hgpu.org » Exa.TrkX
Xiangyang Ju, Daniel Murnane, Paolo Calafiura, Nicholas Choma, Sean Conlon, Steve Farrell, Yaoyuan Xu, Maria Spiropulu, Jean-Roch Vlimant, Adam Aurisano, Jeremy Hewes, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Markus Atkinson, Mark Neubauer, Gage DeZoort, Savannah Thais, Aditi Chauhan, Alex Schuy, Shih-Chieh Hsu, Alex Ballow, Alina Lazar
Tags: Algorithms, CUDA, Deep learning, Exa.TrkX, HEP, Neural networks, nVidia, Package, Physics, Tesla A100, Tesla V100
March 21, 2021 by hgpu
Recent source codes
* * *
Most viewed papers (last 30 days)
- Asynchronous-Many-Task Systems: Challenges and Opportunities - Scaling an AMR Astrophysics Code on Exascale machines using Kokkos and HPX
- Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads
- A comparison of HPC-based quantum computing simulators using Quantum Volume
- HPC-Coder-V2: Studying Code LLMs Across Low-Resource Parallel Languages
- A survey on FPGA-based accelerator for ML models
- CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection
- TorchQC - A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control
- Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations
- Finding Missed Code Size Optimizations in Compilers using LLMs
- Utilizing Tensor Cores in Futhark
* * *