Gurobi Distributed Optimization
Get Gurobi, Your Way
Maximize performance by distributing your computations across multiple machines.
Gurobi Distributed Optimization
Get Gurobi, Your Way
Maximize performance by distributing your computations across multiple machines.
Gurobi Distributed Optimization
Get Gurobi, Your Way
Maximize performance by distributing your computations across multiple machines.
Solve Even Faster
Gurobi Distributed Optimization lets you leverage multiple machines to reduce solve times. Some optimization models solve 15 times faster with 32 machines, and speed-ups of 2-3x are common with eight machines. Gurobi offers three distributed algorithms:
Distributed MIP — where multiple machines work together to solve a single MIP model
Distributed concurrent — where multiple machine use different algorithmic strategies in a race to solve an LP or MIP model
Distributed tuning — where multiple machines do experimental solves to find parameter settings that improve performance
All three distributed algorithms are easy to use. Simply install Gurobi Remote Services on the set of machines that you wish to use as distributed workers, then use the same Gurobi commands that you would use if you were going to perform optimization or tuning on just a single machine. A Gurobi parameter allows you to specify the names of the worker machines that are available for running distributed algorithms, and another parameter indicates how many jobs to start on these workers.
The Gurobi Optimizer handles all the work of dividing the computation among the machines. The speed-up achieved by distributed optimization varies depending on the model.
Solve Even Faster
Gurobi Distributed Optimization lets you leverage multiple machines to reduce solve times. Some optimization models solve 15 times faster with 32 machines, and speed-ups of 2-3x are common with eight machines. Gurobi offers three distributed algorithms:
Distributed MIP — where multiple machines work together to solve a single MIP model
Distributed concurrent — where multiple machine use different algorithmic strategies in a race to solve an LP or MIP model
Distributed tuning — where multiple machines do experimental solves to find parameter settings that improve performance
All three distributed algorithms are easy to use. Simply install Gurobi Remote Services on the set of machines that you wish to use as distributed workers, then use the same Gurobi commands that you would use if you were going to perform optimization or tuning on just a single machine. A Gurobi parameter allows you to specify the names of the worker machines that are available for running distributed algorithms, and another parameter indicates how many jobs to start on these workers.
The Gurobi Optimizer handles all the work of dividing the computation among the machines. The speed-up achieved by distributed optimization varies depending on the model.
How it Works
How it Works
How it Works
Know before you Buy
Do you want to know if your models can benefit from distributed optimization? Simply send us your models as MPS or LP files. We will run them on a cluster of machines and tell you if:
Distributed MIP solves your models faster
Distributed concurrent decreases variations in solve times
Distributed tuning finds parameter settings that speed up single machine solve
Know before you Buy
Do you want to know if your models can benefit from distributed optimization? Simply send us your models as MPS or LP files. We will run them on a cluster of machines and tell you if:
Distributed MIP solves your models faster
Distributed concurrent decreases variations in solve times
Distributed tuning finds parameter settings that speed up single machine solve
Know before you Buy
Do you want to know if your models can benefit from distributed optimization? Simply send us your models as MPS or LP files. We will run them on a cluster of machines and tell you if:
Distributed MIP solves your models faster
Distributed concurrent decreases variations in solve times
Distributed tuning finds parameter settings that speed up single machine solve
