BUILT-IN MATHEMATICA SYMBOL

ParallelArray

generates in parallel a list of length n, with elements

, evaluated.

ParallelArray

generates in parallel an

array of nested lists, with elements

.

ParallelArray

generates in parallel a list using the index origins

(default

).

ParallelArray

uses head h, rather than List, for each level of the array.

MORE INFORMATION

ParallelArray is a parallel version of Array, which automatically distributes different evaluations of expr among different kernels and processors.

ParallelArray will give the same results as Array, except for side effects during the computation.

Parallelize[Array[f, n]] is equivalent to ParallelArray.

If an instance of ParallelArray cannot be parallelized it is evaluated using Array.

ParallelArray takes the same DistributedContexts option as ParallelTable.

EXAMPLES

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Basic Examples (3)

ParallelArray works like Array, but in parallel:

Generate a

array:

Generate a

array:

Use index origin

instead of

:

Start with indices

and

instead of

:

ParallelArray works like Array, but in parallel:

In[1]:=

Out[1]=

In[2]:=

Out[2]=

Generate a

array:

In[1]:=

Out[1]=

Generate a

array:

In[2]:=

Out[2]=

Use index origin

instead of

:

In[1]:=

Out[1]=

Start with indices

and

instead of

:

In[2]:=

Out[2]=

Options (11)

Break the computation into the smallest possible subunits:

Break the computation into as many pieces as there are available kernels:

Break the computation into at most 2 evaluations per kernel for the entire job:

Break the computation into evaluations of at most 5 elements each:

The default option setting balances evaluation size and number of evaluations:

Calculations with vastly differing runtimes should be parallelized as finely as possible:

A large number of simple calculations should be distributed into as few batches as possible:

By default, definitions in the current context are distributed automatically:

Do not distribute any definitions of functions:

Distribute definitions for all symbols in all contexts appearing in a parallel computation:

Distribute only definitions in the given contexts:

Restore the value of the DistributedContexts option to its default:

Possible Issues (2)

A function used that is not known on the parallel kernels may lead to sequential evaluation:

Define the function on all parallel kernels:

The function is now evaluated on the parallel kernels:

Definitions of functions in the current context are distributed automatically:

Definitions from contexts other than the default context are not distributed automatically:

Use DistributeDefinitions to distribute such definitions:

Alternatively, set the DistributedContexts option to include all contexts: