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The growing complexity of Cyber-Physical Systems (CPS), together withincreasingly available parallelism provided by multi-core chips, fosters theparallelization of simulation. Simulation speed-ups are expected fromco-simulation and parallelization based on model splitting into weak-coupledsub-models, as for instance in the framework of Functional Mockup Interface(FMI). However, slackened synchronization between sub-models and theirassociated solvers running in parallel introduces integration errors, whichmust be kept inside acceptable bounds.  CHOPtrey denotes a forecasting framework enhancing the performance of complexsystem co-simulation, with a trivalent articulation. First, we consider theframework of a Computationally Hasty Online Prediction system (CHOPred). Itallows to improve the trade-off between integration speed-ups, needing largecommunication steps, and simulation precision, needing frequent updates formodel inputs. Second, smoothed adaptive forward prediction improvesco-simulation accuracy. It is obtained by past-weighted extrapolation based onCausal Hopping Oblivious Polynomials (CHOPoly). And third, signal behavior issegmented to handle the discontinuities of the exchanged signals: thesegmentation is performed in a Contextual \& Hierarchical Ontology of Patterns(CHOPatt).  Implementation strategies and simulation results demonstrate the frameworkability to adaptively relax data communication constraints beyondsynchronization points which sensibly accelerate simulation. The CHOPtreyframework extends the range of applications of standard Lagrange-type methods,often deemed unstable. The embedding of predictions in lag-dependent smoothingand discontinuity handling demonstrates its practical efficiency.

CHOPtrey: contextual online polynomial extrapolation for enhanced multi-core co-simulation of complex systems