BUILT-IN MATHEMATICA SYMBOL

ParallelCombine

evaluates

in parallel by distributing parts of the computation to all parallel kernels and combining the partial results with comb.

ParallelCombine

is equivalent to ParallelCombine

if h has attribute Flat, and ParallelCombine[f, h[e₁, e₂, ...], Join] otherwise.

MORE INFORMATION

ParallelCombine forms expressions , , ..., , evaluates these on all available kernels, and combines the results with .

The default combiner Join is appropriate for functions f such that the result of has head h. This includes all functions with attribute Listable.

For heads h with attribute Flat the default combiner h effectively implements the associative law .

With a compatible choice of comb, ParallelCombine is equivalent to .

If no kernels are available, ParallelCombine evaluates normally.

ParallelCombine takes the same Method option as Parallelize.

ParallelCombine takes the same DistributedContexts option as Parallelize; the default value is DistributedContexts:>$DistributedContexts.

EXAMPLES

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Basic Examples (2)

Apply f in parallel to chunks of a list (with 4 parallel kernels available):

Show where each computation takes place:

By default Join is used as a combiner function:

Do a parallel filtering operation:

Apply f in parallel to chunks of a list (with 4 parallel kernels available):

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Show where each computation takes place:

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By default Join is used as a combiner function:

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Do a parallel filtering operation:

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Scope (9)

All Listable functions can be parallelized with ParallelCombine:

If the function is not Listable use an explicit Map:

Many functional programming constructs can be parallelized with ParallelCombine:

The result need not have the same length as the input:

Evaluate the elements of a list in parallel:

The overall count of matching elements is equal to the sum of the partial counts:

An element appears in a list if it appears in at least one of the pieces:

An element does not appear on a list if it appears in none of the pieces:

Each subkernel performs a smaller number of additions and the combiner combines the results:

Automatically pick the combiner for Flat functions:

Typical Flat functions:

Generalizations & Extensions (1)

Listable functions of several arguments:

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:

Visualize the number of evaluations per kernel and items per evaluation:

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:

Applications (3)

Reduce an associative expression in parallel:

Find out how a computation is distributed among all kernels:

A parallel version of MapThread:

Properties & Relations (5)

An implementation of ParallelMap:

For listable functions ParallelCombine and ParallelMap are equivalent:

Parallelize is often implemented in terms of ParallelCombine:

Parallel versions of many data-parallel commands can easily be written with ParallelCombine:

Functions defined interactively are automatically distributed to all kernels when needed:

Distribute definitions manually and disable automatic distribution:

The function is now evaluated on the parallel kernels:

Possible Issues (2)

The combiner must be compatible with the type of the partial results:

The default combiner is Join, which is appropriate for list-like results:

Functions may simplify short argument lists, but not longer ones:

Such simplification of partial expressions may make parallel evaluation impossible:

Prevent simplification of partial expressions and apply the desired function only at the end: