This is documentation for Mathematica 8, which was
based on an earlier version of the Wolfram Language.

# ParallelEvaluate

 ParallelEvaluate[expr]evaluates the expression expr on all available parallel kernels and returns the list of results obtained. ParallelEvaluate evaluates expr on the parallel kernel specified. ParallelEvaluateevaluates expr on the parallel kernels .
Obtain each parallel kernel's unique ID number:
Obtain each parallel kernel's system process ID:
Obtain each parallel kernel's unique ID number:
 Out[1]=
Obtain each parallel kernel's system process ID:
 Out[2]=
 Scope   (5)
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:
Specify the kernels to query by a kernel object or a kernel ID:
Query several kernels:
 Applications   (4)
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 does not automatically distribute definitions of functions used:
Use DistributeDefinitions to distribute definitions to all kernels:
Higher-level functions automatically distribute definitions:
Side effects cannot be used between different parallel kernels:
Use a shared variable to support side effects:
Find such that is prime, until the computation is manually aborted:
New in 7