Orders_of_magnitude_(data)

Orders of magnitude (data)

Orders of magnitude (data)

Computer data measurements and scales


An order of magnitude is usually a factor of ten. Thus, four orders of magnitude is a factor of 10,000 or 104.

This article presents a list of multiples, sorted by orders of magnitude, for units of information measured in bits and bytes.

The byte is a common unit of measurement of information (kilobyte, kibibyte, megabyte, mebibyte, gigabyte, gibibyte, terabyte, tebibyte, etc.). For the purpose of this article, a byte is a group of 8 bits (octet), a nibble is a group of four bits. Historically, neither assumption has always been true.

The decimal SI prefixes kilo, mega, giga, tera, etc., are powers of 103 = 1000. The binary prefixes kibi, mebi, gibi, tebi, etc. respectively refer to the corresponding power of 210 = 1024.

In casual usage, when 1024 is a close enough approximation of 1000, some of the decimal prefixes have been used in relation to computer memories to mean the binary power, but increasingly from 1998, standards bodies have chosen to limit the resultant confusion by disallowing when software displays a binary quantity with a decimal prefix.[1][2] Microsoft operating systems still report file and free spaces on a storage device in this casual sense.

More information Binary [bits], Decimal ...

Note: this page mixes between two kinds of entropies:

  1. Entropy (information theory), such as the amount of information that can be stored in DNA
  2. Entropy (thermodynamics), such as entropy increase of 1 mole of water

These two definitions are not entirely equivalent, see Entropy in thermodynamics and information theory.

For comparison, the Avogadro constant is 6.02214076×1023 entities per mole, based upon the number of atoms in 12 grams of carbon-12 isotope.

In 2012, some hard disks used ~984,573 atoms to store each bit. In January 2012, IBM researchers announced they compressed 1 bit in 12 atoms using antiferromagnetism and a scanning tunneling microscope with iron and copper atoms. This could mean a practical jump from a 1 TB disk to a 100 TB disk.[5][32]

See also


References

  1. "Definitions of the SI units: The binary prefixes". physics.nist.gov. Retrieved 17 June 2020.
  2. "quantifiers". www.catb.org. Retrieved 24 January 2022.
  3. Mark Nelson (24 August 2006). "The Hutter Prize". Retrieved 27 November 2008.
  4. "A special report on managing information: All too much". The Economist. 25 February 2010. Retrieved 4 March 2010.
  5. "Cost of Hard Drive Space". 11 May 2013. Retrieved 23 June 2013.
  6. "How much does DivX shrink a file?". 18 April 2002. Retrieved 24 June 2013.
  7. Microsoft TechNet (28 March 2003). "How NTFS Works". Windows Server 2003 Technical Reference. Retrieved 12 September 2011.
  8. Hickey, Thom (OCLC Chief Scientist) (21 June 2005). "Entire Library of Congress". Outgoing. Retrieved 5 May 2010.
  9. 25 petabyte on Megaupload. Archived 1 August 2012 at archive.today Retrieved 16 February 2012
  10. [citation needed] J K−1
  11. "Internet data heads for 500bn gigabytes", The Guardian, 18 May 2009. Retrieved on 23 April 2010.
  12. "Working Draft T10, American National Standard Project 1417-D, Revision 4, 28 July 2001" (PDF). o3one.org. 8 January 2002. p. 72. Retrieved 23 June 2013.
  13. 1 J K−1. Equivalent to 1/(k ln 2) bits, where k is the Boltzmann constant
  14. Equivalent to 5.74 J K−1. Standard molar entropy of graphite.
  15. Equivalent to 69.95 J K−1. Standard molar entropy of water.
  16. Equivalent to 108.9 J K−1
  17. Equivalent to 146.33 J K−1. Standard molar entropy of neon. An experimental value, see for a theoretical calculation.
  18. "What Is ZFS? - Oracle Solaris ZFS Administration Guide". docs.oracle.com. Retrieved 6 May 2021.
  19. "To Boil the Oceans". Harder, Better, Faster, Stronger. 10 February 2009. Retrieved 6 May 2021.
  20. Given as 1042 erg K−1 in Bekenstein (1973), Black Holes and Entropy[permanent dead link], Physical Review D 7 2338
  21. Entropy = in nats, with for a Schwarzschild black hole. 1 nat = 1/ln(2) bits. See Jacob D. Bekenstein (2008), Bekenstein-Hawking entropy, Scholarpedia.
  22. Lloyd, Seth (24 May 2002). "Computational Capacity of the Universe" (PDF). Physical Review Letters. 88 (23): 237901. arXiv:quant-ph/0110141. Bibcode:2002PhRvL..88w7901L. doi:10.1103/PhysRevLett.88.237901. PMID 12059399. S2CID 6341263. Archived (PDF) from the original on 11 November 2017.
  23. "IBM Smashes Moore's Law, Cuts Bit Size to 12 Atoms". 12 January 2012. Retrieved 23 June 2013.

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