interesting use cases for the cplex remote object

Decision Optimization

Interesting Use Cases forthe CPLEX Remote Object

Laszlo Ladanyi & Daniel Junglas

© 2013 IBM Corporation


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History

CPLEX Remote Object– Introduced with 12.5.0 in Fall 2012

• 3 transports: tcpip, MPI, process (ssh)– Example codes:

• Parmipopt:- Start two workers one working on primal and one working on dual bounds - Stop when bounds match

• Parbenders- master solves sub-problems within lazy constraint callback- sub-problems solved on remote machines using remote

CPLEX objects

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Fast forward to 12.6.0: DistMIP

DistMIP: A distributed memory MIP solver– Built using the CPLEX remote object– “Master” distributes tasks to multiple “workers” and controls them– Communication to workers done transparently via CPLEX Remote Object function– Algorithm runs deterministically

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DistMIP

Phases of the algorithm– Presolve problem

• Copy presolved modelto workers

– Racing Rampup• Until winner is chosen then

terminate other rampup runs– Distributed tree search

• Distribute nodes from treeof the winner across workers

• Process distributed tree search

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Presolve

Rampup

Work

er 1

Work

er 2

Work

er N

Tree Search

Work

er 1

Work

er 2

Work

er N



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DistMIP: Racing Rampup

Task– Run concurrent MIP optimizer until– “Good” run has produced enough nodes to distribute among workers

Each worker gets same problem– Setup different parameter settings

• Different random seeds– Candidate winner (currently): worker with best dual;

Stop when candidate winner has enough nodes– Exchange primal and dual bounds between workers

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Racing Rampup

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Master

Mainproblem

Worker N

Worker 1

rootpresolve



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Racing Rampup

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Master

Mainproblem

Worker N

Worker 1

rootpresolve

root

root

Copy problemStart limited MIP optimization



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Racing Rampup

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Master

Worker N

Worker 1

root

root

root

Master querries workers:• primal bound• dual bound• number of nodes

Primal bound

Dual bound 1

Dual bound N



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Racing Rampup

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Master

Worker N

Worker 1

root

root

root

Master• Check for termination• Spread best primal bound• Other workers may prune

nodes from their tree

Primal bound

Primal bound



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Racing Rampup

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Master

Worker N

Worker 1

root

root

root

Master querries worker• Determines winner tobe worker N

Primal bound

Dual bound 1

Dual bound N



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Racing Rampup

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Master

Worker N

Worker 1

root

root

root

Master querries worker• Determines winner tobe worker N

• Terminates other workers

kill



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DistMIP: Racing Rampup

Termination– Regularly (after deterministic time interval) workers report dual bound and open-

node count to master– Master interrupts rampup if worker with best dual bound has enough nodes for

starting tree search– Otherwise workers continue

Infinite Rampup– Run until problem solved or time limit

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DistMIP: Distributed Tree Search

Master collects and redistributes nodes from winner to workers

Workers explore nodes independently as individual MIPs– Master distributes information between workers

• New incumbents• Root bounds

Master rebalances nodes from time to time

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Racing Rampup

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Master

Worker N

Worker 1

root

root

Master queries winner• Grab open nodes• Distribute nodes toworker queues

Grab nodes



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Racing Rampup

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Master

Worker N

Worker 1

root

Master• Setup node from queue ineach worker

• Start MIP optimizer todeterministic time limit

• Winner continues its tree

Setup node

root



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DistMIP: Distributed Tree Search

Steady state– Master has queue of nodes assigned to each worker– Worker gets single node to work on

In a loop– Each worker runs for limited deterministic time up to synchronization point– At synchronization point

• Exchange dual bound, tree size, solutions,...• Possibly master grabs nodes from workers• Possibly master reassigns nodes to workers

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DistMIP: Performance

Infinite Rampup– General purpose applicability to harder problems

• Exploit / reduce performance variability• Limited scalability (4-8 machines)

– Average speedup on our testset• >100s: 1.4x• >1000s: 1.7x

DistMIP– Special purpose solver for problems that require dealing with combinatorial growth

of tree size

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DistMIP: Technical details

Master– Responsible of enforcing limits

Rampup Workers– Combining presolve reductions from workers only allows one worker to carry out

dual reductions

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Ease of Use

Setup a Virtual Machine Configuration file config.vmc– Read in config file

• CPLEX> read config.vmc• CPXXreadcopyvmconfig (env, “config.vmc”)• CPXXreadvmcconfig (env, “...”);

– Call distmip optimizer• CPLEX> optimize• CPXXdistmipopt(env, lp);

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interesting use cases for the cplex remote object

Software

worker worker

master worker n worker

workers workers

root root root master

worker queues

master grabs nodes

root master setup node

root root copy problem