high performance computing on graphics processing units: hgpu.org

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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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Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

On the Compilation Performance of Current SYCL Implementations

M1 GPUs for (scientific) computations in C++

Seamless GPU acceleration for C++ based physics with the Metal Shading Language on Apple’s M series unified chips

CUB: Cooperative primitives for CUDA C++

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Simulator and workload generator for Edge-to-Cloud environments

FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

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

Rodinia Benchmarks for DPC++

DCA++: a state of the art implementation of the dynamical cluster approximation

tntorch - Tensor Network Learning with PyTorch

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

M1 GPUs for (scientific) computations in C++

CUB: Cooperative primitives for CUDA C++

Simulator and workload generator for Edge-to-Cloud environments

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