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<title>GNU Scientific Library – Reference Manual: Alternative optimized functions</title>
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<a name="Alternative-optimized-functions"></a>
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Next: <a href="Support-for-different-numeric-types.html#Support-for-different-numeric-types" accesskey="n" rel="next">Support for different numeric types</a>, Previous: <a href="Portability-functions.html#Portability-functions" accesskey="p" rel="previous">Portability functions</a>, Up: <a href="Using-the-library.html#Using-the-library" accesskey="u" rel="up">Using the library</a> [<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
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<a name="Alternative-optimized-functions-1"></a>
<h3 class="section">2.8 Alternative optimized functions</h3>
<a name="index-alternative-optimized-functions"></a>
<a name="index-optimized-functions_002c-alternatives"></a>
<p>The main implementation of some functions in the library will not be
optimal on all architectures. For example, there are several ways to
compute a Gaussian random variate and their relative speeds are
platform-dependent. In cases like this the library provides alternative
implementations of these functions with the same interface. If you
write your application using calls to the standard implementation you
can select an alternative version later via a preprocessor definition.
It is also possible to introduce your own optimized functions this way
while retaining portability. The following lines demonstrate the use of
a platform-dependent choice of methods for sampling from the Gaussian
distribution,
</p>
<div class="example">
<pre class="example">#ifdef SPARC
#define gsl_ran_gaussian gsl_ran_gaussian_ratio_method
#endif
#ifdef INTEL
#define gsl_ran_gaussian my_gaussian
#endif
</pre></div>
<p>These lines would be placed in the configuration header file
<samp>config.h</samp> of the application, which should then be included by all
the source files. Note that the alternative implementations will not
produce bit-for-bit identical results, and in the case of random number
distributions will produce an entirely different stream of random
variates.
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