Abstract
Many optimization problems arising in practice can be modeled as mixed integer programs (MIPs). In this paper, we present the new parallelization concept for the state-of-the-art MIP solver FICO Xpress-Optimizer. A natural precondition to achieving reasonabling speedups from parallelization is maintaining a high workload of the available computational resources. At the same time, reproducibility and reliability are key requirements for mathematical optimization software; solvers like the FICO Xpress-Optimizer are expected to be deterministic. The resulting synchronization latencies render the goal of a satisfying workload a challenge in itself.
We address this challenge by following a partial information approach and separating the concepts of simultaneous tasks and independent threads from each other. Our computational results indicate that this leads to a much higher CPU workload and thereby to an improved scaling on modern high-performance CPUs. As an added value, the solution path that the FICO Xpress-Optimizer takes is not only deterministic in a fixed environment, but, to a certain extent, thread-independent.
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Those were the instances rocII-4-11, ns1766074, aflow40b, bnatt350, csched010, danoint, dfn-gwin-UUM, gmu-35-40, iis-100-0-cov, m100n500k4r1, n3div36, neos-1337307, neos18, neos-849702, neos-916792, newdano, noswot, ns1830653, pg5_34, pigeon-10, ran16x16, reblock67, rmine6, sp98ic, timtab1, vpphard, bab5, glass4, iis-bupa-cov.
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Berthold, T., Farmer, J., Heinz, S., Perregaard, M. (2016). Parallelization of the FICO Xpress-Optimizer. In: Greuel, GM., Koch, T., Paule, P., Sommese, A. (eds) Mathematical Software – ICMS 2016. ICMS 2016. Lecture Notes in Computer Science(), vol 9725. Springer, Cham. https://doi.org/10.1007/978-3-319-42432-3_31
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DOI: https://doi.org/10.1007/978-3-319-42432-3_31
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