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Consider this code:
f[i_] := Sin[i]
Do[array1[i] = f[i], {i, 1, 5}]
Table[array1[i], {i, 1, 5}]
Returns:
{Sin[1], Sin[2], Sin[3], Sin[4], Sin[5]}
Now, I want to parallelize my code:
ParallelDo[array2[i] = f[i], {i, 1, 5}]
Table[array2[i], {i, 1, 5}]
Returns:
{array2[1], array2[2], array2[3], array2[4], array2[5]}
The parallelized version is unevaluated.
How can I resolve the issue? I used SetSharedFunction[f] but it did not work.
$Version
"9.0 for Microsoft Windows (32-bit) (January 24, 2013)"
It works well. Have you tried like this?
SetSharedFunction[array2]
f[i_] := Sin[i]
ParallelDo[array2[i] = f[i], {i, 1, 5}]
Table[array2[i], {i, 1, 5}]
{Sin[1], Sin[2], Sin[3], Sin[4], Sin[5]}
SetSharedFunctionfor the most expensive part of the code inDo, so have not only negated all benefits of parallelization, but made the code (considerably) slower.SetSharedFunctionforces the function to be evaluated on the main kernel, i.e. almost the same as a non-parallelDoexcept that in this case instead of evaluating something directly on the main kernel, the main kernel is asking the subkernel to ask the main kernel to evaluate a piece of code. This introduces considerable overhead. $\endgroup$ – Szabolcs Nov 5 '14 at 17:27DowithParallelDo, but think about what the subkernels would need to do precisely (and how they need to communicate) to achieve your goal, i.e. design it as a parallel algorithm from the start. $\endgroup$ – Szabolcs Nov 5 '14 at 18:39