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

TL;DR

CHOPtrey is a forecasting framework that enhances multi-core co-simulation of complex systems by adaptively balancing speed and accuracy through polynomial extrapolation, discontinuity segmentation, and signal behavior modeling.

Contribution

It introduces a novel combination of polynomial extrapolation, adaptive smoothing, and pattern-based segmentation to improve co-simulation performance and accuracy.

Findings

Significant acceleration of co-simulation beyond synchronization points

Improved accuracy through smoothed adaptive predictions

Extended applicability of Lagrange-type methods in co-simulation

Abstract

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