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Title: A Parallelised ROOT for Future HEP Data Processing

Abstract

In the coming years, HEP data processing will need to exploit parallelism on present and future hardware resources to sustain the bandwidth requirements. As one of the cornerstones of the HEP software ecosystem, ROOT embraced an ambitious parallelisation plan which delivered compelling results. In this contribution the strategy is characterised as well as its evolution in the medium term. The units of the ROOT framework are discussed where task and data parallelism have been introduced, with runtime and scaling measurements. We will give an overview of concurrent operations in ROOT, for instance in the areas of I/O (reading and writing of data), fitting / minimization, and data analysis. This paper introduces the programming model and use cases for explicit and implicit parallelism, where the former is explicit in user code and the latter is implicitly managed by ROOT internally.

Authors:
 [1]; ORCiD logo [2];  [1];  [3];  [1];  [4];  [1]
  1. European Organization for Nuclear Research (CERN), Geneva (Switzerland)
  2. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  3. European Organization for Nuclear Research (CERN), Geneva (Switzerland); Univ. of Oldenburg, Oldenburg (Germany)
  4. European Organization for Nuclear Research (CERN), Geneva (Switzerland); Jaume I Univ., Castelló de la Plana, Valencia (Spain)
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1574961
Report Number(s):
FERMILAB-CONF-19-551-SCD
Journal ID: ISSN 2100-014X; oai:inspirehep.net:1761253; TRN: US2001155
Grant/Contract Number:  
AC02-07CH11359
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
EPJ Web of Conferences
Additional Journal Information:
Journal Volume: 214; Conference: 23. International Conference on Computing in High Energy and Nuclear Physics, Sofia (Bulgaria), 9-13 Jul 2018; Journal ID: ISSN 2100-014X
Publisher:
EDP Sciences
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Piparo, Danilo, Canal, Philippe, Amadio, Guilherme, Guiraud, Enrico, Naumann, Axel, Valls, Xavier, and Tejedor, Enric. A Parallelised ROOT for Future HEP Data Processing. United States: N. p., 2019. Web. doi:10.1051/epjconf/201921405033.
Piparo, Danilo, Canal, Philippe, Amadio, Guilherme, Guiraud, Enrico, Naumann, Axel, Valls, Xavier, & Tejedor, Enric. A Parallelised ROOT for Future HEP Data Processing. United States. https://doi.org/10.1051/epjconf/201921405033
Piparo, Danilo, Canal, Philippe, Amadio, Guilherme, Guiraud, Enrico, Naumann, Axel, Valls, Xavier, and Tejedor, Enric. Tue . "A Parallelised ROOT for Future HEP Data Processing". United States. https://doi.org/10.1051/epjconf/201921405033. https://www.osti.gov/servlets/purl/1574961.
@article{osti_1574961,
title = {A Parallelised ROOT for Future HEP Data Processing},
author = {Piparo, Danilo and Canal, Philippe and Amadio, Guilherme and Guiraud, Enrico and Naumann, Axel and Valls, Xavier and Tejedor, Enric},
abstractNote = {In the coming years, HEP data processing will need to exploit parallelism on present and future hardware resources to sustain the bandwidth requirements. As one of the cornerstones of the HEP software ecosystem, ROOT embraced an ambitious parallelisation plan which delivered compelling results. In this contribution the strategy is characterised as well as its evolution in the medium term. The units of the ROOT framework are discussed where task and data parallelism have been introduced, with runtime and scaling measurements. We will give an overview of concurrent operations in ROOT, for instance in the areas of I/O (reading and writing of data), fitting / minimization, and data analysis. This paper introduces the programming model and use cases for explicit and implicit parallelism, where the former is explicit in user code and the latter is implicitly managed by ROOT internally.},
doi = {10.1051/epjconf/201921405033},
url = {https://www.osti.gov/biblio/1574961}, journal = {EPJ Web of Conferences},
issn = {2100-014X},
number = ,
volume = 214,
place = {United States},
year = {2019},
month = {9}
}

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Works referenced in this record:

Apache Spark: a unified engine for big data processing
journal, October 2016


NVIDIA cuda software and gpu parallel computing architecture
conference, January 2007