2022/03/16 by Christopher D. Jones, K. Knoepfel, Jones, Christopher D. +7
Computer Science · Physics and Astronomy · #Advanced Data Storage Technologies #Data Analysis #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Parallel #Particle physics theoretical and experimental studies #Statistics and Probability (physics.data-an) #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2203.14345
openalex publication_date 2022/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
HEP data-processing software must support the disparate physics needs of many experiments. For both collider and neutrino environments, HEP experiments typically use data-processing frameworks to manage the computational complexities of their large-scale data processing needs. Data-processing frameworks are being faced with new challenges this decade. The computing landscape has changed from the past three decades of homogeneous single-core x86 batch jobs running on grid sites. Frameworks must now work on a heterogeneous mixture of different platforms: multi-core machines, different CPU architectures, and computational accelerators; and different computing sites: grid, cloud, and high-performance computing. We describe these challenges in more detail and how frameworks may confront them. Given their historic success, frameworks will continue to be critical software systems that enable HEP experiments to meet their computing needs. Frameworks have weathered computing revolutions in the past; they will do so again with support from the HEP community