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Date: Tue, 8 Jun 2010 19:31:14 -0400 (EDT)
From: "William Rossi (JIRA)" <jira@apache.org>
To: issues@commons.apache.org
Subject: [jira] Updated: (MATH-375) Elementary functions in JDK are slower
 than necessary and not as accurate as they could be.
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     [ https://issues.apache.org/jira/browse/MATH-375?page=3Dcom.atlassian.=
jira.plugin.system.issuetabpanels:all-tabpanel ]

William Rossi updated MATH-375:
-------------------------------

    Attachment: FastMath.tar.gz

Description of files in the archive:

FastMath.java                  The software itself
FastMathTest.java           TestNG test case
remez/                                Utilities for generating coefficients=
 of the polynomials  used in the software

remez/lib/dfp10.jar          High precision fp lib used for testing, from d=
fp.sourceforge.net

> Elementary functions in JDK are slower than necessary and not as accurate=
 as they could be.
> -------------------------------------------------------------------------=
------------------
>
>                 Key: MATH-375
>                 URL: https://issues.apache.org/jira/browse/MATH-375
>             Project: Commons Math
>          Issue Type: New Feature
>         Environment: JDK 1.4 - 1.6
>            Reporter: William Rossi
>         Attachments: FastMath.tar.gz
>
>
> I would like to contribute improved versions on exp(), log(), pow(), etc.=
  to the project.  Please refer to this discussion thread http://markmail.o=
rg/message/zyeoguw6gwtofm62.
> I have developed over the past year a set of elementary functions similar=
 to those in java.lang.Math, but with the following characteristics:
> * Higher performance.
> * Better accuracy.  Results are accurate to slightly more that +/- 0.5 UL=
P.
> * Pure Java.  The standard Math class is impleneted via JNI, and thus tak=
es a performance hit.
> Note that some functions such as exp are nearly twice as fast in my imple=
mentation. =C2=A0 I've seen it 3 times faster on different processors. =C2=
=A0 The preformance varies by the relative speed of calculation vs memory l=
ookups.
> The functions are implemented as tables of values in extra precision (app=
rox 70 bits), and then interpolated with a minimax polynomial.

--=20
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