ParallelTable works like Table, but in parallel:

A table of the first 10 squares:

A table with i running from 0 to 20 in steps of 2:

Make a 4×3 matrix:

Plot a table:

Longer computations display information about their progress and estimated time to completion:

The index in the table can run backward:

Make a triangular array:

Make a 3x2x4 array, or tensor:

Iterate over an existing list:

Make an array from existing lists:

The table index can have symbolic values:

Solve and plot a differential equation for many initial conditions and animate the results:

Explore different parameter values for the sine-Gordon equation in two spatial dimensions:

Apply different algorithms to the same set of data:

Apply a list of different filters to the same image and display the result:

Or apply a list of effects:

Generate 10 frames from an animation and save them to individual files:

Run several batches in parallel:

Each run returns one frame which can be used for checking the correctness:

Remove the generated files:

Quickly show the evaluation of several nontrivial cellular automata:

Parallelization happens along the outermost (first) index:

Using multiple iteration specifications is equivalent to nesting Table functions:

ParallelDo evaluates the same sequence of expressions as ParallelTable:

ParallelSum effectively applies Plus to results from ParallelTable:

ParallelArray iterates over successive integers:

Map applies a function to successive elements in a list:

Table can substitute successive elements in a list into an expression:

ParallelTable iterating over a given list is equivalent to ParallelCombine:

ParallelTable can be implemented with WaitAll and ParallelSubmit:

Parallelization at the innermost level of a multidimensional table:

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

Distribute definitions manually and disable automatic distribution:

For functions from a package, use ParallelNeeds rather than DistributeDefinitions:

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:

Trivial operations may take longer when parallelized: