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

Hecto-scale computing (102)[edit]

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

Kilo-scale computing (103)[edit]

Mega-scale computing (106)[edit]

Giga-scale computing (109)[edit]

Tera-scale computing (1012)[edit]

Petascale computing (1015)[edit]

Main article: Petascale computing
  • 1.026×1015 IBM Roadrunner 2009 Supercomputer
  • 8.1×1015 Fastest computer system as of 2012 is the Folding@home distributed computing system
  • 17.17×1015 IBM Sequoia's Linpack performance, June 2013[4]
  • 33.86×1015 Tianhe-2's Linpack performance, June 2013[4]
  • 36.8×1015 Estimated computational power required to simulate a human brain in real time.[5]

Exascale computing (1018)[edit]

Main article: Exascale computing
  • 1×1018 It is estimated that the need for exascale computing will become pressing around 2018[6]
  • 1×1018 Bitcoin network Hash Rate is expected to reach 1 Exahash per seconds in 2016[7]

Zetta-scale computing (1021)[edit]

  • 1×1021 Accurate global weather estimation on the scale of approximately 2 weeks.[8] Assuming Moore's law remains constant, such systems may be feasible around 2030.

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

Yotta-scale computing (1024)[edit]

  • 257.6×1024 Estimated computational power required to simulate 7 billion brains in real time.

See also[edit]


External links[edit]