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

19 May 2021, 11:29
13m
Short Talk Offline Computing Artificial Intelligence

Speaker

Davide Di Croce (University of Alabama (US))

Description

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.

Primary authors

Michael Andrews (Carnegie-Mellon University (US)) Bjorn Burkle (Brown University (US)) Shravan Sunil Chaudhari Davide Di Croce (University of Alabama (US)) Sergei Gleyzer (University of Alabama (US)) Ulrich Heintz (Brown University (US)) Meenakshi Narain (Brown University (US)) Manfred Paulini (Carnegie-Mellon University (US)) Emanuele Usai (Brown University (US))

Presentation materials

Proceedings

Paper