Introducing parallel MIP optimization

Describes parallel MIP optimization in general terms.

By default, CPLEX uses the deterministic parallel MIP optimizer to solve a mixed integer programming problem. In doing so, it exploits parallel computations while it solves nodes of the MIP branch & cut tree. It also executes strong branching computations in parallel. Furthermore, CPLEX also exploits parallelism while solving the root node.

As explained in Determinism of results, you may find it advantageous to develop your application in deterministic parallel mode, where you can rely on the invariance and repeatability of the search path and results to evaluate the correctness of your model and solutions. After you are convinced of correctness of your model, there are two different approaches you can take in deployment of your application. If performance is critical, consider deploying in opportunistic parallel mode. While faster performance in opportunistic mode cannot be guaranteed, it does generally out-perform deterministic mode. On the other hand, if performance is not critical, you may prefer to deploy in deterministic mode to retain the possibility of reproducing any problems that your end-user may encounter during deployment. In summary, you need to evaluate for your model and application which mode is more appropriate.