high performance computing on graphics processing units: hgpu.org

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CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems

Chao Chen, Chris Porter, Santosh Pande

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Tags: Compilers, Computer science, CUDA, nVidia, Package, Task scheduling, Tesla P100, Tesla V100

May 1, 2022 by hgpu

Query Processing on Tensor Computation Runtimes

Dong He, Supun Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, Matteo Interlandi

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Tags: Benchmarking, Cloud, Computer science, CUDA, Databases, Machine learning, nVidia, Package, Tesla P100

March 6, 2022 by hgpu

Lightning: Scaling the GPU Programming Model Beyond a Single GPU

Stijn Heldens, Pieter Hijma, Ben van Werkhoven, Jason Maassen, Rob V. van Nieuwpoort

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Tags: Computer science, CUDA, Distributed computing, nVidia, OpenCL, Package, Performance, Tesla P100

February 20, 2022 by hgpu

The Ecological Footprint of Neural Machine Translation Systems

Dimitar Sherionov, Eva Vanmassenhove

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, nVidia GeForce GTX 1080 Ti, Package, Tesla P100

February 13, 2022 by hgpu

Porting OpenACC to OpenMP on heterogeneous systems

Hichan Agueny

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Tags: Computer science, Heterogeneous systems, Laplace and Poisson equation, nVidia, OpenACC, Tesla P100

February 6, 2022 by hgpu

Building a Performance Model for Deep Learning Recommendation Model Training on GPUs

Zhongyi Lin, Louis Feng, Ehsan K. Ardestani, Jaewon Lee, John Lundell, Changkyu Kim, Arun Kejariwal, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan XP, Package, Performance, Tesla P100, Tesla V100

January 23, 2022 by hgpu

Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment

Hulin Dai, Xuan Peng, Xuanhua Shi, Ligang He, Qian Xiong, Hai Jin

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Neural networks, nVidia, Performance, PyTorch, TensorFlow, Tesla P100

January 9, 2022 by hgpu

GPU-accelerated Faster Mean Shift with euclidean distance metrics

Le You, Han Jiang, Jinyong Hu, Chorng Chang, Lingxi Chen, Xintong Cui, Mengyang Zhao

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Tags: Algorithms, Clustering, CUDA, Image processing, nVidia, nVidia GeForce RTX 3070, nVidia GeForce RTX 3080 Ti, nVidia Quadro P 5000, Package, Pattern recognition, Tesla P100

January 2, 2022 by hgpu

On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

Moritz Lehmann, Mathias J. Krause, Giorgio Amati, Marcello Sega, Jens Harting, Stephan Gekle

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Tags: AMD Radeon Instinct MI100, AMD Radeon VI, ATI, Fluid dynamics, lattice Boltzmann, Mixed precision, nVidia, OpenCL, Tesla K20, Tesla K40, Tesla K80, Tesla P100, Tesla V100

December 19, 2021 by hgpu

A Variant RSA Acceleration with Parallelization

Jun-jie Liu, Kang-Too Tsang, Yu-Hui Deng

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Tags: Computer science, CUDA, nVidia, RSA, Security, Tesla P100

November 28, 2021 by hgpu

Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

Zhengda Bian, Hongxin Liu, Boxiang Wang, Haichen Huang, Yongbin Li, Chuanrui Wang, Fan Cui, Yang You

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Tags: Computer science, CUDA, Data parallelism, Deep learning, nVidia, Package, Tesla P100

October 31, 2021 by hgpu

Least Squares on GPUs in Multiple Double Precision

Jan Verschelde

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Tags: Code generation, Computer science, CUDA, Mathematical Software, nVidia, Package, Tesla P100, Tesla V100

October 24, 2021 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

CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems

Lightning: Scaling the GPU Programming Model Beyond a Single GPU

The Ecological Footprint of Neural Machine Translation Systems

Porting OpenACC to OpenMP on heterogeneous systems

Building a Performance Model for Deep Learning Recommendation Model Training on GPUs

Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment

GPU-accelerated Faster Mean Shift with euclidean distance metrics

On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

A Variant RSA Acceleration with Parallelization

Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

Least Squares on GPUs in Multiple Double Precision

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

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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