Evolution of HEP Processing Frameworks

TL;DR

This paper discusses the evolution of high-energy physics data-processing frameworks, highlighting new challenges posed by heterogeneous computing environments and emphasizing their ongoing importance for large-scale experiments.

Contribution

It analyzes recent challenges faced by HEP frameworks due to changing computing landscapes and discusses how these frameworks can adapt to future technological shifts.

Findings

Frameworks must support heterogeneous hardware and platforms.

Historical success suggests frameworks will adapt to future computing paradigms.

The paper identifies key challenges and potential strategies for evolution.

Abstract

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