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Title: HPC resource integration into CMS Computing via HEPCloud

Abstract

The higher energy and luminosity from the LHC in Run 2 have put increased pressure on CMS computing resources. Extrapolating to even higher luminosities (and thus higher event complexities and trigger rates) beyond Run 3, it becomes clear that simply scaling up the the current model of CMS computing alone will become economically unfeasible. High Performance Computing (HPC) facilities, widely used in scientific computing outside of HEP, have the potential to help fill the gap. Here we describe the U.S.CMS efforts to integrate US HPC resources into CMS Computing via the HEPCloud project at Fermilab. We present advancements in our ability to use NERSC resources at scale and efforts to integrate other HPC sites as well. We present experience in the elastic use of HPC resources, quickly scaling up use when so required by CMS workflows. We also present performance studies of the CMS multi-threaded framework on both Haswell and KNL HPC resources.

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [2]
  1. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  2. Univ. of Nebraska, Lincoln, NE (United States)
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Contributing Org.:
[CMS CERN-LHC]
OSTI Identifier:
1497404
Report Number(s):
[FERMILAB-CONF-18-630-CD]
[Journal ID: ISSN 2100-014X; oai:inspirehep.net:1722477]
Grant/Contract Number:  
[AC02-07CH11359]
Resource Type:
Accepted Manuscript
Journal Name:
EPJ Web of Conferences
Additional Journal Information:
[ Journal Volume: 214; Conference: 23rd International Conference on Computing in High Energy and Nuclear Physics, Sofia, Bulgaria, 07/09-07/13/2018]; Journal ID: ISSN 2100-014X
Publisher:
EDP Sciences
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Hufnagel, Dirk, Holzman, Burt, Mason, David, Mhashilkar, Parag, Timm, Steven, Tiradani, Anthony, Aftab Khan, Farrukh, Gutsche, Oliver, and Bloom, Kenneth. HPC resource integration into CMS Computing via HEPCloud. United States: N. p., 2019. Web. doi:10.1051/epjconf/201921403031.
Hufnagel, Dirk, Holzman, Burt, Mason, David, Mhashilkar, Parag, Timm, Steven, Tiradani, Anthony, Aftab Khan, Farrukh, Gutsche, Oliver, & Bloom, Kenneth. HPC resource integration into CMS Computing via HEPCloud. United States. doi:10.1051/epjconf/201921403031.
Hufnagel, Dirk, Holzman, Burt, Mason, David, Mhashilkar, Parag, Timm, Steven, Tiradani, Anthony, Aftab Khan, Farrukh, Gutsche, Oliver, and Bloom, Kenneth. Tue . "HPC resource integration into CMS Computing via HEPCloud". United States. doi:10.1051/epjconf/201921403031. https://www.osti.gov/servlets/purl/1497404.
@article{osti_1497404,
title = {HPC resource integration into CMS Computing via HEPCloud},
author = {Hufnagel, Dirk and Holzman, Burt and Mason, David and Mhashilkar, Parag and Timm, Steven and Tiradani, Anthony and Aftab Khan, Farrukh and Gutsche, Oliver and Bloom, Kenneth},
abstractNote = {The higher energy and luminosity from the LHC in Run 2 have put increased pressure on CMS computing resources. Extrapolating to even higher luminosities (and thus higher event complexities and trigger rates) beyond Run 3, it becomes clear that simply scaling up the the current model of CMS computing alone will become economically unfeasible. High Performance Computing (HPC) facilities, widely used in scientific computing outside of HEP, have the potential to help fill the gap. Here we describe the U.S.CMS efforts to integrate US HPC resources into CMS Computing via the HEPCloud project at Fermilab. We present advancements in our ability to use NERSC resources at scale and efforts to integrate other HPC sites as well. We present experience in the elastic use of HPC resources, quickly scaling up use when so required by CMS workflows. We also present performance studies of the CMS multi-threaded framework on both Haswell and KNL HPC resources.},
doi = {10.1051/epjconf/201921403031},
journal = {EPJ Web of Conferences},
number = ,
volume = [214],
place = {United States},
year = {2019},
month = {9}
}

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Works referenced in this record:

CMS use of allocation based HPC resources
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