Difference between revisions of "Parallel package"

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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.
 
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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[[Category:Octave-Forge]]

Revision as of 10:26, 21 September 2014

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)

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

calculation on a single array

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.