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Title: INTEGRATION OF PANDA WORKLOAD MANAGEMENT SYSTEM WITH SUPERCOMPUTERS

Abstract

Abstract The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the funda- mental nature of matter and the basic forces that shape our universe, and were recently credited for the dis- covery of a Higgs boson. 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 140 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data cen- ters are physically scattered all over the world. While PanDA currently uses more than 250000 cores with a peak performance of 0.3+ petaFLOPS, next LHC data taking runs will require more resources than Grid computing 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 suchmore » as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with supercomputers in United States, Europe and Russia (in particular with Titan supercomputer at Oak Ridge Leadership Com- puting Facility (OLCF), Supercomputer at the National Research Center Kurchatov Institute , IT4 in Ostrava, and others). The current approach utilizes a 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 Titan s multi-core worker nodes. This implementation was tested with a variety of Monte-Carlo workloads on several supercomputing platforms. We will present our current accom- plishments in running PanDA WMS at supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facility s 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];  [4];  [3];  [4];  [1];  [3];  [4];  [4];  [4];  [5];  [4];  [6];  [7];  [3]
  1. University of Texas at Arlington
  2. Rutgers University
  3. Brookhaven National Laboratory (BNL)
  4. Russian Research Center, Kurchatov Institute, Moscow, Russia
  5. Lawrence Berkeley National Laboratory (LBNL)
  6. University of Wisconsin, Madison
  7. ORNL
Publication Date:
Research Org.:
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:
1325444
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Journal Volume: 13; Journal Issue: 5; Conference: XXV Symposium on Nuclear Electronics and Computing - NEC'2015, Be i i, Budva, Montenegro, Montenegro, 20150928, 20151002
Country of Publication:
United States
Language:
English

Citation Formats

De, K, Jha, S, Maeno, T, Mashinistov, R., Nilsson, P, Novikov, A., Oleynik, D, Panitkin, S, Poyda, A., Ryabinkin, E., Teslyuk, A., Tsulaia, V., Velikhov, V., Wen, G., Wells, Jack C, and Wenaus, T. INTEGRATION OF PANDA WORKLOAD MANAGEMENT SYSTEM WITH SUPERCOMPUTERS. United States: N. p., 2016. Web. doi:10.1134/S1547477116050150.
De, K, Jha, S, Maeno, T, Mashinistov, R., Nilsson, P, Novikov, A., Oleynik, D, Panitkin, S, Poyda, A., Ryabinkin, E., Teslyuk, A., Tsulaia, V., Velikhov, V., Wen, G., Wells, Jack C, & Wenaus, T. INTEGRATION OF PANDA WORKLOAD MANAGEMENT SYSTEM WITH SUPERCOMPUTERS. United States. doi:10.1134/S1547477116050150.
De, K, Jha, S, Maeno, T, Mashinistov, R., Nilsson, P, Novikov, A., Oleynik, D, Panitkin, S, Poyda, A., Ryabinkin, E., Teslyuk, A., Tsulaia, V., Velikhov, V., Wen, G., Wells, Jack C, and Wenaus, T. Fri . "INTEGRATION OF PANDA WORKLOAD MANAGEMENT SYSTEM WITH SUPERCOMPUTERS". United States. doi:10.1134/S1547477116050150.
@article{osti_1325444,
title = {INTEGRATION OF PANDA WORKLOAD MANAGEMENT SYSTEM WITH SUPERCOMPUTERS},
author = {De, K and Jha, S and Maeno, T and Mashinistov, R. and Nilsson, P and Novikov, A. and Oleynik, D and Panitkin, S and Poyda, A. and Ryabinkin, E. and Teslyuk, A. and Tsulaia, V. and Velikhov, V. and Wen, G. and Wells, Jack C and Wenaus, T},
abstractNote = {Abstract The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the funda- mental nature of matter and the basic forces that shape our universe, and were recently credited for the dis- covery of a Higgs boson. 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 140 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data cen- ters are physically scattered all over the world. While PanDA currently uses more than 250000 cores with a peak performance of 0.3+ petaFLOPS, next LHC data taking runs will require more resources than Grid computing 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, Europe and Russia (in particular with Titan supercomputer at Oak Ridge Leadership Com- puting Facility (OLCF), Supercomputer at the National Research Center Kurchatov Institute , IT4 in Ostrava, and others). The current approach utilizes a 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 Titan s multi-core worker nodes. This implementation was tested with a variety of Monte-Carlo workloads on several supercomputing platforms. We will present our current accom- plishments in running PanDA WMS at supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facility s infrastructure for High Energy and Nuclear Physics, as well as other data-intensive science applications, such as bioinformatics and astro-particle physics.},
doi = {10.1134/S1547477116050150},
journal = {},
issn = {1547-4771},
number = 5,
volume = 13,
place = {United States},
year = {2016},
month = {1}
}

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Works referenced in this record:

The ATLAS PanDA Pilot in Operation
journal, December 2011


Characterization of ancient and modern genomes by SNP detection and phylogenomic and metagenomic analysis using PALEOMIX
journal, April 2014

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