A Roadmap for HEP Software and Computing R&D for the 2020s (1712.06982v5)

Abstract: Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.

• The paper presents a comprehensive R&D roadmap to advance software and computing for HL-LHC experiments.
• It details the enhancement of simulation, event reconstruction, and data analysis techniques to manage a 30-fold increase in data volume.
• The strategy leverages heterogeneous computing and machine learning to ensure sustainable, efficient, and scalable operations.

Software and Computing R&D for Particle Physics in the 2020s: A Focus on HL-LHC

The white paper under analysis outlines the expansive research and development (R&D) strategy required to support the High-Luminosity Large Hadron Collider (HL-LHC) and other high-energy physics (HEP) experiments in the 2020s. The document, produced by the HEP Software Foundation (HSF), evaluates the evolving software and computing demands that these physics experiments entail, emphasizing the necessity for software advancements that complement planned hardware upgrades.

Strategic Goals and Challenges

The paper identifies several broad scientific goals for particle physics, focusing principally on extending our understanding of the Higgs boson, exploring beyond the Standard Model (BSM) physics, probing neutrino properties, and detecting potential signatures of dark matter. The HL-LHC is expected to collect 30 times more data than its predecessor, requiring software advances to process, store, and analyze petabyte and ultimately exabyte-scale datasets efficiently.

Computational Challenges:

• Data Volume: The anticipated data increase demands significant enhancements in data management and processing efficiencies.
• Heterogeneous Computing: Diverse computing architectures, including GPUs and possibly FPGAs, necessitate adaptability and innovation in the software that manages these resources.
• Sustainable Software: Long-term sustainability through enhanced efficiency and scalability is vital.

Key R&D Areas

The white paper highlights several critical R&D topics where sustained effort is required:

1. Event Generators and Detector Simulation: Improvements in Monte Carlo event generators, particularly in increasing their practical computational efficiency, are essential. Furthermore, detector simulations need optimization for both electromagnetic and hadronic interactions.
2. Data Analysis and Machine Learning: The community's growing engagement with machine learning methods, facilitated by open-source tools and libraries, signals a shift towards more innovative data analysis techniques that can push the boundaries of discovery.
3. Trigger and Event Reconstruction: Next-generation experiments will rely significantly on real-time and offline event reconstruction. The evolution of multithreaded and parallel processing models will play a crucial role here.
4. Facilities and Distributed Computing: Robust data processing frameworks are necessary to handle the new scale of computation. This includes facilitating data flow across distributed systems, making efficient use of networking advances such as software-defined networking (SDN).
5. Security and Conditions Data Management: Security challenges are compounded by the heterogeneous and distributed nature of HEP computing. Effective conditions data management systems need to be resilient and efficient under high-access rates during HL-LHC operations.
6. Software Development and Preservation: R&D in software development practices, validation, and long-term data and software preservation is vital for the success of scientific communication and reproducibility.

Implications and Future Directions

The developments envisaged in the white paper are poised to make significant impacts in multiple domains. Practical improvements in detector simulation and event reconstruction can lead to more precise measurements, advancing HEP's understanding of fundamental physics. Moreover, optimization of data handling and processing frameworks is essential for reducing costs and enhancing the analysis performance, critical for maximizing scientific output.

Future developments in AI and ML applications in HEP can provide transformative insights, particularly if tailored to the physics context. Furthermore, the community's adaptive software R&D endeavor, complemented by external collaborations with the computer science and Big Data communities, stands to enhance both present and future HEP projects.

Conclusion

The white paper provides an update on a comprehensive roadmap addressing imminent and long-term software challenges within HEP. The outlined strategies underscore the necessity for collaborative efforts, both within the HEP community and with industry and academic partners, to sustain its role at the forefront of scientific discovery. The effective implementation of these technological upgrades is anticipated to unlock new realms of investigation in particle physics, transcending the capabilities of existing infrastructure.