Changes

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}}

* Load: {{Codeline|pkg load parallel}}

== multicore parallelization (parcellfun, pararrayfun) ==

== Multicore parallelization (parcellfun, pararrayfun) ==

<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;

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

     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.

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;

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.

=== 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).

<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;

                                 @(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 ==

 

 

== 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]]