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Long term sustainability of the high energy physics (HEP) research software ecosystem is essential for the field. With upgrades and new facilities coming online throughout the 2020s this will only become increasingly relevant throughout this decade. Meeting this sustainability challenge requires a workforce with a combination of HEP domain knowledge and advanced software skills. The required software skills fall into three broad groups. The first is fundamental and generic software engineering (e.g. Unix, version control,C++, continuous integration). The second is knowledge of domain specific HEP packages and practices (e.g., the ROOT data format and analysis framework). The third is more advanced knowledge involving more specialized techniques. These include parallel programming, machine learning and data science tools, and techniques to preserve software projects at all scales. This paper dis-cusses the collective software training program in HEP and its activities led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP). The program equips participants with an array of software skills that serve as ingredients from which solutions to the computing challenges of HEP can be formed. Beyond serving the community by ensuring that members are able to pursue research goals, this program serves individuals by providing intellectual capital and transferable skills that are becoming increasingly important to careers in the realm of software and computing, whether inside or outside HEP
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Meta-software for data acquisition (DAQ) is a new approach to design the DAQ systems for experimental setups in experiments in high energy physics (HEP). It abstracts from experiment-specific data processing logic, but reflects it through configuration. It is also intended to substitute highly integrated DAQ software for a swarm of single-functional components, orchestrated by universal meta-software.
This paper represents the vision of the members of the Fermilab Scientific Computing Divisions Computational Physics Department (SCD-CPD) on the status and the evolution of various HEP software tools such as the Geant4 detector simulation toolkit, the Pythia and GENIE physics generators, and the ROOT data analysis framework. The goal of this paper is to contribute ideas to the Snowmass 2013 process toward the composition of a unified document on the current status and potential evolution of the physics software tools which are essential to HEP.
The rapid evolution of technology and the parallel increasing complexity of algorithmic analysis in HEP requires developers to acquire a much larger portfolio of programming skills. Young researchers graduating from universities worldwide currently do not receive adequate preparation in the very diverse fields of modern computing to respond to growing needs of the most advanced experimental challenges. There is a growing consensus in the HEP community on the need for training programmes to bring researchers up to date with new software technologies, in particular in the domains of concurrent programming and artificial intelligence. We review some of the initiatives under way for introducing new training programmes and highlight some of the issues that need to be taken into account for these to be successful.
Setting up the infrastructure to manage a software project can become a task as significant writing the software itself. A variety of useful open source tools are available, such as Web-based viewers for version control systems, wikis for collaborative discussions and bug-tracking systems, but their use in high-energy physics, outside large collaborations, is insubstantial. Understandably, physicists would rather do physics than configure project management tools. We introduce the CEDAR HepForge system, which provides a lightweight development environment for HEP software. Services available as part of HepForge include the above-mentioned tools as well as mailing lists, shell accounts, archiving of releases and low-maintenance Web space. HepForge also exists to promote best-practice software development methods and to provide a central repository for re-usable HEP software and phenomenology codes.
Machine Learning (ML) will play a significant role in the success of the upcoming High-Luminosity LHC (HL-LHC) program at CERN. An unprecedented amount of data at the exascale will be collected by LHC experiments in the next decade, and this effort will require novel approaches to train and use ML models. In this paper, we discuss a Machine Learning as a Service pipeline for HEP (MLaaS4HEP) which provides three independent layers: a data streaming layer to read High-Energy Physics (HEP) data in their native ROOT data format; a data training layer to train ML models using distributed ROOT files; a data inference layer to serve predictions using pre-trained ML models via HTTP protocol. Such modular design opens up the possibility to train data at large scale by reading ROOT files from remote storage facilities, e.g. World-Wide LHC Computing Grid (WLCG) infrastructure, and feed the data to the users favorite ML framework. The inference layer implemented as TensorFlow as a Service (TFaaS) may provide an easy access to pre-trained ML models in existing infrastructure and applications inside or outside of the HEP domain. In particular, we demonstrate the usage of the MLaaS4HEP architecture for a physics use-case, namely the $tbar{t}$ Higgs analysis in CMS originally performed using custom made Ntuples. We provide details on the training of the ML model using distributed ROOT files, discuss the performance of the MLaaS and TFaaS approaches for the selected physics analysis, and compare the results with traditional methods.
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