# Parallel package

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)

### Calculation on a single array

```
# 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

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

## See also

- File:Examples of how to use parrarrayfun.pdf
- NDpar package - an extension of these functions to N-dimensional arrays