Unraveling Parallelism in Automated Workload Modeling for Distributed Cyber-Physical Systems

Designing next generation distributed CyberPhysical Systems (dCPS) requires effective Design Space Exploration (DSE) methods to evaluate system design alternatives and their impact on performance. While existing DSE approaches focus on hardware optimization and software-to-hardware mapping, they often overlook parallel execution opportunities within software tasks. Current application workload models for complex dCPS assume fixed execution orders, limiting the ability to explore and exploit software parallelism. To address this issue, we propose refined workload models derived from execution traces that capture both inter- and intra-process dependencies. Building on these models, we present a method to identify tasks that can be safely reordered or executed in parallel without modifying the existing software implementation. We validate our approach through a case study on the ASML Twinscan lithography machine, demonstrating measurable performance improvements without impacting the system functional correctness.