diff src/fftw-3.3.5/doc/html/Load-balancing.html @ 42:2cd0e3b3e1fd

Current fftw source
author Chris Cannam
date Tue, 18 Oct 2016 13:40:26 +0100
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+Copyright (C) 2003 Matteo Frigo.
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+<a name="Load-balancing"></a>
+<div class="header">
+<p>
+Next: <a href="Transposed-distributions.html#Transposed-distributions" accesskey="n" rel="next">Transposed distributions</a>, Previous: <a href="Basic-and-advanced-distribution-interfaces.html#Basic-and-advanced-distribution-interfaces" accesskey="p" rel="prev">Basic and advanced distribution interfaces</a>, Up: <a href="MPI-Data-Distribution.html#MPI-Data-Distribution" accesskey="u" rel="up">MPI Data Distribution</a> &nbsp; [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>][<a href="Concept-Index.html#Concept-Index" title="Index" rel="index">Index</a>]</p>
+</div>
+<hr>
+<a name="Load-balancing-1"></a>
+<h4 class="subsection">6.4.2 Load balancing</h4>
+<a name="index-load-balancing"></a>
+
+<p>Ideally, when you parallelize a transform over some <em>P</em>
+processes, each process should end up with work that takes equal time.
+Otherwise, all of the processes end up waiting on whichever process is
+slowest.  This goal is known as &ldquo;load balancing.&rdquo;  In this section,
+we describe the circumstances under which FFTW is able to load-balance
+well, and in particular how you should choose your transform size in
+order to load balance.
+</p>
+<p>Load balancing is especially difficult when you are parallelizing over
+heterogeneous machines; for example, if one of your processors is a
+old 486 and another is a Pentium IV, obviously you should give the
+Pentium more work to do than the 486 since the latter is much slower.
+FFTW does not deal with this problem, however&mdash;it assumes that your
+processes run on hardware of comparable speed, and that the goal is
+therefore to divide the problem as equally as possible.
+</p>
+<p>For a multi-dimensional complex DFT, FFTW can divide the problem
+equally among the processes if: (i) the <em>first</em> dimension
+<code>n0</code> is divisible by <em>P</em>; and (ii), the <em>product</em> of
+the subsequent dimensions is divisible by <em>P</em>.  (For the advanced
+interface, where you can specify multiple simultaneous transforms via
+some &ldquo;vector&rdquo; length <code>howmany</code>, a factor of <code>howmany</code> is
+included in the product of the subsequent dimensions.)
+</p>
+<p>For a one-dimensional complex DFT, the length <code>N</code> of the data
+should be divisible by <em>P</em> <em>squared</em> to be able to divide
+the problem equally among the processes.
+</p>
+
+
+
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