Precomputation
In
, rather than computing their approximations to the necessary precision at run time.In databases, the term materialization is used to refer to storing the results of a precomputation,[1][2] such as in a materialized view.[3][4]
Overview
Precomputing a set of intermediate results at the beginning of an algorithm's execution can often increase
interpolating
for intermediate input values, since interpolation is also a linear operation.
History
Before the advent of computers, printed
statistical density functions[5]
School children are often taught to memorize "times tables" to avoid calculations of the most commonly used numbers (up to 9 x 9 or 12 x 12). Even as early as 493 A.D., Victorius of Aquitaine wrote a 98-column multiplication table which gave (in Roman numerals) the product of every number from 2 to 50 times and the rows were "a list of numbers starting with one thousand, descending by hundreds to one hundred, then descending by tens to ten, then by ones to one, and then the fractions down to 1/144" [6]
Examples
Even modern computer implementations of digital
trigonometric functions often use precomputed lookup tables to either provide coefficients for interpolation algorithms or to initialise successive approximation algorithms
.
Many attacks on cryptosystems involve precomputation.
Examples of large-scale precomputation as part of modern efficient algorithms include:
- Rainbow tables
- Perfect hashes
- The cube attack
- Precalculated BSP treesfor visibility calculations in 3D graphics
- Radiosityprecomputation for illumination in 3D graphics
dataflow analysis and strength reduction
steps.
See also
References
- ISBN 978-0-12-381480-7.
- ISBN 978-3-642-19357-6.
- ISBN 978-1-4302-6220-6.
- ISBN 978-3-642-27357-5.
- ISBN 978-0-19-850841-0.
- ^ Maher, David. W. J. and John F. Makowski. "Literary Evidence for Roman Arithmetic With Fractions", 'Classical Philology' (2001) Vol. 96 No. 4 (2001) pp. 376–399. (See page p. 383.)