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Title: Coffea -- Columnar Object Framework For Effective Analysis

Abstract

The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions.

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [2];  [3];  [4]
  1. Fermilab
  2. Vanderbilt U.
  3. INFN, Trieste
  4. Princeton U.
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:
1706163
Report Number(s):
arXiv:2008.12712; FERMILAB-CONF-20-494-CMS-SCD
oai:inspirehep.net:1813893
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Conference
Resource Relation:
Conference: 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 11/04-11/08/2019
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Smith, Nicholas, Gray, Lindsey, Cremonesi, Matteo, Jayatilaka, Bo, Gutsche, Oliver, Hall, Allison, Pedro, Kevin, Acosta, Maria, Melo, Andrew, Belforte, Stefano, and Pivarski, Jim. Coffea -- Columnar Object Framework For Effective Analysis. United States: N. p., 2020. Web.
Smith, Nicholas, Gray, Lindsey, Cremonesi, Matteo, Jayatilaka, Bo, Gutsche, Oliver, Hall, Allison, Pedro, Kevin, Acosta, Maria, Melo, Andrew, Belforte, Stefano, & Pivarski, Jim. Coffea -- Columnar Object Framework For Effective Analysis. United States.
Smith, Nicholas, Gray, Lindsey, Cremonesi, Matteo, Jayatilaka, Bo, Gutsche, Oliver, Hall, Allison, Pedro, Kevin, Acosta, Maria, Melo, Andrew, Belforte, Stefano, and Pivarski, Jim. Fri . "Coffea -- Columnar Object Framework For Effective Analysis". United States. https://www.osti.gov/servlets/purl/1706163.
@article{osti_1706163,
title = {Coffea -- Columnar Object Framework For Effective Analysis},
author = {Smith, Nicholas and Gray, Lindsey and Cremonesi, Matteo and Jayatilaka, Bo and Gutsche, Oliver and Hall, Allison and Pedro, Kevin and Acosta, Maria and Melo, Andrew and Belforte, Stefano and Pivarski, Jim},
abstractNote = {The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions.},
doi = {},
url = {https://www.osti.gov/biblio/1706163}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {8}
}

Conference:
Other availability
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