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Impacts of floating-point non-associativity on reproducibility for HPC
  and deep learning applications
v1v2 (latest)

Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications

9 August 2024
Sanjif Shanmugavelu
Mathieu Taillefumier
Christopher Culver
Oscar Hernandez
Mark Coletti
Ada Sedova
ArXiv (abs)PDFHTML

Papers citing "Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications"

2 / 2 papers shown
Title
Portability for GPU-accelerated molecular docking applications for cloud
  and HPC: can portable compiler directives provide performance across all
  platforms?
Portability for GPU-accelerated molecular docking applications for cloud and HPC: can portable compiler directives provide performance across all platforms?
M. Thavappiragasam
Weinan E
A. Sedova
16
3
0
04 Mar 2022
DeePMD-kit: A deep learning package for many-body potential energy
  representation and molecular dynamics
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Han Wang
Linfeng Zhang
Jiequn Han
E. Weinan
AI4CE
72
1,253
0
11 Dec 2017
1