264 citations · 1.2k across the 45 of their papers we have counts for
10 papers · 1 filter
FPGAs-as-a-Service Toolkit (FaaST)
Dylan Sheldon Rankin, Jeffrey Krupa, Philip Harris +13
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically a…
GPU-accelerated machine learning inference as a service for computing in neutrino experiments
Michael Wang, Tingjun Yang, Maria Acosta Flechas +7
Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algori…
HL-LHC Computing Review: Common Tools and Community Software
HEP Software Foundation, :, Thea Aarrestad +107
Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will b…
Coffea -- Columnar Object Framework For Effective Analysis
Nicholas Smith, Lindsey Gray, Matteo Cremonesi +8
The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility…
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta +19
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering…
GPU coprocessors as a service for deep learning inference in high energy physics
Jeffrey Krupa, Kelvin Lin, Maria Acosta Flechas +13
In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limi…
GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP
G. Amadio, A. Ananya, J. Apostolakis +39
Full detector simulation was among the largest CPU consumer in all CERN experiment software stacks for the first two runs of the Large Hadron Collider (LHC). In the early 2010's, t…
Integration and Performance of New Technologies in the CMS Simulation
Kevin Pedro
The HL-LHC and the corresponding detector upgrades for the CMS experiment will present extreme challenges for the full simulation. In particular, increased precision in models of p…
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Xiangyang Ju, Steven Farrell, Paolo Calafiura +20
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and…
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
Giuseppe Di Guglielmo, Javier Duarte, Philip Harris +13
We present the implementation of binary and ternary neural networks in the hls4ml library, designed to automatically convert deep neural network models to digital circuits with FPG…