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python-scientific-2.9.4-8.mga7.armv7hl.rpm

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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class TaskManager</h1><p class="nomargin-top"></p>
<p>Parallel task manager for shared-memory multiprocessor machines</p>
  <p>This class provides a rather simple way to profit from shared-memory 
  multiprocessor machines by running several tasks in parallel. The calling
  program decides how many execution threads should run at any given time, 
  and then feeds compute tasks to the task manager, who runs them as soon 
  as possible without exceeding the maximum number of threads.</p>
  <p>The major limitation of this approach lies in Python's Global 
  Interpreter Lock. This effectively means that no more than one Python 
  thread can run at the same time. Consequently, parallelization can only 
  be achieved if the tasks to be parallelized spend significant time in C 
  extension modules that release the Global Interpreter Lock.</p>

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          <td><span class="summary-sig"><a href="Scientific.Threading.TaskManager.TaskManager-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">nthreads</span>)</span></td>
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          <td><span class="summary-sig"><a href="Scientific.Threading.TaskManager.TaskManager-class.html#runTask" class="summary-sig-name">runTask</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">function</span>,
        <span class="summary-sig-arg">args</span>)</span><br />
      Run a task as soon as processing capacity becomes available</td>
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          <td><span class="summary-sig"><a name="terminate"></a><span class="summary-sig-name">terminate</span>(<span class="summary-sig-arg">self</span>)</span><br />
      Wait until all tasks have finished</td>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">nthreads</span>)</span>
    <br /><em class="fname">(Constructor)</em>
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    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>nthreads</code></strong> (<code>int</code>) - the maximum number of compute threads that should run in 
          parallel. Note: This does not include the main thread which 
          generated and feeds the task manager!</li>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">runTask</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">function</span>,
        <span class="sig-arg">args</span>)</span>
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  <p>Run a task as soon as processing capacity becomes available</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>function</code></strong> (callable) - the function that will be executed as the body of the task</li>
        <li><strong class="pname"><code>args</code></strong> (<code>tuple</code>) - the arguments that will be passed to function when it is called. 
          An additional argument will be added at the end: a lock object 
          that the task can use to get temporarily exclusive access to data
          shared with other tasks.</li>
    </ul></dd>
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