Difference between revisions of "Parallel package"

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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 Parallel execution package provides utilities to work with clusters, but also functions to parallelize work among cores of a single machine.
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 +
To install: {{Codeline|pkg install -forge parallel}}
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And then, once on each octave session, {{Codeline|pkg load parallel}}
  
 
== multicore parallelization (parcellfun, pararrayfun) ==
 
== multicore parallelization (parcellfun, pararrayfun) ==
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See also the [[NDpar package]], for an extension of these functions to N-dimensional arrays
  
 
=== calculation on a single array ===
 
=== calculation on a single array ===
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</pre>
 
</pre>
 
}}
 
}}
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should output
 
should output
  
<code>
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<code><pre>
<pre>
 
 
parcellfun: 10/10 jobs done
 
parcellfun: 10/10 jobs done
  
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     1    4    9    16    25    36    49    64    81  100
 
     1    4    9    16    25    36    49    64    81  100
</pre>
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</pre></code>
</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.
 
{{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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should output
 
should output
  
<code>
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<code><pre>
<pre>
 
 
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
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</pre></code>
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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 ===
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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).
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{{code|diagonalize NxN matrices contained in an array|
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<pre>
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A = [0.6060168 0.8340029 0.0064574 0.7133187;
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0.6325375 0.0919912 0.5692567 0.7432627;
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0.8292699 0.5136958 0.4171895 0.2530783;
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0.7966113 0.1975865 0.6687064 0.3226548;
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0.0163615 0.2123476 0.9868179 0.1478827];
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N = 2;
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[eigenvectors, eigenvalues] = pararrayfun(nproc,
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                                @(row_idx) eig(reshape(A(row_idx, :), N, N)),
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                                1:rows(A), "UniformOutput", false)
 
</pre>
 
</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.
+
 
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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.
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== cluster operation ==
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Documentation can be found in the {{codeline|README.parallel}} or {{codeline|README.bw}} files, located inside the {{codeline|doc}} directory of the parallel package.
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[[Category:Octave Forge]]

Latest revision as of 04:18, 10 June 2019

The Parallel execution package provides utilities to work with clusters, but also functions to parallelize work among cores of a single machine.

To install: pkg install -forge parallel

And then, once on each octave session, pkg load parallel

multicore parallelization (parcellfun, pararrayfun)[edit]

See also the NDpar package, for an extension of these functions to N-dimensional arrays

calculation on a single array[edit]

Code: simple
# fun is the function to apply 
fun = @(x) x^2;

vector_x = 1:10;

vector_y = pararrayfun(nproc, fun, vector_x)

should output

parcellfun: 10/10 jobs done

vector_y =

     1     4     9    16    25    36    49    64    81   100

nproc returns the number of cpus available (number of cores or twice as much with hyperthreading). One can use nproc - 1 instead, in order to leave one cpu free for instance.

fun can be replaced by @myfun if the function resides in the myfun.m file.

In the previous example, the function was executed once for each element of the input vector_x. 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 "Vectorized", true option.

Code: vectorized
# fun is the function to apply, vectorized (see the dot)
fun = @(x) x.^2;

vector_x = 1:10;

vector_y = pararrayfun(nproc, fun, vector_x, "Vectorized", true, "ChunksPerProc", 1)

should output

parcellfun: 4/4 jobs done
vector_y =

     1     4     9    16    25    36    49    64    81   100

The "ChunksPerProc" option is mandatory with "Vectorized", true. 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 "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[edit]

The following sample code was an answer to 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
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)

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

cluster operation[edit]

Documentation can be found in the README.parallel or README.bw files, located inside the doc directory of the parallel package.