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The | The {{Forge|parallel|parallel package}} is part of the Octave Forge project. See its {{Forge|parallel|homepage}} for the latest release. | ||
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. | |||
* Install: {{Codeline|pkg install -forge parallel}} | |||
* Load: {{Codeline|pkg load parallel}} | |||
== | == Multicore parallelization (parcellfun, pararrayfun) == | ||
=== Calculation on a single array === | |||
<syntaxhighlight lang="octave"> | |||
# 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) | ||
</ | </syntaxhighlight> | ||
should output | should output | ||
< | <syntaxhighlight lang="text"> | ||
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 | ||
</ | </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. | ||
| Line 40: | Line 36: | ||
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. | ||
<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; | ||
| Line 47: | Line 43: | ||
vector_y = pararrayfun(nproc, fun, vector_x, "Vectorized", true, "ChunksPerProc", 1) | vector_y = pararrayfun(nproc, fun, vector_x, "Vectorized", true, "ChunksPerProc", 1) | ||
</ | </syntaxhighlight> | ||
should output | should output | ||
< | <syntaxhighlight lang="text"> | ||
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 | ||
</ | </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 === | |||
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"> | |||
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) | |||
</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. | |||
== References == | |||
<references /> | |||
== See also == | |||
* [[File:Examples_of_how_to_use_parrarrayfun.pdf]] | |||
* [[NDpar package]] - an extension of these functions to N-dimensional arrays | |||
[[Category:Octave Forge]] | |||
Latest revision as of 03:09, 4 March 2021
The parallel package is part of the Octave Forge project. See its homepage for the latest release.
This package provides utilities to work with clusters[1], but also functions to parallelize work among cores of a single machine.
- Install:
pkg install -forge parallel - Load:
pkg load parallel
Multicore parallelization (parcellfun, pararrayfun)[edit]
Calculation on a single array[edit]
# 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.
# 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).
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.
References[edit]
See also[edit]
- File:Examples of how to use parrarrayfun.pdf
- NDpar package - an extension of these functions to N-dimensional arrays