15 citations · 26 across the 8 of their papers we have counts for
8 papers
Optimizing the Hit Finding Algorithm for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures
Sophie Berkman, Giuseppe Cerati, Kyle Knoepfel +5
Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is be…
Parallelizing the Unpacking and Clustering of Detector Data for Reconstruction of Charged Particle Tracks on Multi-core CPUs and Many-core GPUs
Giuseppe Cerati, Peter Elmer, Brian Gravelle +14
We present results from parallelizing the unpacking and clustering steps of the raw data from the silicon strip modules for reconstruction of charged particle tracks. Throughput is…
Speeding up Particle Track Reconstruction using a Parallel Kalman Filter Algorithm
Steven Lantz, Kevin McDermott, Michael Reid +16
One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during event…
Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter Algorithm
Giuseppe Cerati, Peter Elmer, Brian Gravelle +14
One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is finding and fitting particle tracks during event reconstruct…
Reconstruction for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures
Sophie Berkman, Giuseppe Cerati, Brian Gravelle +3
Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is be…
Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS Detector
Giuseppe Cerati, Peter Elmer, Brian Gravelle +12
In the High-Luminosity Large Hadron Collider (HL-LHC), one of the most challenging computational problems is expected to be finding and fitting charged-particle tracks during event…
Speeding up Particle Track Reconstruction in the CMS Detector using a Vectorized and Parallelized Kalman Filter Algorithm
Giuseppe Cerati, Peter Elmer, Brian Gravelle +13
Building particle tracks is the most computationally intense step of event reconstruction at the LHC. With the increased instantaneous luminosity and associated increase in pileup…
Parallelized and Vectorized Tracking Using Kalman Filters with CMS Detector Geometry and Events
Giuseppe Cerati, Peter Elmer, Brian Gravelle +13
The High-Luminosity Large Hadron Collider at CERN will be characterized by greater pileup of events and higher occupancy, making the track reconstruction even more computationally…