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Title: Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science

Abstract

The.LHC, operating at CERN, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe. ATLAS, one of the largest collaborations ever assembled in the sciences, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. While PanDA currently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, LHC data taking runs require more resources than grid can possibly provide. To alleviate these challenges, LHC experiments are engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with supercomputers in United States, in particular with Titan supercomputer atmore » Oak Ridge Leadership Computing Facility. Current approach utilizes modified PanDA pilot framework for job submission to the supercomputers batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on LCFs multi-core worker nodes. This implementation was tested with a variety of Monte-Carlo workloads on several supercomputing platforms for ALICE and ATLAS experiments and it is in full pro duction for the ATLAS since September 2015. We will present our current accomplishments with running PanDA at supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications, such as bioinformatics and astro-particle physics.« less

Authors:
 [1];  [2];  [3];  [1];  [1];  [4];  [1];  [5];  [1]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States). Dept. of Physics
  2. Univ. of Texas, Arlington, TX (United States). Dept. of Physics
  3. Rutgers Univ., Piscataway, NJ (United States). Dept. of Electrical and Computer Engineering
  4. Univ. of Texas, Arlington, TX (United States). Dept. of Physics; Joint Inst. for Nuclear Research (JINR), Dubna (Russia)
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1567418
Grant/Contract Number:  
AC02-98CH10886; AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Physics. Conference Series
Additional Journal Information:
Journal Volume: 762; Conference: 17. International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2016), Valparaiso (Chile), 18-22 Jan 2016; Journal ID: ISSN 1742-6588
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Computer Science; Physics

Citation Formats

Klimentov, A., De, K., Jha, S., Maeno, T., Nilsson, P., Oleynik, D., Panitkin, S., Wells, J., and Wenaus, T. Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science. United States: N. p., 2016. Web. doi:10.1088/1742-6596/762/1/012021.
Klimentov, A., De, K., Jha, S., Maeno, T., Nilsson, P., Oleynik, D., Panitkin, S., Wells, J., & Wenaus, T. Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science. United States. https://doi.org/10.1088/1742-6596/762/1/012021
Klimentov, A., De, K., Jha, S., Maeno, T., Nilsson, P., Oleynik, D., Panitkin, S., Wells, J., and Wenaus, T. Sat . "Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science". United States. https://doi.org/10.1088/1742-6596/762/1/012021. https://www.osti.gov/servlets/purl/1567418.
@article{osti_1567418,
title = {Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science},
author = {Klimentov, A. and De, K. and Jha, S. and Maeno, T. and Nilsson, P. and Oleynik, D. and Panitkin, S. and Wells, J. and Wenaus, T.},
abstractNote = {The.LHC, operating at CERN, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe. ATLAS, one of the largest collaborations ever assembled in the sciences, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. While PanDA currently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, LHC data taking runs require more resources than grid can possibly provide. To alleviate these challenges, LHC experiments are engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with supercomputers in United States, in particular with Titan supercomputer at Oak Ridge Leadership Computing Facility. Current approach utilizes modified PanDA pilot framework for job submission to the supercomputers batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on LCFs multi-core worker nodes. This implementation was tested with a variety of Monte-Carlo workloads on several supercomputing platforms for ALICE and ATLAS experiments and it is in full pro duction for the ATLAS since September 2015. We will present our current accomplishments with running PanDA at supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications, such as bioinformatics and astro-particle physics.},
doi = {10.1088/1742-6596/762/1/012021},
url = {https://www.osti.gov/biblio/1567418}, journal = {Journal of Physics. Conference Series},
issn = {1742-6588},
number = ,
volume = 762,
place = {United States},
year = {2016},
month = {10}
}

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Works referenced in this record:

SAGA: A standardized access layer to heterogeneous Distributed Computing Infrastructure
journal, September 2015