Parallel package: Difference between revisions

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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.
The {{Forge|parallel|parallel package}} is part of the Octave Forge project. See its {{Forge|parallel|homepage}} for the latest release.


To install: {{Codeline|pkg install -forge parallel}}
This package provides utilities to work with clusters<ref>[https://octave.sourceforge.io/parallel/package_doc/ Package documentation]</ref>, but also functions to parallelize work among cores of a single machine.


And then, once on each octave session, {{Codeline|pkg load parallel}}
* Install: {{Codeline|pkg install -forge parallel}}
* Load: {{Codeline|pkg load parallel}}


== multicore parallelization (parcellfun, pararrayfun) ==
== Multicore parallelization (parcellfun, pararrayfun) ==


=== Calculation on a single array ===


See also the [[NDpar package]], for an extension of these functions to N-dimensional arrays
<syntaxhighlight lang="octave">
 
=== calculation on a single array ===
 
{{Code|simple|<pre>
# fun is the function to apply  
# fun is the function to apply  
fun = @(x) x^2;
fun = @(x) x^2;
Line 19: Line 17:


vector_y = pararrayfun(nproc, fun, vector_x)
vector_y = pararrayfun(nproc, fun, vector_x)
</pre>
</syntaxhighlight>
}}


should output
should output


<code><pre>
<syntaxhighlight lang="plain">
parcellfun: 10/10 jobs done
parcellfun: 10/10 jobs done


Line 30: Line 27:


     1    4    9    16    25    36    49    64    81  100
     1    4    9    16    25    36    49    64    81  100
</pre></code>
</syntaxhighlight>


{{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.
{{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.
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If the function is vectorized (can act on a vector and not just on scalar input), then it can be much more efficient to use the {{Codeline|"Vectorized", true}} option.
If the function is vectorized (can act on a vector and not just on scalar input), then it can be much more efficient to use the {{Codeline|"Vectorized", true}} option.


{{Code|vectorized|<pre>
<syntaxhighlight lang="octave">
# fun is the function to apply, vectorized (see the dot)
# fun is the function to apply, vectorized (see the dot)
fun = @(x) x.^2;
fun = @(x) x.^2;
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vector_y = pararrayfun(nproc, fun, vector_x, "Vectorized", true, "ChunksPerProc", 1)
vector_y = pararrayfun(nproc, fun, vector_x, "Vectorized", true, "ChunksPerProc", 1)
</pre>
</syntaxhighlight>
}}
should output
should output


<code><pre>
<syntaxhighlight lang="plain">
parcellfun: 4/4 jobs done
parcellfun: 4/4 jobs done
vector_y =
vector_y =


     1    4    9    16    25    36    49    64    81  100
     1    4    9    16    25    36    49    64    81  100
</pre></code>
</syntaxhighlight>


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.
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.
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=== Output in cell arrays ===
=== 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).
The following sample code was an answer to [https://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|
<syntaxhighlight lang="octave">
<pre>
A = [0.6060168 0.8340029 0.0064574 0.7133187;
A = [0.6060168 0.8340029 0.0064574 0.7133187;
0.6325375 0.0919912 0.5692567 0.7432627;
    0.6325375 0.0919912 0.5692567 0.7432627;
0.8292699 0.5136958 0.4171895 0.2530783;
    0.8292699 0.5136958 0.4171895 0.2530783;
0.7966113 0.1975865 0.6687064 0.3226548;
    0.7966113 0.1975865 0.6687064 0.3226548;
0.0163615 0.2123476 0.9868179 0.1478827];
    0.0163615 0.2123476 0.9868179 0.1478827];


N = 2;
N = 2;
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                                 @(row_idx) eig(reshape(A(row_idx, :), N, N)),  
                                 @(row_idx) eig(reshape(A(row_idx, :), N, N)),  
                                 1:rows(A), "UniformOutput", false)
                                 1:rows(A), "UniformOutput", false)
</pre>
</syntaxhighlight>
}}


With {{codeline|"UniformOutput", false}}, the outputs are contained in cell arrays (one cell per slice). In the sample above, both {{codeline|eigenvectors}} and {{codeline|eigenvalues}} are {{codeline|1x5}} cell arrays.
With {{codeline|"UniformOutput", false}}, the outputs are contained in cell arrays (one cell per slice). In the sample above, both {{codeline|eigenvectors}} and {{codeline|eigenvalues}} are {{codeline|1x5}} cell arrays.


== cluster operation ==
== References ==
 
<references />
 
== See also ==


Documentation can be found in the {{codeline|README.parallel}} or {{codeline|README.bw}} files, located inside the {{codeline|doc}} directory of the parallel package.
* [[File:]] - examples of how to use <code>parrarrayfun</code>
* [[NDpar package]] - an extension of these functions to N-dimensional arrays


[[Category:Octave Forge]]
[[Category:Octave Forge]]

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