Distributed concurrent optimizer job count
Enables distributed concurrent optimization, which can be used
to solve LP or MIP models on multiple machines.
A value of
n causes the
solver to create
n independent models, using different
parameter settings for each. Each of these models is sent to a
distributed worker for processing. Optimization terminates when the first
solve completes. Use the
ComputeServer parameter to
indicate the name of the cluster where you would like your distributed
concurrent job to run (or use
your client machine will act as manager and you just
need a pool of workers).
By default, Gurobi chooses the parameter settings used for each independent solve automatically. You can create concurrent environments to choose your own parameter settings (refer to the concurrent optimization section for details). The intent of concurrent MIP solving is to introduce additional diversity into the MIP search. By bringing the resources of multiple machines to bear on a single model, this approach can sometimes solve models much faster than a single machine.
The distributed concurrent solver produces a slightly different
log from the standard solver, and provides different callbacks as
well. Please refer to the
Distributed Algorithms section of the
Gurobi Remote Services Reference Manual
for additional details.
One important note about integer-valued parameters: while the maximum value that can be stored in a signed integer is , we use a MAXINT value of 2,000,000,000. Attempting to set an integer parameter to a value larger than this maximum will produce an error.
For examples of how to query or modify parameter values from our different APIs, refer to our Parameter Examples.