A5061685628- Distributed and Parallel Computing Systems
- Scientific Computing and Data Management
- Advanced Data Storage Technologies
- Big Data Technologies and Applications
- Particle physics theoretical and experimental studies
- Particle Detector Development and Performance
- Surface and Thin Film Phenomena
- Advanced Materials Characterization Techniques
- Semiconductor materials and interfaces
- Economic theories and models
- Data Mining Algorithms and Applications
- Topic Modeling
- Advanced Text Analysis Techniques
- Superconductivity in MgB2 and Alloys
- Economic and Technological Systems Analysis
- Advanced Chemical Physics Studies
- Nonlinear Dynamics and Pattern Formation
- Nonlinear Waves and Solitons
- Photosynthetic Processes and Mechanisms
- Research Data Management Practices
- Software System Performance and Reliability
- Advanced Mathematical Physics Problems
- Advanced Data Processing Techniques
- Plant responses to elevated CO2
- Economic Systems and Logistics Management
The University of Texas at Arlington
2024-2025
Russian New University
2024
Gubkin Russian State University of Oil and Gas
2024
Tambov State Technical University
2024
Institute for System Programming
2020-2023
Universidad Andrés Bello
2020-2023
Plekhanov Russian University of Economics
2018-2023
Ulyanovsk State University
2022
Lomonosov Moscow State University
2007-2022
Moscow State University
2012-2022
Abstract The Production and Distributed Analysis (PanDA) system is a data-driven workload management engineered to operate at the LHC data processing scale. PanDA provides solution for scientific experiments fully leverage their distributed heterogeneous resources, showcasing scalability, usability, flexibility, robustness. has successfully proven itself through nearly two decades of steady operation in ATLAS experiment, addressing intricate requirements such as diverse resources worldwide...
Abstract The ATLAS experiment has developed extensive software and distributed computing systems for Run 3 of the LHC. These are described in detail, including infrastructure workflows, data workload management, database infrastructure, validation. use these to prepare physics analysis assess its quality described, along with tools used itself. An outlook development projects towards 4 is also provided.
For the last 10 years, ATLAS Distributed Computing project has based its monitoring infrastructure on a set of custom designed dashboards provided by CERN. This system functioned very well for LHC Runs 1 and 2, but maintenance progressively become more difficult conditions Run 3, starting in 2021, will be even demanding; hence standard code base automatic operations are needed. A new been CERN, InfluxDB as data store Grafana display environment. adapted further developed tools to use this...
Monitoring services play a crucial role in the day-to-day operation of distributed computing systems. The ATLAS Experiment at LHC uses Production and Distributed Analysis workload management system (PanDA WMS), which allows million computational jobs to run daily over 170 centers WLCG opportunistic resources, utilizing 600k cores simultaneously on average. BigPanDA monitor is an essential part monitoring infrastructure for that provides wide range views, from top-level summaries single job...
In recent years, advanced and complex analysis workflows have gained increasing importance in the ATLAS experiment at CERN, one of large scientific experiments LHC. Support for such has allowed users to exploit remote computing resources service providers distributed worldwide, overcoming limitations on local services. The spectrum options keeps across Worldwide LHC Computing Grid (WLCG), volunteer computing, high-performance commercial clouds, emerging levels like Platform-as-a-Service...
Machine Learning (ML) has become one of the important tools for High Energy Physics analysis. As size dataset increases at Large Hadron Collider (LHC), and same time search spaces bigger in order to exploit physics potentials, more computing resources are required processing these ML tasks. In addition, complex advanced workflows developed which task may depend on results previous How make use vast distributed CPUs/GPUs WLCG big tasks a popular research area. this paper, we present our...
BigPanDA monitoring is a web application that provides various processing and representation of the Production Distributed Analysis (PanDA) system objects states. Analysing hundreds millions computation entities, such as an event or job, builds different scales levels abstraction reports in real time mode. Provided information allows users to drill down into reason concrete failure observe broad picture tracking nucleus satellites performance progress whole production campaign. PanDA was...
ATLAS Computing Management has identified the migration of all computing resources to Harvester, PanDA’s new workload submission engine, as a critical milestone for LHC Run 3 and 4. This contribution will focus on Grid Harvester. We have built redundant architecture based CERN IT’s common offerings (e.g. Openstack Virtual Machines Database Demand) run necessary Harvester HTCondor services, capable sustaining load O(1M) workers per day. reviewed region by moved much possible away from blind...
The paper describes the implementation of a high-performance system for processing and analysis log files PanDA infrastructure ATLAS experiment at Large Hadron Collider (LHC), responsible workload management order 2M daily jobs across Worldwide LHC Computing Grid. solution is based on ELK technology stack, which includes several components: Filebeat, Logstash, ElasticSearch (ES), Kibana. Filebeat used to collect data from logs. Logstash processes export Elasticsearch. ES are centralized...
The intelligent Data Delivery Service (iDDS) has been developed to cope with the huge increase of computing and storage resource usage in coming LHC data taking. iDDS designed intelligently orchestrate workflow management systems, decoupling pre-processing, delivery, main processing various workflows. It is an experiment-agnostic service around a workflow-oriented structure work existing emerging use cases ATLAS other experiments. Here we will present motivation for iDDS, its design schema...
In this work, we present a method to control text-to-image generative model produce training data specifically "useful" for supervised learning. Unlike previous works that employ an open-loop approach and pre-define prompts generate new using either language or human expertise, develop automated closed-loop system which involves two feedback mechanisms. The first mechanism uses from given finds adversarial result in image generations maximize the loss. While these diverse informed by model,...
The ATLAS experiment at the LHC utilizes complex multicomponent distributed systems for processing (PanDA WMS) and managing (Rucio) data. complexity of relationships between components, amount data being processed continuous development new functionalities critical are main challenges to consider when creating monitoring accounting tools able adapt this dynamic environment in a short time. To overcome these challenges, uses unified infrastructure (UMA) provided by CERN-IT since 2018, which...
In the conditions of natural resources depletion, and Climate changes caused by greenhouse effect, world community is focused on exploring new raw materials, secondary including waste that can be usefully used. Fat-containing generated in food, pulp paper, leather other industries, catering facilities have significant potential this field. Analysis official statistics fat-containing management Russian Federation shows about 22% such disposed landfills irretrievably lost as materials. And...
The efficiency of application calcium-containing reagents (calcium oxide, calcium hydroxide, peroxide) for removal phosphate ions from waste water was evaluated. It has been established that oxide (99,78 %) and hydroxide (99,80 were the most effective ones dephosphorization; peroxide 90,0 %. changes COD pH indicators depending on concentration dephosphorizing added to wastewater experimentally investigated. Conclusions about are formulated.
Abstract The next phase of LHC Operations – High Luminosity (HL-LHC), which is aimed at ten-fold increase in the luminosity proton-proton collisions energy 14 TeV, expected to start operation 2027-2028 and will deliver an unprecedented scientific data volume multi-exabyte scale. This amount has be stored corresponding storage system should ensure fast reliable delivery for processing by groups distributed all over world. present computing model not able provide required infrastructure growth...
The High Luminosity phase of the LHC, which aims for a tenfold increase in luminosity proton-proton collisions is expected to start operation eight years. An unprecedented scientific data volume at multiexabyte scale will be delivered particle physics experiments CERN. This amount has stored and corresponding technology must ensure fast reliable delivery processing by community all over world. present LHC computing model not able provide required infrastructure growth even taking into...