Computer performance by orders of magnitude

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This list compares various amounts of computing power in instructions per second organized by order of magnitude in FLOPS.

Scientific E notation index: 2 | 3 | 6 | 9 | 12 | 15 | 18 | 21 | 24 | >24

Deciscale computing (10−1)[edit]

  • 5×10−1 Speed of the average human mental calculation for multiplication using pen and paper

Scale computing (100)[edit]

  • 1 OP/S the speed of the average human addition calculation using pen and paper
  • 1 OP/S the speed of Zuse Z1
  • 5 OP/S world record for addition set

Decascale computing (101)[edit]

  • 5×101 Upper end of serialized human perception computation (light bulbs do not flicker to the human observer)

Hectoscale computing (102)[edit]

  • 2.2×102 Upper end of serialized human throughput. This is roughly expressed by the lower limit of accurate event placement on small scales of time (The swing of a conductor's arm, the reaction time to lights on a drag strip, etc.)[1]
  • 2×102 IBM 602 1946 computer.

Kiloscale computing (103)[edit]

Megascale computing (106)[edit]

Gigascale computing (109)[edit]

Terascale computing (1012)[edit]

Petascale computing (1015)[edit]

  • 1.026×1015 IBM Roadrunner 2009 Supercomputer
  • 2×1015 Nvidia DGX-2 a 2 Petaflop Machine Learning system (the newer DGX A100 has 5 Petaflop performance)
  • 11.5×1015 Google TPU pod containing 64 second-generation TPUs, May 2017[6]
  • 17.17×1015 IBM Sequoia's LINPACK performance, June 2013[7]
  • 20×1015 Roughly the hardware-equivalent of the human brain according to Kurzweil. Published in his 1999 book: The Age of Spiritual Machines: When Computers Exceed Human Intelligence[8]
  • 33.86×1015 Tianhe-2's LINPACK performance, June 2013[7]
  • 36.8×1015 Estimated computational power required to simulate a human brain in real time.[9]
  • 93.01×1015 Sunway TaihuLight's LINPACK performance, June 2016[10]
  • 143.5×1015 Summit's LINPACK performance, November 2018[11]

Exascale computing (1018)[edit]

  • 1×1018 The U.S. Department of Energy and NSA estimated in 2008 that they would need exascale computing around 2018[12]
  • 1×1018 Fugaku 2020 supercomputer in single precision mode[13]
  • 1.1x1018 Frontier 2022 supercomputer
  • 1.88×1018 U.S. Summit achieves a peak throughput of this many operations per second, whilst analysing genomic data using a mixture of numerical precisions.[14]
  • 2.43×1018 Folding@home distributed computing system during COVID-19 pandemic response[15]

Zettascale computing (1021)[edit]

  • 1×1021 Accurate global weather estimation on the scale of approximately 2 weeks.[16] Assuming Moore's law remains constant, such systems may be feasible around 2035.[17]

A zettascale computer system could generate more single floating point data in one second than was stored by any digital means on Earth in the first quarter of 2011.

Beyond zettascale computing (>1021)[edit]

  • 1.12×1036 Estimated computational power of a Matrioshka brain, assuming 1.87×1026 Watt power produced by solar panels and 6 GFLOPS/Watt efficiency.[18]
  • 4×1048 Estimated computational power of a Matrioshka brain, where the power source is the Sun, the outermost layer operates at 10 kelvins, and the constituent parts operate at or near the Landauer limit and draws power at the efficiency of a Carnot engine. Approximate maximum computational power for a Kardashev 2 civilization.[citation needed]
  • 5×1058 Estimated power of a galaxy equivalent in luminosity to the Milky Way converted into Matrioshka brains. Approximate maximum computational power for a Type III civilization on the Kardashev scale.

See also[edit]


  1. ^ "How many frames per second can the human eye see?". 2004-05-19. Retrieved 2013-02-19.
  2. ^ Overclock3D - Sandra CPU
  3. ^ Tony Pearson, IBM Watson - How to build your own "Watson Jr." in your basement, Inside System Storage
  4. ^ "DGX-1 deep learning system" (PDF). NVIDIA DGX-1 Delivers 75X Faster Training...Note: Caffe benchmark with AlexNet, training 1.28M images with 90 epochs
  5. ^ "DGX Server". DGX Server. Nvidia. Retrieved 7 September 2017.
  6. ^ "Build and train machine learning models on our new Google Cloud TPUs". 17 May 2017.
  7. ^ a b "Top500 List - June 2013 | TOP500 Supercomputer Sites". Archived from the original on 2013-06-22.
  8. ^ Kurzweil, Ray (1999). The Age of Spiritual Machines: When Computers Exceed Human Intelligence. New York, NY: Penguin. ISBN 9780140282023.
  9. ^ "Brain on a Chip". 30 November 2001.
  10. ^ Top500 list, June 2016
  11. ^ "November 2018 | TOP500 Supercomputer Sites". Retrieved 2018-11-30.
  12. ^ "'Exaflop' Supercomputer Planning Begins". 2008-02-02. Archived from the original on 2008-10-01. Retrieved 2010-01-04. Through the IAA, scientists plan to conduct the basic research required to create a computer capable of performing a million trillion calculations per second, otherwise known as an exaflop.
  13. ^ "June 2020 | TOP500".
  14. ^ "Genomics Code Exceeds Exaops on Summit Supercomputer". Oak Ridge Leadership Computing Facility. Retrieved 2018-11-30.
  15. ^ Pande lab. "Client Statistics by OS". Archived from the original on 2020-04-12. Retrieved 2020-04-12.
  16. ^ DeBenedictis, Erik P. (2005). "Reversible logic for supercomputing". Proceedings of the 2nd conference on Computing frontiers. pp. 391–402. ISBN 1-59593-019-1.
  17. ^ "Zettascale by 2035? China Thinks So". 6 December 2018.
  18. ^ Jacob Eddison; Joe Marsden; Guy Levin; Darshan Vigneswara (2017-12-12), "Matrioshka Brain", Journal of Physics Special Topics, Department of Physics and Astronomy, University of Leicester, 16 (1)
  19. ^ Moore, Gordon E. (1965). "Cramming more components onto integrated circuits" (PDF). Electronics Magazine. p. 4. Retrieved 2006-11-11.

External links[edit]