skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: BigPanDA: PanDA Workload Management System and its Applications beyond ATLAS

Abstract

Modern experiments collect peta-scale volumes of data and utilize vast, geographically distributed computing infrastructure that serves thousands of scientists around the world. Requirements for rapid, near real-time data processing, fast analysis cycles and need to run massive detector simulations to support data analysis pose special premium on efficient use of available computational resources. A sophisticated Workload Management System (WMS) is needed to coordinate the distribution and processing of data and jobs in such environment. The ATLAS experiment at CERN uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. While PanDAcurrently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, it runs around 2 million jobs per day on hundreds of Grid sites and serving thousands of ATLAS users. In 2017 about 1.5 exabytes of data were processed with PanDA.In 2012 BigPanDA project project was started with aim to introduce new types of computing resources into ATLAS computing infrastructure, but also to offering PanDA features to different data-intensive applications for projects and experiments outside of ATLAS and High-Energy and Nuclear Physics. In this article we will present accomplishments and discuss possible directions for future work.

Authors:
 [1];  [2];  [3];  [1];  [1];  [4];  [1];  [1]; ORCiD logo [5];  [1]
  1. Brookhaven National Laboratory (BNL)
  2. University of Texas at Arlington
  3. University of Manchester, UK
  4. Lancaster University, UK
  5. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1564105
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) - Sofie, , Bulgaria - 7/9/2018 4:00:00 AM-7/13/2018 4:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Svirin, Pavlo, De, Kaushik, Forti, Alessandra, Klimentov, A, Larsen, Rasmus, Love, Peter, Maeno, T, Mashinistov, Ruslan, Wells, Jack, and Wenaus, T. BigPanDA: PanDA Workload Management System and its Applications beyond ATLAS. United States: N. p., 2019. Web.
Svirin, Pavlo, De, Kaushik, Forti, Alessandra, Klimentov, A, Larsen, Rasmus, Love, Peter, Maeno, T, Mashinistov, Ruslan, Wells, Jack, & Wenaus, T. BigPanDA: PanDA Workload Management System and its Applications beyond ATLAS. United States.
Svirin, Pavlo, De, Kaushik, Forti, Alessandra, Klimentov, A, Larsen, Rasmus, Love, Peter, Maeno, T, Mashinistov, Ruslan, Wells, Jack, and Wenaus, T. Sun . "BigPanDA: PanDA Workload Management System and its Applications beyond ATLAS". United States. https://www.osti.gov/servlets/purl/1564105.
@article{osti_1564105,
title = {BigPanDA: PanDA Workload Management System and its Applications beyond ATLAS},
author = {Svirin, Pavlo and De, Kaushik and Forti, Alessandra and Klimentov, A and Larsen, Rasmus and Love, Peter and Maeno, T and Mashinistov, Ruslan and Wells, Jack and Wenaus, T},
abstractNote = {Modern experiments collect peta-scale volumes of data and utilize vast, geographically distributed computing infrastructure that serves thousands of scientists around the world. Requirements for rapid, near real-time data processing, fast analysis cycles and need to run massive detector simulations to support data analysis pose special premium on efficient use of available computational resources. A sophisticated Workload Management System (WMS) is needed to coordinate the distribution and processing of data and jobs in such environment. The ATLAS experiment at CERN uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. While PanDAcurrently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, it runs around 2 million jobs per day on hundreds of Grid sites and serving thousands of ATLAS users. In 2017 about 1.5 exabytes of data were processed with PanDA.In 2012 BigPanDA project project was started with aim to introduce new types of computing resources into ATLAS computing infrastructure, but also to offering PanDA features to different data-intensive applications for projects and experiments outside of ATLAS and High-Energy and Nuclear Physics. In this article we will present accomplishments and discuss possible directions for future work.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: