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→Usage: add "diagonalize NxN matrices contained in an array"
* {{codeline|"ChunksPerProc", 2}} It means that each process should make 2 chunks (2 calls to {{codeline|f}} with {{codeline|"Vectorized", true}}). Increase this number to minimize memory usage for instance. Increasing this number is also useful if function executions can have very different durations. If a process is finished, it can take over jobs from another process that is still busy.
* {{codeline|"ChunksPerProc", 2}} It means that each process should make 2 chunks (2 calls to {{codeline|f}} with {{codeline|"Vectorized", true}}). Increase this number to minimize memory usage for instance. Increasing this number is also useful if function executions can have very different durations. If a process is finished, it can take over jobs from another process that is still busy.
=== 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] = ndpar_arrayfun(nproc,
@(row) eig(reshape(row, N, N)),
A, "IdxDimensions", 1, "UniformOutput", false)
</pre>
}}
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.
== Issues ==
== Issues ==
Please report any issue using [https://sourceforge.net/p/ndpar/tickets/ tickets].
Please report any issue using [https://sourceforge.net/p/ndpar/tickets/ tickets].