ConcurrentJobs

ConcurrentJobs

Distributed concurrent optimizer job count

	Type:	int
	Default value:	0
	Minimum value:	0
	Maximum value:	MAXINT

Enables distributed concurrent optimization, which can be used to solve LP or MIP models on multiple machines. A value ofncauses the solver to createnindependent 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 theWorkerPool参数提供一个分布式我们rker cluster.

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 theconcurrent optimizationsection 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 theDistributed Algorithmssection of theGurobi Remote Services Reference Manualfor additional details.

For examples of how to query or modify parameter values from our different APIs, refer to ourParameter Examples.