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First commit. VS2013, Codeblocks and Mac OSX configuration
author Geogaddi\David <d.m.ronan@qmul.ac.uk>
date Thu, 09 Jul 2015 01:12:16 +0100
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+<html lang="en">
+<head>
+<title>Thread safety - FFTW 3.2.1</title>
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+<p>
+<a name="Thread-safety"></a>
+Previous:&nbsp;<a rel="previous" accesskey="p" href="How-Many-Threads-to-Use_003f.html#How-Many-Threads-to-Use_003f">How Many Threads to Use?</a>,
+Up:&nbsp;<a rel="up" accesskey="u" href="Multi_002dthreaded-FFTW.html#Multi_002dthreaded-FFTW">Multi-threaded FFTW</a>
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+
+<h3 class="section">5.4 Thread safety</h3>
+
+<p><a name="index-threads-327"></a><a name="index-thread-safety-328"></a>Users writing multi-threaded programs must concern themselves with the
+<dfn>thread safety</dfn> of the libraries they use&mdash;that is, whether it is
+safe to call routines in parallel from multiple threads.  FFTW can be
+used in such an environment, but some care must be taken because the
+planner routines share data (e.g. wisdom and trigonometric tables)
+between calls and plans.
+
+   <p>The upshot is that the only thread-safe (re-entrant) routine in FFTW is
+<code>fftw_execute</code> (and the new-array variants thereof).  All other routines
+(e.g. the planner) should only be called from one thread at a time.  So,
+for example, you can wrap a semaphore lock around any calls to the
+planner; even more simply, you can just create all of your plans from
+one thread.  We do not think this should be an important restriction
+(FFTW is designed for the situation where the only performance-sensitive
+code is the actual execution of the transform), and the benefits of
+shared data between plans are great.
+
+   <p>Note also that, since the plan is not modified by <code>fftw_execute</code>,
+it is safe to execute the <em>same plan</em> in parallel by multiple
+threads.  However, since a given plan operates by default on a fixed
+array, you need to use one of the new-array execute functions (see <a href="New_002darray-Execute-Functions.html#New_002darray-Execute-Functions">New-array Execute Functions</a>) so that different threads compute the transform of different data.
+
+   <p>(Users should note that these comments only apply to programs using
+shared-memory threads.  Parallelism using MPI or forked processes
+involves a separate address-space and global variables for each process,
+and is not susceptible to problems of this sort.)
+
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