RNTuple performance: Status and Outlook

Upcoming HEP experiments, e.g. at the HL-LHC, are expected to increase the volume of generated data by at least one order of magnitude. In order to retain the ability to analyze the influx of data, full exploitation of modern storage hardware and systems, such as low-latency high-bandwidth NVMe devices and distributed object stores, becomes critical. To this end, the ROOT RNTuple I/O subsystem has been designed to address performance bottlenecks and shortcomings of ROOT's current state of the art TTree I/O subsystem. RNTuple provides a backwards-incompatible redesign of the TTree binary format and access API that evolves the ROOT event data I/O for the challenges of the upcoming decades. It focuses on a compact data format, on performance engineering for modern storage hardware, for instance through making parallel and asynchronous I/O calls by default, and on robust interfaces that are easy to use correctly. In this contribution, we evaluate the RNTuple performance for typical HEP analysis tasks. We compare the throughput delivered by RNTuple to popular I/O libraries outside HEP, such as HDF5 and Apache Parquet. We demonstrate the advantages of RNTuple for HEP analysis workflows and provide an outlook on the road to its use in production.Comment: 5 pages, 5 figures; submitted to proceedings of 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021

The Need for a Versioned Data Analysis Software Environment

Scientific results in high-energy physics and in many other fields often rely on complex software stacks. In order to support reproducibility and scrutiny of the results, it is good practice to use open source software and to cite software packages and versions. With ever-growing complexity of scientific software on one side and with IT life-cycles of only a few years on the other side, however, it turns out that despite source code availability the setup and the validation of a minimal usable analysis environment can easily become prohibitively expensive. We argue that there is a substantial gap between merely having access to versioned source code and the ability to create a data analysis runtime environment. In order to preserve all the different variants of the data analysis runtime environment, we developed a snapshotting file system optimized for software distribution. We report on our experience in preserving the analysis environment for high-energy physics such as the software landscape used to discover the Higgs boson at the Large Hadron Collider

The GrGen.NET User Manual. Refers to GrGen.NET User Manual, www.grgen.net

GrGen.NET is a graph rewrite tool enabling elegant and convenient development of graph rewriting applications with comparable performance to conventionally developed ones. GrGen.NET uses attributed, typed, and directed multigraphs with multiple inheritance on node and edge types. Extensive graphical debugging integrated into an interactive shell complements the feature highlights of GrGen.NET. This user manual contains both, normative statements in the sense of a reference manual as well as an informal guide to the features and usage of GrGen.NET

A caching mechanism to exploit object store speed in High Energy Physics analysis

[EN] Data analysis workflows in High Energy Physics (HEP) read data written in the ROOT columnar format. Such data has traditionally been stored in files that are often read via the network from remote storage facilities, which represents a performance penalty especially for data processing workflows that are I/O bound. To address that issue, this paper presents a new caching mechanism, implemented in the I/O subsystem of ROOT, which is independent of the storage backend used to write the dataset. Notably, it can be used to leverage the speed of high-bandwidth, low-latency object stores. The performance of this caching approach is evaluated by running a real physics analysis on an Intel DAOS cluster, both on a single node and distributed on multiple nodes.This work benefited from the support of the CERN Strategic R&D Programme on Technologies for Future Experiments [1] and from grant PID2020-113656RB-C22 funded by Ministerio de Ciencia e Innovacion MCIN/AEI/10.13039/501100011033. The hardware used to perform the experimental evaluation involving DAOS (HPE Delphi cluster described in Sect. 5.2) was made available thanks to a collaboration agreement with Hewlett-Packard Enterprise (HPE) and Intel. User access to the Virgo cluster at the GSI institute was given for the purpose of running the benchmarks using the Lustre filesystem.Padulano, VE.; Tejedor Saavedra, E.; Alonso-Jordá, P.; López Gómez, J.; Blomer, J. (2022). A caching mechanism to exploit object store speed in High Energy Physics analysis. Cluster Computing. 1-16. https://doi.org/10.1007/s10586-022-03757-211

