Unit in the last place

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In computer science and numerical analysis, unit in the last place or unit of least precision (ULP) is the spacing between floating-point numbers, i.e., the value the least significant digit represents if it is 1. It is used as a measure of accuracy in numeric calculations.[citation needed]

Definition[edit]

In radix b, if x has exponent E, then ULP(x) = machine epsilon · bE,[1] but alternative definitions exist in the numerics and computing literature for ULP, exponent and machine epsilon, and they may give different equalities.[citation needed]

Another definition is slightly different: ULP(x) is the distance between the two closest straddling floating-point numbers a and b (i.e., those with axb and ab), assuming that the exponent range is not upper-bounded.[2][3] These definitions differ only at signed powers of the radix.[citation needed]

The IEEE 754 specification—followed by all modern floating-point hardware—requires that the result of an elementary arithmetic operation (addition, subtraction, multiplication, division, and square root since 1985, and FMA since 2008) be within 0.5 ULP of the mathematically exact result, using John Harrison's definition—that is, that it be the best possible result. Reputable numeric libraries compute the basic transcendental functions to between 0.5 and about 1 ULP. Only a few libraries compute them within 0.5 ULP, this problem being complex due to the Table-Maker's Dilemma.[citation needed]

Example[edit]

Let x be a nonnegative floating-point number and assume that the active rounding attribute is round to nearest. If ULP(x) is less than or equal to 1, then x + 1 > x. Otherwise, x + 1 = x. This is demonstrated in the following Haskell code typed at an interactive prompt:[citation needed]

> until (\x -> x == x+1) (+1) 0 :: Float
1.6777216e7
> it-1
1.6777215e7
> it+1
1.6777216e7

Here we start with 0 in 32-bit single-precision and repeatedly add 1 until the operation is idempotent. The result is equal to 224 since the significand for a single-precision number in this example contains 24 bits.[citation needed]

Another example, in Python, also typed at an interactive prompt, is:[citation needed]

>>> x = 1.0
>>> p = 0
>>> while x != x + 1:
...   x = x * 2
...   p = p + 1
... 
>>> x
9007199254740992.0
>>> p
53

In this case, we start with x = 1 and repeatedly double it until x = x + 1. The result is 253, because the double-precision floating-point format uses a 53-bit significand.[citation needed]

Language support[edit]

Since Java 1.5, the Java standard library has included Math.ulp(double) and Math.ulp(float) functions.[citation needed]

The C language library math.h provides the function nextafter to calculate the next double.[citation needed]

The Boost C++ Libraries offer boost::math::float_distance(a, b) to calculate the floating point distance between two doubles.[citation needed]

See also[edit]

References[edit]

  1. ^ Higham, Nicholas (2002). Accuracy and Stability of Numerical Algorithms (2 ed). SIAM. 
  2. ^ Harrison, John. "A Machine-Checked Theory of Floating Point Arithmetic". Retrieved 2013-07-17. 
  3. ^ Muller, Jean-Michel (2005-11). "On the definition of ulp(x)". INRIA Technical Report 5504. ACM Transactions on Mathematical Software, Vol. V, No. N, November 2005. Retrieved in 2012-03 from http://ljk.imag.fr/membres/Carine.Lucas/TPScilab/JMMuller/ulp-toms.pdf.

Bibliography[edit]

External links[edit]