arXiv subjects

Oksana Shadura

Publications and source records attributed to Oksana Shadura.

At least 19 recordsLinked to original sources

arXiv2026

A study of CMS analysis pipelines through the Integration Challenge

The upcoming High-Luminosity Large Hadron Collider (HL-LHC) at CERN will deliver an unprecedented volume of data for High Energy Physics (HEP). This wealth of information offers significant opportunities for scientific discovery, but its scale challenges traditional analysis workflows. In this paper, we present CMS analysis pipelines being developed to meet HL-LHC demands. These pipelines build on the broader scientific Python ecosystem, complemented by solutions specifically designed for HEP. A central focus of this work is the Integration Challenge, an IRIS-HEP led effort aimed at assessing the readiness of developed software stack to be used in real world physics analysis and improving the readiness of analysis facilities for the HL-LHC era. The Integration Challenge acts as an end-to-end integration test: by implementing a complete physics analysis pipeline, it evaluates tool interoperability and the overall user experience for analysts. The current pipeline includes columnar data processing, machine learning, statistical inference, and visualization tasks covering a variety of CMS analysis scenarios. In addition, the Integration Challenge explores efficient strategies for delivering skimmed data using diverse tools and data formats, as well as evaluating the ServiceX data-delivery system for HEP analyses. Throughout the testing phase, we also investigated several prototype services, such as histogram-as-a-service capabilities, along with other emerging services that may support future HL-LHC analysis workflows.

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arXiv2026

Recent benchmarks in the Analysis Grand Challenge and integration with Combine (and HS3)

The Analysis Grand Challenge (AGC) showcases an example of HEP analysis. Its reference implementation uses modern Python packages to realize the main steps, from data access to statistical model building and fitting. The packages used for data handling and processing (coffea, uproot, awkward-array) have recently undergone a series of performance optimizations. While not being part of the HEP Python (PyHEP) ecosystem, the Combine tool is a pillar of CMS analyses, covering more than 90% of the analyses published in the last few years. As such, it is necessary to have Combine integrated in the PyHEP ecosystem, using the AGC as example. This project also includes, in the long-term, providing support and integration for the High Energy Physics Statistics Serialization Standard (HS3), as a way to have a language-independent way of representing the likelihood and use different frameworks interchangeably. In these proceedings we cover part of the recent work performed on the AGC and Combine, including: performance benchmarks, covering benefits introduced by the recent improvements in the data processing packages; examples of how Combine can be integrated and run in a dedicated infrastructure (coffea-casa); and examples and plans to integrate HS3 in Combine.

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arXiv2025

The 200 Gbps Challenge: Imagining HL-LHC analysis facilities

The IRIS-HEP software institute, as a contributor to the broader HEP Python ecosystem, is developing scalable analysis infrastructure and software tools to address the upcoming HL-LHC computing challenges with new approaches and paradigms, driven by our vision of what HL-LHC analysis will require. The institute uses a "Grand Challenge" format, constructing a series of increasingly large, complex, and realistic exercises to show the vision of HL-LHC analysis. Recently, the focus has been demonstrating the IRIS-HEP analysis infrastructure at scale and evaluating technology readiness for production. As a part of the Analysis Grand Challenge activities, the institute executed a "200 Gbps Challenge", aiming to show sustained data rates into the event processing of multiple analysis pipelines. The challenge integrated teams internal and external to the institute, including operations and facilities, analysis software tools, innovative data delivery and management services, and scalable analysis infrastructure. The challenge showcases the prototypes - including software, services, and facilities - built to process around 200 TB of data in both the CMS NanoAOD and ATLAS PHYSLITE data formats with test pipelines. The teams were able to sustain the 200 Gbps target across multiple pipelines. The pipelines focusing on event rate were able to process at over 30 MHz. These target rates are demanding; the activity revealed considerations for future testing at this scale and changes necessary for physicists to work at this scale in the future. The 200 Gbps Challenge has established a baseline on today's facilities, setting the stage for the next exercise at twice the scale.

