association for Computing Machinery (published January 2015). 47 (2). doi:10.1145/2636342. ISSN 0360-0300. Retrieved 2020-08-08.
  30. ^ Atwood, Jeff (2006-08-18). "Video Card Power Consumption".
  31. ^ "Video card power consumption". Xbit Labs. Archived from the original on 2011-09-04.
  32. ^ Tim Smalley. "Performance per What?". Bit Tech. Retrieved 2008-04-21.
  33. ^ "SPEC launches standardized energy efficiency benchmark". ZDNet.
  34. ^ Mike Chin. "Asus EN9600GT Silent Edition Graphics Card". Silent PC Review. p. 5. Retrieved 2008-04-21.
  35. ^ MIke Chin (19 March 2008). "80 Plus expands podium for Bronze, Silver & Gold". Silent PC Review. Retrieved 2008-04-21.
  36. ^ Mike Chin. "Life Cycle Analysis and Eco PC Review". Eco PC Review. Archived from the original on 2008-03-04.
  37. ^ Eric Williams (2004). "Energy intensity of computer manufacturing: hybrid assessment combining process and economic input-output methods". Environ. Sci. Technol. 38 (22): 6166–74. Bibcode:2004EnST...38.6166W. doi:10.1021/es035152j. PMID 15573621.
  38. ^ Wu-chun Feng (2005). "The Importance of Being Low Power in High Performance Computing". CT Watch Quarterly. 1 (5).
  39. ^ Greenhill, David. "SWaP Space Watts and Power" (PDF). US EPA Energystar. Retrieved 14 November 2013.

Further reading[edit]

External links[edit]