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Mixing Low-Precision Formats in Multiply-Accumulate Units for DNN Training

Mariko Tatsumi, Silviu-Ioan Filip, Caroline White, Olivier Sentieys, Guy Lemieux

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Tags: Computer science, CUDA, Deep learning, FPGA, nVidia, Precision

December 11, 2022 by hgpu

SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems

Jiangsu Du, Dongsheng Li, Yingpeng Wen, Jiazhi Jiang, Dan Huang, Xiangke Liao, Yutong Lu

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, HPC, nVidia, PyTorch, Tesla V100

December 11, 2022 by hgpu

Fast convolution kernels on pascal GPU with high memory efficiency

Qiong Chang, Masaki Onishi, Tsutomu Maruyama

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Tags: CNN, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Performance

December 4, 2022 by hgpu

Efficient Incremental Text-to-Speech on GPUs

Muyang Du, Chuan Liu, Jiaxing Qi, Junjie Lai

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Tags: Computer science, Deep learning, Machine learning, nVidia, nVidia A10, Tesla V100

December 4, 2022 by hgpu

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Xupeng Miao, Yujie Wang, Youhe Jiang, Chunan Shi, Xiaonan Nie, Hailin Zhang, Bin Cui

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Tags: Computer science, CUDA, Databases, Deep learning, nVidia, nVidia Titan RTX, Package

December 4, 2022 by hgpu

User-Driven Online Kernel Fusion for SYCL

Victor Perez, Lukas Sommer, Victor Lomüller, Kumudha Narasimhan, Mehdi Goli

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Tags: Compilers, Computer science, Deep learning, OpenCL, Package, SYCL

November 27, 2022 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

Training a Vision Transformer from scratch in less than 24 hours with 1 GPU

Saghar Irandoust, Thibaut Durand, Yunduz Rakhmangulova, Wenjie Zi, Hossein Hajimirsadeghi

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Tags: Benchmarking, Computer science, Computer vision, Deep learning

November 20, 2022 by hgpu

TorchOpt: An Efficient Library for Differentiable Optimization

Jie Ren, Xidong Feng, Bo Liu, Xuehai Pan, Yao Fu, Luo Mai, Yaodong Yang

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Tags: Computer science, CUDA, Deep learning, Mathematical Software, nVidia, OpenMP, Package, PyTorch

November 20, 2022 by hgpu

A Study on Neural-based Code Summarization in Low-resource Settings

Yang He

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Tags: Computer science, CUDA, Deep learning, nVidia, PyTorch, Software Engineering, Tesla V100, Thesis

November 13, 2022 by hgpu

iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

Fei Xu, Jianian Xu, Jiabin Chen, Li Chen, Ruitao Shang, Zhi Zhou, Fangming Liu

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Tags: Cloud, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla V100

November 13, 2022 by hgpu

Enabling Data Movement and Computation Pipelining in Deep Learning Compiler

Guyue Huang, Yang Bai, Liu Liu, Yuke Wang, Bei Yu, Yufei Ding, Yuan Xie

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia A100

November 6, 2022 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Mixing Low-Precision Formats in Multiply-Accumulate Units for DNN Training

SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems

Fast convolution kernels on pascal GPU with high memory efficiency

Efficient Incremental Text-to-Speech on GPUs

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

User-Driven Online Kernel Fusion for SYCL

Training a Vision Transformer from scratch in less than 24 hours with 1 GPU

TorchOpt: An Efficient Library for Differentiable Optimization

A Study on Neural-based Code Summarization in Low-resource Settings

Enabling Data Movement and Computation Pipelining in Deep Learning Compiler

Recent source codes

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Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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