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author Geogaddi\David <d.m.ronan@qmul.ac.uk>
date Fri, 05 Feb 2016 19:21:42 +0000
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d@0 3 <title>Thread safety - FFTW 3.2.1</title>
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d@0 49 <a name="Thread-safety"></a>
d@0 50 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>,
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d@0 54
d@0 55 <h3 class="section">5.4 Thread safety</h3>
d@0 56
d@0 57 <p><a name="index-threads-327"></a><a name="index-thread-safety-328"></a>Users writing multi-threaded programs must concern themselves with the
d@0 58 <dfn>thread safety</dfn> of the libraries they use&mdash;that is, whether it is
d@0 59 safe to call routines in parallel from multiple threads. FFTW can be
d@0 60 used in such an environment, but some care must be taken because the
d@0 61 planner routines share data (e.g. wisdom and trigonometric tables)
d@0 62 between calls and plans.
d@0 63
d@0 64 <p>The upshot is that the only thread-safe (re-entrant) routine in FFTW is
d@0 65 <code>fftw_execute</code> (and the new-array variants thereof). All other routines
d@0 66 (e.g. the planner) should only be called from one thread at a time. So,
d@0 67 for example, you can wrap a semaphore lock around any calls to the
d@0 68 planner; even more simply, you can just create all of your plans from
d@0 69 one thread. We do not think this should be an important restriction
d@0 70 (FFTW is designed for the situation where the only performance-sensitive
d@0 71 code is the actual execution of the transform), and the benefits of
d@0 72 shared data between plans are great.
d@0 73
d@0 74 <p>Note also that, since the plan is not modified by <code>fftw_execute</code>,
d@0 75 it is safe to execute the <em>same plan</em> in parallel by multiple
d@0 76 threads. However, since a given plan operates by default on a fixed
d@0 77 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.
d@0 78
d@0 79 <p>(Users should note that these comments only apply to programs using
d@0 80 shared-memory threads. Parallelism using MPI or forked processes
d@0 81 involves a separate address-space and global variables for each process,
d@0 82 and is not susceptible to problems of this sort.)
d@0 83
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