Model Optimization for A Dynamic Rail Transport System on an Asymmetric Multi-Core System

TL;DR

This paper presents an optimized real-time dynamic rail transport model that leverages parallel processing and microarchitecture-aware scheduling to improve simulation efficiency and support practical locomotive design and modernization.

Contribution

It introduces a novel parallel scheduling algorithm for dynamic rail system simulation on asymmetric multi-core systems, enhancing computational efficiency and real-time performance.

Findings

Confirmed operability of the optimized model

Reduced processor time in data processing

Maintained simulation accuracy with cyclic schedules

Abstract

The problem of optimization of the rolling dynamics model is considered. That providing safe movement at high frequency when interacting with the railway. Moreover, allowing to evaluate the dynamic parameters when designing new and modernizing existing locomotives. The object of this research is a rail transport dynamic system model. The article's purpose is to increase the efficiency of the digital hardware in the rolling stock loop model by optimizing the organization of the computing process. The mathematical model analysis of the object made it possible to attribute it to the class of hard real-time systems. The computation of the model phase variables with different frequencies is necessary to optimize the simulation time of the train movements and is performed by splitting the original algorithm into parallel threads. The developed planning algorithm and the cyclic schedule implementation for the model of a dynamic real-time object consider microarchitecture solutions of symmetric multiprocessor systems with shared memory and methods for optimizing software tools. The experiments confirmed the operability of the optimized model. Also, allow us to recommend it for practical use in studying objects and determine the dynamic force of trolley structural elements during operation. Analysis of the optimized model simulation results, using cyclic schedules shows the correspondence of the obtained simulation results to the standard. The main advantage of the model is the increase in productivity when performing data processing by reducing the processor time. The optimized cyclic schedule algorithm of the semi-natural modeling platform is used for the subsequent development of the control system in real and accelerated time scales.