Title: Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data

Journal Article · · EPJ Web of Conferences (Online)
 [1];  [2];  [3];  [4];  [4];  [2];  [2];  [5];  [2]
  1. Carnegie Mellon Univ., Pittsburgh, PA (United States); Carnegie Mellon University, Pittsburgh, PA 15213
  2. Brown Univ., Providence, RI (United States)
  3. Birla Institute of Technology and Science (BITS) Pilani, Goa (India)
  4. Univ. of Alabama, Tuscaloosa, AL (United States)
  5. Carnegie Mellon Univ., Pittsburgh, PA (United States)
+ Show Author Affiliations

Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments benefit from the use of machine learning. Incorporating these computational tools in the experimental framework presents new challenges. This paper reports on the implementation of the end-to-end deep learning with the CMS software framework and the scaling of the end-to-end deep learning with multiple GPUs. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation for particle and event identification. We demonstrate the end-to-end implementation on a top quark benchmark and perform studies with various hardware architectures including single and multiple GPUs and Google TPU.

Research Organization:
Carnegie Mellon Univ., Pittsburgh, PA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
Grant/Contract Number:
SC0010118
OSTI ID:
1837660
Journal Information:
EPJ Web of Conferences (Online), Journal Name: EPJ Web of Conferences (Online) Vol. 251; ISSN 2100-014X
Publisher:
EDP SciencesCopyright Statement
Country of Publication:
United States
Language:
English

References (5)

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journal October 2020
The anti- k t jet clustering algorithm journal April 2008
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Advances in flash memory SSD technology for enterprise database applications
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conference June 2009

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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS