Snowmass Computational Frontier: Topical Group Report on Experimental Algorithm Parallelization
G. Cerati, K. Heitmann, W. Hopkins, J. Bennett, T.Y. Chen, V.V., Gligorov, O. Gutsche, S. Habib, M. Kortelainen, C. Leggett, R. Mandelbaum, N., Whitehorn, M. Williams

TL;DR
This report discusses the challenges and solutions for developing and parallelizing experimental algorithms in high-energy physics, emphasizing heterogeneous computing, software sharing, workforce development, and cross-disciplinary collaboration for future experiments.
Contribution
It provides a comprehensive overview of strategies and infrastructure needs for experimental algorithm development and parallelization over the next 10-15 years.
Findings
Need for portable, shared software tools across experiments.
Importance of workforce development in algorithm research.
Challenges posed by heterogeneous computing architectures.
Abstract
The substantial increase in data volume and complexity expected from future experiments will require significant investment to prepare experimental algorithms. These algorithms include physics object reconstruction, calibrations, and processing of observational data. In addition, the changing computing architecture landscape, which will be primarily composed of heterogeneous resources, will continue to pose major challenges with regard to algorithmic migration. Portable tools need to be developed that can be shared among the frontiers (e.g., for code execution on different platforms) and opportunities, such as forums or cross-experimental working groups, need to be provided where experiences and lessons learned can be shared between experiments and frontiers. At the same time, individual experiments also need to invest considerable resources to develop algorithms unique to their needs…
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Taxonomy
TopicsDistributed and Parallel Computing Systems · Advanced Data Storage Technologies · Scientific Computing and Data Management