PROOF as a Service on the Cloud: a Virtual Analysis Facility based on the CernVM ecosystem

PROOF, the Parallel ROOT Facility, is a ROOT-based framework which enables interactive parallelism for event-based tasks on a cluster of computing nodes. Although PROOF can be used simply from within a ROOT session with no additional requirements, deploying and configuring a PROOF cluster used to be not as straightforward. Recently great efforts have been spent to make the provisioning of generic PROOF analysis facilities with zero configuration, with the added advantages of positively affecting both stability and scalability, making the deployment operations feasible even for the end user. Since a growing amount of large-scale computing resources are nowadays made available by Cloud providers in a virtualized form, we have developed the Virtual PROOF-based Analysis Facility: a cluster appliance combining the solid CernVM ecosystem and PoD (PROOF on Demand), ready to be deployed on the Cloud and leveraging some peculiar Cloud features such as elasticity. We will show how this approach is effective both for sysadmins, who will have little or no configuration to do to run it on their Clouds, and for the end users, who are ultimately in full control of their PROOF cluster and can even easily restart it by themselves in the unfortunate event of a major failure. We will also show how elasticity leads to a more optimal and uniform usage of Cloud resources.Comment: Talk from Computing in High Energy and Nuclear Physics 2013 (CHEP2013), Amsterdam (NL), October 2013, 7 pages, 4 figure

Software Challenges For HL-LHC Data Analysis

The high energy physics community is discussing where investment is needed to prepare software for the HL-LHC and its unprecedented challenges. The ROOT project is one of the central software players in high energy physics since decades. From its experience and expectations, the ROOT team has distilled a comprehensive set of areas that should see research and development in the context of data analysis software, for making best use of HL-LHC's physics potential. This work shows what these areas could be, why the ROOT team believes investing in them is needed, which gains are expected, and where related work is ongoing. It can serve as an indication for future research proposals and cooperations

ROOT for the HL-LHC: data format

This document discusses the state, roadmap, and risks of the foundational components of ROOT with respect to the experiments at the HL-LHC (Run 4 and beyond). As foundational components, the document considers in particular the ROOT input/output (I/O) subsystem. The current HEP I/O is based on the TFile container file format and the TTree binary event data format. The work going into the new RNTuple event data format aims at superseding TTree, to make RNTuple the production ROOT event data I/O that meets the requirements of Run 4 and beyond

A Roadmap for HEP Software and Computing R&D for the 2020s

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.Peer reviewe

Decentralized Data Storage and Processing in the Context of the LHC Experiments at CERN

The computing facilities used to process data for the experiments at the Large Hadron Collider (LHC) at CERN are scattered around the world. The embarrassingly parallel workload allows for use of various computing resources, such as computer centers comprising the Worldwide LHC Computing Grid, commercial and institutional cloud resources, as well as individual home PCs in “volunteer clouds”. Unlike data, the experiment software and its operating system dependencies cannot be easily split into small chunks. Deployment of experiment software on distributed grid sites is challenging since it consists of millions of small files and changes frequently. This thesis develops a systematic approach to distribute a homogeneous runtime environment to a heterogeneous and geographically distributed computing infrastructure. A uniform bootstrap environment is provided by a minimal virtual machine tailored to LHC applications. Based on a study of the characteristics of LHC experiment software, the thesis argues for the use of content-addressable storage and decentralized caching in order to distribute the experiment software. In order to utilize the technology at the required scale, new methods of pre-processing data into content-addressable storage are developed. A co-operative, decentralized memory cache is designed that is optimized for the high peer churn expected in future virtualized computing clusters. This is achieved using a combination of consistent hashing with global knowledge about the worker nodes’ state. The methods have been implemented in the form of a file system for software and Conditions Data delivery. The file system has been widely adopted by the LHC community and the benefits of the presented methods have been demonstrated in practice