Apr 20 – 30, 2020
Virtual/Digital only workshop
Europe/Paris timezone

Parallelizing the unpacking and clustering of detector data for reconstruction of charged particle tracks on multi-core CPUs and many-core GPUs

Apr 22, 2020, 9:25 PM
Virtual/Digital only workshop

Virtual/Digital only workshop


Bei Wang (Princeton University (US))


Charged particle tracking is the most computationally intensive step of event reconstruction at the LHC. Due to the computational cost, the current CMS online High Level Trigger only performs track reconstruction in detector regions of interest identified by the hardware trigger or other detector elements. We have made significant progress towards developing a parallelized and vectorized implementation of the combinatoric Kalman filter algorithm for track building that would allow efficient global reconstruction of the entire event within the projected online CPU budget. Global reconstruction necessarily entails the unpacking and clustering of the hit information from all the silicon strip tracker modules; however, currently only modules selected by the regional reconstruction are processed. Therefore, we have recently begun to investigate improving the efficiency of the unpacking and clustering steps.

In this talk, we report recent progress in the integration of the Kalman filter track builder mkFit with the CMS data processing framework, and improvements in its track building physics performance. We present results from parallelizing the unpacking and clustering steps of the raw data from the silicon strip modules so that all the required hit information for global reconstruction can be produced efficiently. We include performance evaluations of the new unpacking and clustering algorithms on Intel Xeon and NVIDIA GPU architectures, along with updated track building performance results on Intel Xeon.

Second most appropriate track (if necessary) Enhanced performance of tracking algorithms
Consider for young scientist forum (Student or postdoc speaker) No

Primary authors

Steven R Lantz (Cornell University (US)) Kevin McDermott (Cornell University (US)) Tres Reid (Cornell University (US)) Daniel Sherman Riley (Cornell University (US)) Peter Wittich (Cornell University (US)) Giuseppe Cerati (Fermi National Accelerator Lab. (US)) Allison Reinsvold Hall (Fermilab) Matti Kortelainen (Fermi National Accelerator Lab. (US)) Peter Elmer (Princeton University (US)) Bei Wang (Princeton University (US)) Slava Krutelyov (Univ. of California San Diego (US)) Mario Masciovecchio (Univ. of California San Diego (US)) Matevz Tadel (Univ. of California San Diego (US)) Frank Wurthwein (UCSD) Avi Yagil (Univ. of California San Diego (US)) Brian Gravelle (University of Oregon) Boyana Norris (University of Oregon)

Presentation materials

Peer reviewing