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arXiv2025

Tuning the CMS Coffea-casa facility for 200 Gbps Challenge

As a part of the IRIS-HEP "Analysis Grand Challenge" activities, the Coffea-casa AF team executed a "200 Gbps Challenge". One of the goals of this challenge was to provide a setup for execution of a test notebook-style analysis on the facility that could process a 200 TB CMS NanoAOD dataset in 20 minutes. We describe the solutions we deployed at the facility to execute the challenge tasks. The facility was configured to provide 2000+ cores for quick turn-around, low-latency analysis. To reach the highest event processing rates we tested different scaling backends, both scaling over HTCondor and Kubernetes resources and using Dask and Taskvine schedulers. This configuration also allowed us to compare two different services for managing Dask clusters, Dask labextention, and Dask Gateway server, under extreme conditions. A robust set of XCache servers with a redirector were deployed in Kubernetes to cache the dataset to minimize wide-area network traffic. The XCache servers were backed with solid-state NVME drives deployed within the Kubernetes cluster nodes. All data access was authenticated using scitokens and was transparent to the user. To ensure we could track and measure data throughput precisely, we used our existing Prometheus monitoring stack to monitor the XCache pod throughput on the Kubernetes network layer. Using the rate query across all of the 8 XCache pods we were able to view a stacked cumulative graph of the total throughput for each XCache. This monitoring setup allowed us to ensure uniform data rates across all nodes while verifying we had reached the 200 Gbps benchmark.

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arXiv2024

PyHEP.dev 2024 Workshop Summary Report, August 26-30 2024, Aachen, Germany

The second PyHEP.dev workshop, part of the "Python in HEP Developers" series organized by the HEP Software Foundation (HSF), took place in Aachen, Germany, from August 26 to 30, 2024. This gathering brought together nearly 30 Python package developers, maintainers, and power users to engage in informal discussions about current trends in Python, with a primary focus on analysis tools and techniques in High Energy Physics (HEP). The workshop agenda encompassed a range of topics, such as defining the scope of HEP data analysis, exploring the Analysis Grand Challenge project, evaluating statistical models and serialization methods, assessing workflow management systems, examining histogramming practices, and investigating distributed processing tools like RDataFrame, Coffea, and Dask. Additionally, the workshop dedicated time to brainstorming the organization of future PyHEP.dev events, upholding the tradition of alternating between Europe and the United States as host locations. This document, prepared by the session conveners in the weeks following the workshop, serves as a summary of the key discussions, salient points, and conclusions that emerged.

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arXiv2024

Physics analysis for the HL-LHC: concepts and pipelines in practice with the Analysis Grand Challenge

Realistic environments for prototyping, studying and improving analysis workflows are a crucial element on the way towards user-friendly physics analysis at HL-LHC scale. The IRIS-HEP Analysis Grand Challenge (AGC) provides such an environment. It defines a scalable and modular analysis task that captures relevant workflow aspects, ranging from large-scale data processing and handling of systematic uncertainties to statistical inference and analysis preservation. By being based on publicly available Open Data, the AGC provides a point of contact for the broader community. Multiple different implementations of the analysis task that make use of various pipelines and software stacks already exist. This contribution presents an updated AGC analysis task. It features a machine learning component and expanded analysis complexity, including the handling of an extended and more realistic set of systematic uncertainties. These changes both align the AGC further with analysis needs at the HL-LHC and allow for probing an increased set of functionality. Another focus is the showcase of a reference AGC implementation, which is heavily based on the HEP Python ecosystem and uses modern analysis facilities. The integration of various data delivery strategies is described, resulting in multiple analysis pipelines that are compared to each other.

