| Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
|---|---|---|
| Article Number | 06026 | |
| Number of page(s) | 8 | |
| Section | T6 - Machine learning & analysis | |
| DOI | https://doi.org/10.1051/epjconf/201921406026 | |
| Published online | 17 September 2019 | |
https://doi.org/10.1051/epjconf/201921406026
Columnar data processing for HEP analysis
1
Princeton University
2
National Institute of Technology,
Silchar,
India
Published online: 17 September 2019
In the last stages of data analysis, physicists are often forced to choose between simplicity and execution speed. In High Energy Physics (HEP), high-level languages like Python are known for ease of use but also very slow execution. However, Python is used in speed-critical data analysis in other fields of science and industry. In those fields, most operations are performed on Numpy arrays in an array programming style; this style can be adopted for HEP by introducing variable-sized, nested data structures. We describe how array programming may be extended for HEP use-cases and an implementation known as awkward-array. We also present integration with ROOT, Apache Arrow, and Parquet, as well as preliminary performance results.
© The Authors, published by EDP Sciences, 2019
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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.