high performance computing on graphics processing units: hgpu.org

hgpu.org » Compression

PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

Ning Kang, Shanzhao Qiu, Shifeng Zhang, Zhenguo Li, Shutao Xia

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Tags: Algorithms, Compression, Image processing, nVidia, Tesla V100

June 19, 2022 by hgpu

SZx: an Ultra-fast Error-bounded Lossy Compressor for Scientific Datasets

Xiaodong Yu, Sheng Di, Kai Zhao, jiannan Tian, Dingwen Tao, Xin Liang, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia A100, Tesla V100

February 6, 2022 by hgpu

Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs

Cody Rivera, Sheng Di, Jiannan Tian, Xiaodong Yu, Dingwen Tao, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, Package, Tesla V100

January 30, 2022 by hgpu

Accelerating JPEG Decompression on GPUs

André Weißenberger, Bertil Schmidt

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Tags: Compression, CUDA, Image processing, nVidia, nVidia A100

November 21, 2021 by hgpu

ndzip-gpu: Efficient Lossless Compression of Scientific Floating-Point Data on GPUs

Fabian Knorr, Peter Thoman, Thomas Fahringer

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Tags: Compression, Computer science, CUDA, nVidia, nVidia GeForce RTX 2070, nVidia GeForce RTX 3090, Package, SYCL, Tesla V100

August 8, 2021 by hgpu

cuSZ(x): Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs

Jiannan Tian, Sheng Di, Xiaodong Yu, Cody Rivera, Kai Zhao, Sian Jin, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, Heterogeneous systems, nVidia, Package, Tesla A100, Tesla V100

May 30, 2021 by hgpu

Efficient Video Compression via Content-Adaptive Super-Resolution

Mehrdad Khani, Vibhaalakshmi Sivaraman, Mohammad Alizadeh

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

April 11, 2021 by hgpu

A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression

Sian Jin, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao

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

November 22, 2020 by hgpu

Parallel acceleration of CPU and GPU range queries over large data sets

Mitchell Nelson, Zachary Sorenson, Joseph M. Myre, Jason Sawin, David Chiu

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Tags: Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080

August 9, 2020 by hgpu

Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations

Sian Jin, Pascal Grosset, Christopher M. Biwer, Jesus Pulido, Jiannan Tian, Dingwen Tao, James Ahrens

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Tags: Astrophysics, Compression, Computational Physics, Cosmology, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2080 Ti, nVidia Quadro P 6000, Package, Physics, Tesla K80, Tesla P100, Tesla V100

April 5, 2020 by hgpu

Inline Vector Compression for Computational Physics

Will Trojak, Freddie Witherden

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Tags: Compression, Computational Physics, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan V, Package, Physics, Tesla V100

March 8, 2020 by hgpu

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Sangkyun Lee, Jeonghyun Lee

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Tags: Algorithms, ARM, Compression, Computer science, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Package

May 5, 2019 by hgpu

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KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

Three Contributions to the Theory and Practice of Optimizing Compilers

HETU: a high-performance distributed deep learning system targeting large-scale and automated distributed training

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

TorchOpt: an efficient library for differentiable optimization built upon PyTorch

TorchOpt: An Efficient Library for Differentiable Optimization

NaturalCC: An Open-Source Toolkit for Code Intelligence

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

mt4g: Memory Topology for GPUs

Capturing the Memory Topology of GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

SZx: an Ultra-fast Error-bounded Lossy Compressor for Scientific Datasets

Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs

Accelerating JPEG Decompression on GPUs

ndzip-gpu: Efficient Lossless Compression of Scientific Floating-Point Data on GPUs

cuSZ(x): Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs

Efficient Video Compression via Content-Adaptive Super-Resolution

A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression

Parallel acceleration of CPU and GPU range queries over large data sets

Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations

Inline Vector Compression for Computational Physics

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Recent source codes

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

HETU: a high-performance distributed deep learning system targeting large-scale and automated distributed training

SYCL-DNN: a library implementing neural network algorithms written using SYCL

SW#SYCL

Flink-TornadoVM-Artifact

StateMover: a checkpoint-based debugging framework for FPGAs

Kernel Tuner

TorchOpt: an efficient library for differentiable optimization built upon PyTorch

NaturalCC: An Open-Source Toolkit for Code Intelligence

mt4g: Memory Topology for GPUs

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