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arXiv2024

Machine Learning for Columnar High Energy Physics Analysis

Machine learning (ML) has become an integral component of high energy physics data analyses and is likely to continue to grow in prevalence. Physicists are incorporating ML into many aspects of analysis, from using boosted decision trees to classify particle jets to using unsupervised learning to search for physics beyond the Standard Model. Since ML methods have become so widespread in analysis and these analyses need to be scaled up for HL-LHC data, neatly integrating ML training and inference into scalable analysis workflows will improve the user experience of analysis in the HL-LHC era. We present the integration of ML training and inference into the IRIS-HEP Analysis Grand Challenge (AGC) pipeline to provide an example of how this integration can look like in a realistic analysis environment. We also utilize Open Data to ensure the project's reach to the broader community. Different approaches for performing ML inference at analysis facilities are investigated and compared, including performing inference through external servers. Since ML techniques are applied for many different types of tasks in physics analyses, we showcase options for ML integration that can be applied to various inference needs.

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arXiv2023

Coffea-Casa: Building composable analysis facilities for the HL-LHC

The large data volumes expected from the High Luminosity LHC (HL-LHC) present challenges to existing paradigms and facilities for end-user data analysis. Modern cyberinfrastructure tools provide a diverse set of services that can be composed into a system that provides physicists with powerful tools that give them straightforward access to large computing resources, with low barriers to entry. The Coffea-Casa analysis facility (AF) provides an environment for end users enabling the execution of increasingly complex analyses such as those demonstrated by the Analysis Grand Challenge (AGC) and capturing the features that physicists will need for the HL-LHC. We describe the development progress of the Coffea-Casa facility featuring its modularity while demonstrating the ability to port and customize the facility software stack to other locations. The facility also facilitates the support of batch systems while staying Kubernetes-native. We present the evolved architecture of the facility, such as the integration of advanced data delivery services (e.g. ServiceX) and making data caching services (e.g. XCache) available to end users of the facility. We also highlight the composability of modern cyberinfrastructure tools. To enable machine learning pipelines at coffee-casa analysis facilities, a set of industry ML solutions adopted for HEP columnar analysis were integrated on top of existing facility services. These services also feature transparent access for user workflows to GPUs available at a facility via inference servers while using Kubernetes as enabling technology.

cs.DC↗

arXiv2023

First performance measurements with the Analysis Grand Challenge

The IRIS-HEP Analysis Grand Challenge (AGC) is designed to be a realistic environment for investigating how analysis methods scale to the demands of the HL-LHC. The analysis task is based on publicly available Open Data and allows for comparing the usability and performance of different approaches and implementations. It includes all relevant workflow aspects from data delivery to statistical inference. The reference implementation for the AGC analysis task is heavily based on tools from the HEP Python ecosystem. It makes use of novel pieces of cyberinfrastructure and modern analysis facilities in order to address the data processing challenges of the HL-LHC. This contribution compares multiple different analysis implementations and studies their performance. Differences between the implementations include the use of multiple data delivery mechanisms and caching setups for the analysis facilities under investigation.

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arXiv2022

Second Analysis Ecosystem Workshop Report

The second workshop on the HEP Analysis Ecosystem took place 23-25 May 2022 at IJCLab in Orsay, to look at progress and continuing challenges in scaling up HEP analysis to meet the needs of HL-LHC and DUNE, as well as the very pressing needs of LHC Run 3 analysis. The workshop was themed around six particular topics, which were felt to capture key questions, opportunities and challenges. Each topic arranged a plenary session introduction, often with speakers summarising the state-of-the art and the next steps for analysis. This was then followed by parallel sessions, which were much more discussion focused, and where attendees could grapple with the challenges and propose solutions that could be tried. Where there was significant overlap between topics, a joint discussion between them was arranged. In the weeks following the workshop the session conveners wrote this document, which is a summary of the main discussions, the key points raised and the conclusions and outcomes. The document was circulated amongst the participants for comments before being finalised here.

