Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing
Abstract: The Production and Distributed Analysis (PanDA) system, originally developed for the ATLAS experiment at the CERN Large Hadron Collider (LHC), has evolved into a robust platform for orchestrating large-scale workflows across distributed computing resources. Coupled with its intelligent Distributed Dispatch and Scheduling (iDDS) component, PanDA supports AI/ML-driven workflows through a scalable and flexible workflow engine.
This article presents an AI-assisted framework for detector design optimization that integrates multi-objective Bayesian optimization with the PanDA-iDDS workflow engine to coordinate iterative simulations across heterogeneous resources. The framework addresses the challenge of exploring high-dimensional parameter spaces inherent in modern detector design.
Introduction
As scientific experiments become increasingly complex, optimizing detector design has emerged as a critical challenge. The ability to efficiently explore vast parameter spaces is essential for achieving optimal performance. The integration of artificial intelligence (AI) and machine learning (ML) into workflow management systems offers a promising approach to enhancing the efficiency and scalability of this optimization process.
Framework Overview
The proposed framework leverages the PanDA system, which has been instrumental in managing large-scale workflows in high-energy physics. The integration of iDDS allows for intelligent scheduling and dispatching of computational tasks, ensuring that resources are utilized effectively. Key features of the framework include:
- Multi-Objective Bayesian Optimization: This technique enables the simultaneous optimization of multiple objectives, which is crucial for modern detector design.
- Heterogeneous Resource Coordination: The framework efficiently manages simulations across various computing resources, enhancing scalability.
- Iterative Simulations: By coordinating iterative simulations, the framework facilitates continuous improvement in detector design through rapid feedback loops.
Benchmark Studies
To validate the effectiveness of the proposed framework, benchmark problems and realistic studies of the ePIC and dRICH detectors for the Electron-Ion Collider (EIC) were conducted. The results demonstrated significant improvements in:
- Automation: The framework automates the optimization process, reducing the need for manual intervention and increasing productivity.
- Scalability: The ability to scale across distributed resources allows for handling larger, more complex optimization problems.
- Efficiency: Enhanced efficiency in multi-objective optimization was observed, leading to quicker convergence to optimal solutions.
Conclusion
This work establishes a flexible and extensible paradigm for AI-driven detector design and other computationally intensive scientific applications. The integration of PanDA and iDDS with advanced optimization techniques marks a significant advancement in workflow management for scientific research, paving the way for future innovations in detector design and beyond.
In summary, the AI-assisted framework not only addresses the challenges of modern detector design but also sets a precedent for the application of AI in various scientific domains where large-scale computational workflows are essential.