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The Parallel execution package provides utilities to work with clusters, but also functions to parallelize work among cores of a single machine.
 
To install: {{Codeline|pkg install -forge parallel}}
 
And then, once on each octave session, {{Codeline|pkg load parallel}}
== multicore parallelization (parcellfun, pararrayfun) ==
 
 
See also the [[NDpar package]], for an extension of these functions to N-dimensional arrays
=== calculation on a single array ===
</pre>
}}
 
should output
<code><pre>
parcellfun: 10/10 jobs done
1 4 9 16 25 36 49 64 81 100
</pre></code>
{{Codeline|nproc}} returns the number of cpus available (number of cores or twice as much with hyperthreading). One can use {{Codeline|nproc - 1}} instead, in order to leave one cpu free for instance.
should output
<code><pre>
parcellfun: 4/4 jobs done
vector_y =
1 4 9 16 25 36 49 64 81 100
</pre></code>
 
The {{Codeline|"ChunksPerProc"}} option is mandatory with {{Codeline|"Vectorized", true}}. {{Codeline|1}} means that each proc will do its job in one shot (chunk). This number can be increased to use less memory for instance. A higher number of {{Codeline|"ChunksPerProc"}} allows also more flexibility in case of long calculations on a busy machine. If one cpu has finished all its jobs, it can take over the pending jobs of another.
 
=== Output in cell arrays ===
 
The following sample code was an answer to [http://stackoverflow.com/questions/27422219/for-every-row-reshape-and-calculate-eigenvectors-in-a-vectorized-way this question]. The goal was to diagonalize 2x2 matrices contained as rows of a 2d array (each row of the array being a flattened 2x2 matrix).
 
{{code|diagonalize NxN matrices contained in an array|
<pre>
A = [0.6060168 0.8340029 0.0064574 0.7133187;
0.6325375 0.0919912 0.5692567 0.7432627;
0.8292699 0.5136958 0.4171895 0.2530783;
0.7966113 0.1975865 0.6687064 0.3226548;
0.0163615 0.2123476 0.9868179 0.1478827];
 
N = 2;
[eigenvectors, eigenvalues] = pararrayfun(nproc,
@(row_idx) eig(reshape(A(row_idx, :), N, N)),
1:rows(A), "UniformOutput", false)
</pre>
</code>}} The With {{Codelinecodeline|"ChunksPerProcUniformOutput", false}} option is mandatory with , the outputs are contained in cell arrays (one cell per slice). In the sample above, both {{Codelinecodeline|"Vectorized", trueeigenvectors}} and {{codeline|eigenvalues}}. are {{Codelinecodeline|11x5}} means that each proc will do its job in one shot (chunk)cell arrays. This number  == cluster operation == Documentation can be increased to use less memory for instancefound in the {{codeline|README. A higher number of parallel}} or {{Codelinecodeline|"ChunksPerProc"README.bw}} allows also more flexibility in case of long calculations on a busy machine. If one cpu has finished all its jobsfiles, it can take over located inside the pending jobs {{codeline|doc}} directory of anotherthe parallel package[[Category:Octave Forge]]

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