[Python-checkins] bpo-26256: Document algorithm speed for the Decimal module. (#4808)

[Stefan Krah]

https://github.com/python/cpython/commit/00e9c55d27aff3e445ab4c8629cf4d59f46ff945
commit: 00e9c55d27aff3e445ab4c8629cf4d59f46ff945
branch: master
author: Cheryl Sabella <cheryl.sabella at gmail.com>
committer: Stefan Krah <skrah at bytereef.org>
date: 2019-02-02T15:37:39+01:00
summary:

bpo-26256: Document algorithm speed for the Decimal module. (#4808)

files:
M Doc/library/decimal.rst

diff --git a/Doc/library/decimal.rst b/Doc/library/decimal.rst
index f2a677e69363..bcae55eb8217 100644
--- a/Doc/library/decimal.rst
+++ b/Doc/library/decimal.rst
@@ -2115,3 +2115,23 @@ Alternatively, inputs can be rounded upon creation using the
 
    >>> Context(prec=5, rounding=ROUND_DOWN).create_decimal('1.2345678')
    Decimal('1.2345')
+
+Q. Is the CPython implementation fast for large numbers?
+
+A. Yes.  In the CPython and PyPy3 implementations, the C/CFFI versions of
+the decimal module integrate the high speed `libmpdec
+<https://www.bytereef.org/mpdecimal/doc/libmpdec/index.html>`_ library for
+arbitrary precision correctly-rounded decimal floating point arithmetic.
+``libmpdec`` uses `Karatsuba multiplication
+<https://en.wikipedia.org/wiki/Karatsuba_algorithm>`_
+for medium-sized numbers and the `Number Theoretic Transform
+<https://en.wikipedia.org/wiki/Discrete_Fourier_transform_(general)#Number-theoretic_transform>`_
+for very large numbers.  However, to realize this performance gain, the
+context needs to be set for unrounded calculations.
+
+    >>> c = getcontext()
+    >>> c.prec = MAX_PREC
+    >>> c.Emax = MAX_EMAX
+    >>> c.Emin = MIN_EMIN
+
+.. versionadded:: 3.3
\ No newline at end of file