11 citations · 30 across the 11 of their papers we have counts for
6 papers · 1 filter
Evaluating Portable Parallelization Strategies for Heterogeneous Architectures in High Energy Physics
Mohammad Atif, Meghna Battacharya, Paolo Calafiura +17
High-energy physics (HEP) experiments have developed millions of lines of code over decades that are optimized to run on traditional x86 CPU systems. However, we are seeing a rapid…
Portable Programming Model Exploration for LArTPC Simulation in a Heterogeneous Computing Environment: OpenMP vs. SYCL
Meifeng Lin, Zhihua Dong, Tianle Wang +6
The evolution of the computing landscape has resulted in the proliferation of diverse hardware architectures, with different flavors of GPUs and other compute accelerators becoming…
Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses
Yi Huang, Dmitrii Torbunov, Brett Viren +4
Deep learning algorithms often are trained and deployed on different datasets. Any systematic difference between the training and a test dataset may degrade the algorithm performan…
Solving Simulation Systematics in and with AI/ML
Brett Viren, Jin Huang, Yi Huang +5
Training an AI/ML system on simulated data while using that system to infer on data from real detectors introduces a systematic error which is difficult to estimate and in many ana…
DUNE Software and High Performance Computing
Bonnie Fleming, Kyle Knoepfel, Meifeng Lin +6
DUNE, like other HEP experiments, faces a challenge related to matching execution patterns of our production simulation and data processing software to the limitations imposed by m…
Porting HEP Parameterized Calorimeter Simulation Code to GPUs
Zhihua Dong, Heather Gray, Charles Leggett +3
The High Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), traditionally consume large amounts of CPU cycles for detector simulations and data ana…