#include <ilcplex/ilocplex.h>
void load_solve(const std::string& modelName) {
IloEnv env;
IloCplex cplex(env);
IloModel model(env);
cplex.importModel(model, modelName.c_str());
cplex.extract(model);
cplex.solve();
env.out() << "Solution value = "
<< cplex.getObjValue()
<< std::endl;
env.end();
}
int main() {
load_solve("noswot.mps");
return 0;
}
When that is all you need, you're good. But some challenging problems demand specific algorithms that CPLEX doesn't provide (yet!) and/or would benefit from the power of multiple machines... An example could be that you need to use a Benders decomposition for your very large problem, or you came up with a problem-specific algorithm that uses big LPs or MIPs as sub-problems. CPLEX includes APIs in multiple languages (currently : C, C++, Java, Python, C# and VB), and using any of these solves the first part of the issue. But what about the 'using multiple computers' part? You probably don't want to invest much time into writing a framework to enable distributed computing. So we did it for you!
CPLEX Optimizers 12.5.1, the Mathematical Programming engine in the latest version of IBM CPLEX Optimization Studio, features what we called the CPLEX Remote Object. With only additional parameters given to the first CPLEX call, you turn this CPLEX object into a 'remote' object that does its computations on another machine.
This feature was introduced in version 12.5 for the C API, and we just added the C++ and Java APIs. Let's see how to use this with the example above:
#include <ilcplex/ilocplex.h>
void load_solve(const std::string& modelName) {
IloEnv env;
const char* remote = "-address=the_server:12345";
IloCplex cplex(env, "tcpiptransport", 1, &remote);
IloModel model(env);
cplex.importModel(model, modelName.c_str());
cplex.extract(model);
cplex.solve();
env.out() << "Solution value = "
<< cplex.getObjValue()
<< std::endl;
env.end();
}
int main() {
load_solve("noswot.mps");
return 0;
}
Note that the only difference with a purely 'local' computation is in the creation of the CPLEX object. In the case above, your program will try to connect to a distant machine and run CPLEX there. The data will still be read from the same (local) file, but they will be serialized and sent to the remote object. The latter will execute the 'solve' call, and when instructed to with the last call, will return you the objective value of the optimal solution.
This needs some preparation work on the distant machine, of course, and this depends on the protocol you want to use:
- TCP/IP : fire up the CPLEX interactive with some options to listen on a specific port;
- Process : the distant computer must accept SSH sessions from which the CPLEX interactive is in the path;
- MPI : in that case, both machines (local and distant) must belong to the MPI cluster.
In addition to offloading your computations to a distant machine, the CPLEX Remote Object allows you to create fully distributed algorithms, where a 'master' connects to several 'workers' and gives different computations to each. The CPLEX distribution includes two such examples: a Benders decomposition, and a distributed concurrent MIP solver... You will find more information about the Remote Object in Roland Wunderling's presentation. And you can browse the online documentation on the topic.
By the way, this new 12.5.1 version has a number of features, including a 43% performance improvement in the time to solve difficult MIP problems... See Jean-François Puget's blog post for more on this topic.
In addition to offloading your computations to a distant machine, the CPLEX Remote Object allows you to create fully distributed algorithms, where a 'master' connects to several 'workers' and gives different computations to each. The CPLEX distribution includes two such examples: a Benders decomposition, and a distributed concurrent MIP solver... You will find more information about the Remote Object in Roland Wunderling's presentation. And you can browse the online documentation on the topic.
By the way, this new 12.5.1 version has a number of features, including a 43% performance improvement in the time to solve difficult MIP problems... See Jean-François Puget's blog post for more on this topic.
