CPLEX Optimizer performance

IBM ILOG CPLEX Optimizer confirms its performance leadership. The Version 12.6.1 CPLEX Optimizers provide yet another breakthrough in performance.

Mixed integer programming models with linear terms are solved by the CPLEX MIP Optimizer on average 5% faster than the industry-leading prior release 12.6, on models requiring 1 second and above, and for the challenging group of models requiring at least 100 seconds the average speedup is 15%. For models with quadratic terms, improvements range from 30% to 65% for models requiring at least 1 second to solve.

Integer models are solved by the CPLEX CP Optimizer on average 25% faster than the prior 12.6 release.

More detail on these results can be found by clicking on the respective tabs.

CPLEX MIP performance evolution

CPLEX MILP performance evolution

CPLEX 12.6.3 vs.12.7.0: MILP performance improvement

CPLEX 12.6.3 vs.12.7.0: MIQCP performance improvement

CPLEX 12.6.3 vs.12.7.0: LP performance improvement

CPLEX 12.6.3 vs.12.7.0: MIQP performance improvement

CPLEX 12.6.3 vs.12.7.0: Global (MI)QP performance improvement

CPLEX CP Optimizer v. 12.6.1 performance benchmark details

Test-cases are separated into two groups:

Performance on integer problems was improved 25% in version 12.6.1 compared to version 12.6. Performance on scheduling problems was unchanged. The graph below shows cumulative performance improvements since 2010. Integer problem performance improved by a factor of 1.4 and scheduling problem performance improved by a factor of 2.6.

CP Optimizer performance improvements

Benchmark conditions

Benchmarks were conducted on 2.93 GHz Intel Xeon X570 processors with 2 x 4 cores, 8 Mbytes cache on each processor, and 36 Gbytes of RAM. The version of Linux used for results on 12.5.1 and earlier was 2.6.14 SMP x86_64 GNU/Linux with gcc 4.1 and for 12.6 was 2.6.32 SMP x86_64 GNU/Linux with gcc 4.4.

Default algorithmic settings were used, with a time limit of between 500 and 1000 seconds, depending on the problem. Each run uses 4 threads. Each problem is run with 10 different random seeds. The test set consisted of optimization models collected from public and private sources.

Results for CP Optimizer 12.3 and earlier use non-deterministic search; deterministic search is used in version 12.4 and up.

Performance is based on measurements and projections using standard IBM® benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.