Obtain each parallel kernel's unique ID number:

Obtain each parallel kernel's system process ID:

Use ParallelEvaluate to perform initializations on all parallel kernels:

Make definitions of local functions on all parallel kernels:

Obtain local properties of all parallel kernels:

Obtain a local property from a single kernel:

Access data from sources local to the parallel kernels:

Save results computed on the parallel kernels locally in unique files:

Verify that the files have been written:

Prevent local reevaluation of results returned from subkernels:

Run an evaluation on only one kernel per machine:

Run an evaluation only on all local kernels:

Run an evaluation only on all LightweightGrid kernels:

Specify the kernels to query by a kernel object or a kernel ID:

Query several kernels:

All can be used to specify all available kernels:

All is equivalent to Kernels[]:

Tabulate properties of all parallel kernels:

Parallelize a Monte Carlo simulation by running the same code on all parallel kernels:

Combine the individual averages for a more accurate overall result:

Store large intermediate results locally on each parallel kernel:

Work with data stored locally:

Check whether all of these polynomials are irreducible:

Shared variables used for synchronization:

Run one search loop on each kernel until the computation is aborted:

ParallelEvaluate performs the same evaluation on each subkernel:

Parallelize distributes parts of an evaluation to each subkernel:

Deterministic calculations give the same result on each parallel kernel:

Calculations involving randomness give independent results on each parallel kernel:

Force the same result by setting SeedRandom:

Each parallel kernel has a distinct ID, which can be used to make expressions unique:

DistributeDefinitions uses ParallelEvaluate to transport definitions to all kernels:

An explicit ParallelEvaluate does the same:

Distributed definitions are remembered for new kernels:

The effects of ParallelEvaluate are not remembered:

ParallelNeeds uses ParallelEvaluate to run Needs on all parallel kernels:

Additionally all uses are remembered, so that new kernels also load needed packages:

ParallelEvaluate automatically distributes definitions of functions used:

Side effects cannot be used between different parallel kernels:

Use a shared variable to support side effects:

Suppress the automatic distribution of definitions when making definitions with ParallelEvaluate:

Find such that is prime, until the computation is manually aborted:

Given multiple running kernels on many different machines:

Load a resource function into the cache on each machine only once:

Use the resource function in all kernels: