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hgpu.org » nVidia DGX-A100

A Deep Learning Model for Loop Interchange

Lina Mezdour, Khadidja Kadem, Massinissa Merouani, Amina Selma Haichour, Saman Amarasinghe, Riyadh Baghdadi

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Tags: Compilers, Computer science, Deep learning, nVidia, nVidia DGX-A100, Package

March 12, 2023 by hgpu

SciAI4Industry – Solving PDEs for industry-scale problems with deep learning

Philipp A. Witte, Russell J. Hewett, Kumar Saurabh, AmirHossein Sojoodi, Ranveer Chandra

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Tags: AI, Cloud, Computational Physics, Computer science, Deep learning, Differential equations, Neural networks, nVidia, nVidia A100, nVidia DGX-A100, Partial differential equations, PDEs

November 27, 2022 by hgpu

KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks

J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian Foster, Zhao Zhang

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia A100, nVidia DGX-A100, Package, Tesla V100

July 11, 2021 by hgpu

tcFFT: Accelerating Half-Precision FFT through Tensor Cores

Binrui Li, Shenggan Cheng, James Lin

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Tags: Computer science, FFT, Mixed precision, nVidia, nVidia DGX-2, nVidia DGX-A100, Tesla V100

May 2, 2021 by hgpu

Efficient Large-Scale Language Model Training on GPU Clusters

Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia

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Tags: Computer science, CUDA, Data parallelism, Deep learning, GPU cluster, NLP, nVidia, nVidia DGX-A100, Package

April 18, 2021 by hgpu

Tinker-HP: Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems

Olivier Adjoua, Louis Lagardère, Luc-Henri Jolly, Arnaud Durocher, Thibaut Very, Isabelle Dupays, Zhi Wang, Théo Jaffrelot Inizan, Frédéric Célerse, Pengyu Ren, Jay W. Ponder, Jean-Philip Piquemal

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Tags: Biophysics, Computational Physics, CUDA, Molecular dynamics, nVidia, nVidia DGX-A100, OpenACC, Package, Physics, Tesla V100

November 8, 2020 by hgpu

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

high performance computing on graphics processing units: hgpu.org

A Deep Learning Model for Loop Interchange

KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks

tcFFT: Accelerating Half-Precision FFT through Tensor Cores

Efficient Large-Scale Language Model Training on GPU Clusters

Tinker-HP: Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems

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