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solver:distributed_parallel_algorithms_with_gams_gurobi

Distributed Parallel Algorithms with GAMS/Gurobi

Starting with Gurobi 6.0.2 (GAMS 24.4.2), GAMS/Gurobi can utilize the distributed parallel algorithms offered by Gurobi:

  • A distributed MIP solver, which allows you to divide the work of solving a single MIP model among multiple machines. A manager machine passes problem data to a set of worker machines in order to coordinate the overall solution process.
  • A distributed concurrent solver, which allows you to use multiple machines to solve an LP or MIP model. Unlike the distributed MIP solver, the concurrent solver doesn't divide the work associated with solving the problem among the machines. Instead, each machine uses a different strategy to solve the whole problem, with the hope that one strategy will be particularly effective and will finish much earlier than the others. For some problems, this concurrent approach can be more effective than attempting to divide up the work.
  • Distributed parameter tuning, which automatically searches for parameter settings that improve performance on your optimization model. Tuning solves your model with a variety of parameter settings, measuring the performance obtained by each set, and then uses the results to identify the settings that produce the best overall performance. The distributed version of tuning performs these trials on multiple machines, which makes the overall tuning process run much faster.

These distributed algorithms are designed to be nearly transparent to the user. The user simply modifies a few parameters, and the work of distributing the computation among multiple machines is handled behind the scenes by the Gurobi library.

Before your program can perform a distributed optimization task, you'll need to identify a set of machines to use as your distributed workers. Ideally these machines should give very similar performance. Identical performance is best, especially for distributed tuning, but small variations in performance won't hurt your overall results too much. Once you've identified your distributed worker machines, you'll need to start Gurobi Remote Services on these machines. The setup is requires some admin knowledge and subject to frequent change, so we refer to the Gurobi web site: http://www.gurobi.com/documentation/, see “Remote Services”. You will need some extra software to run the Gurobi Remote Services. Contact support.gams.com to get access to this software.

Once the server side is set up, the client GAMS/Gurobi will need to know how to reach your workers. You'll use the WorkerPool parameter to tell GAMS/Gurobi how to access the pool of workers. In order to use the distributed MIP solver you specify option DistributedMIPJobs, for distributed concurrent solver you use option ConcurrentJobs, and for distributed tuning you use option TuneJobs. Details can be found in the GAMS/Gurobi solver manual.

Here is a log of a successful distributed MIP run with three workers:

Gurobi           24.4.2 r51223 Released Mar 14, 2015 VS8 x86 32bit/MS Windows

Gurobi full + distributed license.
Gurobi library version 6.0.2
Reading parameter(s) from "C:\tmp\gurobi.opt"
>>  workerpool  192.168.178.86,192.168.178.32,192.168.178.37
>>  distributedmipjobs 3
Finished reading from "C:\tmp\gurobi.opt"
Starting Gurobi...
Optimize a model with 126 rows, 127 columns and 465 nonzeros
Coefficient statistics:
  Matrix range    [1e+00, 2e+01]
  Objective range [1e+00, 1e+00]
  Bounds range    [1e+00, 2e+01]
  RHS range       [1e+00, 2e+01]
Started distributed worker on 192.168.178.86
Started distributed worker on 192.168.178.32
Started distributed worker on 192.168.178.37

Distributed MIP job count: 3
Job count limited by machine availability

    Nodes    |    Utilization     |     Objective Bounds      |     Work
 Expl Unexpl |  Active Sync Comm  | Incumbent    BestBd   Gap | It/Node Time

*    0     -                      -0.0000000          -      -     -   21s
     0     0      -    -    -              -          -      -     -   21s
*    0     -                      16.0000000          -      -     -   21s
*    0     -                      17.0000000          -      -     -   21s
*    0     -                      18.0000000          -      -     -   21s
*    0     -                      19.0000000          -      -     -   21s
*    0     -                      21.0000000          -      -     -   21s

Explored 140 nodes (2495 simplex iterations) in 21.39 seconds
Distributed MIP job count: 3

Time limit reached
Best objective 2.100000000000e+01, best bound 2.600000000000e+01, gap 23.8095%
MIP status(9): Optimization terminated due to time limit.

Solving fixed MIP.
Optimize a model with 126 rows, 127 columns and 465 nonzeros
Coefficient statistics:
  Matrix range    [1e+00, 2e+01]
  Objective range [1e+00, 1e+00]
  Bounds range    [1e+00, 2e+01]
  RHS range       [1e+00, 2e+01]
Presolve removed 126 rows and 127 columns
Presolve time: 0.00s
Presolve: All rows and columns removed
Iteration    Objective       Primal Inf.    Dual Inf.      Time
       0    2.1000000e+01   0.000000e+00   0.000000e+00      0s

Solved in 0 iterations and 0.01 seconds
Optimal objective  2.100000000e+01
Fixed MIP status(2): Model was solved to optimality (subject to tolerances).

MIP   Solution:         21.000000    (2495 iterations, 140 nodes)
Final Solve:            21.000000    (0 iterations)

Best possible:          26.000000
Absolute gap:            5.000000
Relative gap:            0.192308
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