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HandWiki. Denormal Number. Encyclopedia. Available online: https://encyclopedia.pub/entry/33472 (accessed on 11 October 2026).
HandWiki. Denormal Number. Encyclopedia. Available at: https://encyclopedia.pub/entry/33472. Accessed October 11, 2026.
HandWiki. "Denormal Number" Encyclopedia, https://encyclopedia.pub/entry/33472 (accessed October 11, 2026).
HandWiki. (2022, November 08). Denormal Number. In Encyclopedia. https://encyclopedia.pub/entry/33472
HandWiki. "Denormal Number." Encyclopedia. Web. 08 November, 2022.
Denormal Number
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In computer science, subnormal numbers are the subset of denormalized numbers (sometimes called denormals) that fill the underflow gap around zero in floating-point arithmetic. Any non-zero number with magnitude smaller than the smallest normal number is subnormal. In a normal floating-point value, there are no leading zeros in the significand or mantissa; rather, leading zeros are removed by adjusting the exponent (for example, the number 0.0123 would be written as 1.23 × 10−2). Conversely, a denormalized floating point value has a significand with a leading digit of zero. Of these, the subnormal numbers represent values which if normalized would have exponents below the smallest representable exponent (the exponent having a limited range). The significand (or mantissa) of an IEEE floating-point number is the part of a floating-point number that represents the significant digits. For a positive normalised number it can be represented as m0.m1m2m3...mp−2mp−1 (where m represents a significant digit, and p is the precision) with non-zero m0. Notice that for a binary radix, the leading binary digit is always 1. In a subnormal number, since the exponent is the least that it can be, zero is the leading significant digit (0.m1m2m3...mp−2mp−1), allowing the representation of numbers closer to zero than the smallest normal number. A floating-point number may be recognized as subnormal whenever its exponent is the least value possible. By filling the underflow gap like this, significant digits are lost, but not as abruptly as when using the flush to zero on underflow approach (discarding all significant digits when underflow is reached). Hence the production of a subnormal number is sometimes called gradual underflow because it allows a calculation to lose precision slowly when the result is small. In IEEE 754-2008, denormal numbers are renamed subnormal numbers and are supported in both binary and decimal formats. In binary interchange formats, subnormal numbers are encoded with a biased exponent of 0, but are interpreted with the value of the smallest allowed exponent, which is one greater (i.e., as if it were encoded as a 1). In decimal interchange formats they require no special encoding because the format supports unnormalized numbers directly. Mathematically speaking, the normalized floating-point numbers of a given sign are roughly logarithmically spaced, and as such any finite-sized normal float cannot include zero. The subnormal floats are a linearly spaced set of values, which span the gap between the negative and positive normal floats.

denormal numbers linearly mathematically

References

  1. William Kahan. "IEEE 754R meeting minutes, 2002". http://grouper.ieee.org/groups/754/meeting-minutes/02-09-19.html#underflow. 
  2. "An Interview with the Old Man of Floating-Point". University of California, Berkeley. http://www.eecs.berkeley.edu/~wkahan/ieee754status/754story.html. 
  3. Schwarz, E.M.; Schmookler, M.; Son Dao Trong (July 2005). "FPU Implementations with Denormalized Numbers". IEEE Transactions on Computers 54 (7): 825–836. doi:10.1109/TC.2005.118. http://www.acsel-lab.com/arithmetic/arith16/papers/ARITH16_Schwarz.pdf. 
  4. Dooley, Isaac; Kale, Laxmikant (2006-09-12). "Quantifying the Interference Caused by Subnormal Floating-Point Values". http://charm.cs.uiuc.edu/papers/SubnormalOSIHPA06.pdf. 
  5. Fog, Agner. "Instruction tables: Lists of instruction latencies, throughputs and microoperation breakdowns for Intel, AMD and VIA CPUs". http://www.agner.org/optimize/instruction_tables.pdf. 
  6. Andrysco, Marc; Kohlbrenner, David; Mowery, Keaton; Jhala, Ranjit; Lerner, Sorin; Shacham, Hovav. "On Subnormal Floating Point and Abnormal Timing". https://cseweb.ucsd.edu/~dkohlbre/papers/subnormal.pdf. 
  7. Serris, John (2002-04-16). "Pentium 4 denormalization: CPU spikes in audio applications". http://phonophunk.com/articles/pentium4-denormalization.php. 
  8. de Soras, Laurent (2005-04-19). "Denormal numbers in floating point signal processing applications". http://ldesoras.free.fr/doc/articles/denormal-en.pdf. 
  9. Casey, Shawn (2008-10-16). "x87 and SSE Floating Point Assists in IA-32: Flush-To-Zero (FTZ) and Denormals-Are-Zero (DAZ)". http://software.intel.com/en-us/articles/x87-and-sse-floating-point-assists-in-ia-32-flush-to-zero-ftz-and-denormals-are-zero-daz/. 
  10. "Intel® MPI Library – Documentation". Intel. http://software.intel.com/sites/products/documentation/hpc/composerxe/en-us/2011Update/fortran/win/fpops/common/fpops_reduce_denorm.htm. 
  11. "Re: Macbook pro performance issue". Apple Inc.. https://lists.apple.com/archives/perfoptimization-dev/2006/May/msg00013.html. 
  12. "Re: Changing floating point state (Was: double vs float performance)". Apple Inc.. http://lists.apple.com/archives/perfoptimization-dev/2007/Jun/msg00025.html. 
  13. "C++ Compiler for Linux* Systems User's Guide". Intel. https://software.intel.com/sites/default/files/ae/4f/6320. 
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