Speakers
Description
The ePIC Physics and Detector Simulations leverage the Geant 4 and DD4hep software frameworks, which serves as a single source of truth for detector description, ensuring consistent configuration across full (Geant 4/DDG4) and accelerated simulation models. As simulation complexity scales, we employed a systematic profiling methodology using the DD4hep plugin mechanism to pinpoint computational bottlenecks. This analysis definitively showed that optical photon propagation in Cherenkov detectors to identify particles and electromagnetic shower physics consume the largest fraction of compute time, thus defining our acceleration R&D priorities.
We have achieved significant advancements in GPU-based acceleration for optical photon transport. The EIC-Opticks framework, utilizing the NVIDIA OptiX Ray Tracing Engine, demonstrated an order-of-magnitude speedup over multi-threaded Geant 4 for a simplified Cherenkov detector geometry, validating a highly promising technique for low-to-moderate photon yield detectors. To ensure comprehensive performance evaluation, we established a parallel effort for comparative studies with other GPU transport solutions like the Celeritas project, implementing a Celeritas-DD4hep integration plugin via the G4TrackingManager.
Finally, we are exploring the integration of AI/ML surrogate models to accelerate detector simulations. We have already developed a framework-agnostic ML training and inference system for reconstruction tasks that provides the foundation for deploying new models. The complex particle showers within calorimeters are ideal candidates for FastCaloSim-inspired ML surrogate models. We are now investigating the ddFastSim DD4hep-native framework as a concrete path to integrate and validate these fast simulation models, making them accessible to all ePIC calorimeters within the existing DD4hep framework.