7 citations · 10 across the 6 of their papers we have counts for
11 papers
Snowmass Computational Frontier: Topical Group Report on Experimental Algorithm Parallelization
G. Cerati, K. Heitmann, W. Hopkins +10
The substantial increase in data volume and complexity expected from future experiments will require significant investment to prepare experimental algorithms. These algorithms inc…
Portability: A Necessary Approach for Future Scientific Software
Meghna Bhattacharya, Paolo Calafiura, Taylor Childers +16
Today's world of scientific software for High Energy Physics (HEP) is powered by x86 code, while the future will be much more reliant on accelerators like GPUs and FPGAs. The porta…
Porting CMS Heterogeneous Pixel Reconstruction to Kokkos
Taylor Childers, Matti J. Kortelainen, Martin Kwok +2
Programming for a diverse set of compute accelerators in addition to the CPU is a challenge. Maintaining separate source code for each architecture would require lots of effort, an…
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…
Heterogeneous reconstruction of tracks and primary vertices with the CMS pixel tracker
Andrea Bocci, Matti Kortelainen, Vincenzo Innocente +2
The High-Luminosity upgrade of the LHC will see the accelerator reach an instantaneous luminosity of with an average pileup of proton-proton c…
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…