EPJ Web Conf.
Volume 127, 2016Connecting the Dots 2016
|Number of page(s)||8|
|Published online||15 November 2016|
Kalman Filter Tracking on Parallel Architectures
1 UC San Diego, La Jolla, CA, USA
2 Princeton University, Princeton, NJ, USA
3 Cornell University, Ithaca, NY, USA
a e-mail: email@example.com
b e-mail: firstname.lastname@example.org
c e-mail: email@example.com
d e-mail: firstname.lastname@example.org
e e-mail: email@example.com
f e-mail: firstname.lastname@example.org
g e-mail: email@example.com
h e-mail: firstname.lastname@example.org
i e-mail: email@example.com
j e-mail: firstname.lastname@example.org
k e-mail: email@example.com
Published online: 15 November 2016
Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. In order to achieve the theoretical performance gains of these processors, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-LHC), for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are those based on a Kalman filter approach. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust, and are in use today at the LHC. Given the utility of the Kalman filter in track finding, we have begun to port these algorithms to parallel architectures, namely Intel Xeon and Xeon Phi. We report here on our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a simplified experimental environment.
© The Authors, published by EDP Sciences, 2016
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.