| Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
|---|---|---|
| Article Number | 09006 | |
| Number of page(s) | 7 | |
| Section | 9 - Exascale Science | |
| DOI | https://doi.org/10.1051/epjconf/202024509006 | |
| Published online | 16 November 2020 | |
https://doi.org/10.1051/epjconf/202024509006
Single-Pass Covariance Matrix Calculation on a Hybrid FPGA/CPU Platform
1
Columbia University, formerly ETH Zurich
2
ETH Zurich
* e-mail: lukas.arnold@cern.ch
Published online: 16 November 2020
Covariance matrices are used for a wide range of applications in particle physics, including Kálmán filter for tracking purposes or Primary Component Analysis for dimensionality reduction. Based on a novel decomposition of the covariance matrix, a design that requires only one pass of data for calculating the covariance matrix is presented. Two computation engines are used depending on parallelizability of the necessary computation steps. The design is implemented onto a hybrid FPGA/CPU system and yields speed-up of up to 5 orders of magnitude compared to previous FPGA implementation.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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