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arXiv2022

Collaborative Computing Support for Analysis Facilities Exploiting Software as Infrastructure Techniques

Prior to the public release of Kubernetes it was difficult to conduct joint development of elaborate analysis facilities due to the highly non-homogeneous nature of hardware and network topology across compute facilities. However, since the advent of systems like Kubernetes and OpenShift, which provide declarative interfaces for building fault-tolerant and self-healing deployments of networked software, it is possible for multiple institutes to collaborate more effectively since resource details are abstracted away through various forms of hardware and software virtualization. In this whitepaper we will outline the development of two analysis facilities: "Coffea-casa" at University of Nebraska Lincoln and the "Elastic Analysis Facility" at Fermilab, and how utilizing platform abstraction has improved the development of common software for each of these facilities, and future development plans made possible by this methodology.

physics.data-an↗

arXiv2022

Analysis Facilities for HL-LHC

The HL-LHC presents significant challenges for the HEP analysis community. The number of events in each analysis is expected to increase by an order of magnitude and new techniques are expected to be required; both challenges necessitate new services and approaches for analysis facilities. These services are expected to provide new capabilities, a larger scale, and different access modalities (complementing -- but distinct from -- traditional batch-oriented approaches). To facilitate this transition, the US-LHC community is actively investing in analysis facilities to provide a testbed for those developing new analysis systems and to demonstrate new techniques for service delivery. This whitepaper outlines the existing activities within the US LHC community in this R&D area, the short- to medium-term goals, and the outline of common goals and milestones.

hep-ex↗

arXiv2022

HL-LHC Computing Review Stage 2, Common Software Projects: Data Science Tools for Analysis

This paper was prepared by the HEP Software Foundation (HSF) PyHEP Working Group as input to the second phase of the LHCC review of High-Luminosity LHC (HL-LHC) computing, which took place in November, 2021. It describes the adoption of Python and data science tools in HEP, discusses the likelihood of future scenarios, and recommendations for action by the HEP community.

physics.data-an↗

arXiv2021

Software Training in HEP

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

hep-ex↗

arXiv2021

Coffea-casa: an analysis facility prototype

Data analysis in HEP has often relied on batch systems and event loops; users are given a non-interactive interface to computing resources and consider data event-by-event. The "Coffea-casa" prototype analysis facility is an effort to provide users with alternate mechanisms to access computing resources and enable new programming paradigms. Instead of the command-line interface and asynchronous batch access, a notebook-based web interface and interactive computing is provided. Instead of writing event loops, the column-based Coffea library is used. In this paper, we describe the architectural components of the facility, the services offered to end-users, and how it integrates into a larger ecosystem for data access and authentication.

cs.DC↗

arXiv2020

C++ Modules in ROOT and Beyond

C++ Modules come in C++20 to fix the long-standing build scalability problems in the language. They provide an io-efficient, on-disk representation capable to reduce build times and peak memory usage. ROOT employs the C++ modules technology further in the ROOT dictionary system to improve its performance and reduce the memory footprint. ROOT with C++ Modules was released as a technology preview in fall 2018, after intensive development during the last few years. The current state is ready for production, however, there is still room for performance optimizations. In this talk, we show the roadmap for making the technology default in ROOT. We demonstrate a global module indexing optimization which allows reducing the memory footprint dramatically for many workflows. We will report user feedback on the migration to ROOT with C++ Modules.

cs.DC↗

arXiv2020

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.

physics.data-an↗

arXiv2020

Automatic Differentiation in ROOT

In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evaluate the derivative of a function specified by a computer program. AD exploits the fact that every computer program, no matter how complicated, executes a sequence of elementary arithmetic operations (addition, subtraction, multiplication, division, etc.), elementary functions (exp, log, sin, cos, etc.) and control flow statements. AD takes source code of a function as input and produces source code of the derived function. By applying the chain rule repeatedly to these operations, derivatives of arbitrary order can be computed automatically, accurately to working precision, and using at most a small constant factor more arithmetic operations than the original program. This paper presents AD techniques available in ROOT, supported by Cling, to produce derivatives of arbitrary C/C++ functions through implementing source code transformation and employing the chain rule of differential calculus in both forward mode and reverse mode. We explain its current integration for gradient computation in TFormula. We demonstrate the correctness and performance improvements in ROOT's fitting algorithms.

cs.MS↗