high performance computing on graphics processing units: hgpu.org

hgpu.org » Compression

A Highly Efficient GPU-CPU Hybrid Parallel Implementation of Sparse LU Factorization

Liu Li, Liu Li, Yang Guangwen

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Tags: Algorithms, Compression, Computer science, CUDA, Factorization, nVidia, Sparse matrix, Tesla C1060

January 8, 2012 by hgpu

Artifact-Free JPEG Decompression with Total Generalized Variation

Kristian Bredies, Martin Holler

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Tags: Algorithms, Compression, CUDA, Image processing, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 580, nVidia Quadro FX 3700

January 6, 2012 by hgpu

Hyper neural network on OpenCL

Frantisek Sindelar

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Tags: Algorithms, Compression, Computer science, Genetic programming, Neural networks, nVidia, nVidia Quadro NVS 140 M, OpenCL, Thesis

January 6, 2012 by hgpu

Lossless Compression of Variable-Precision Floating-Point Buffers on GPUs

Jeff Pool, Anselmo Lastra, Montek Singh

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Tags: Compression, Computer science, Programming techniques

January 6, 2012 by hgpu

Parallel Implementation of Compressive Sensing Based SAR Imaging with GPU

Tian Jihua, Sun Jinping, Zhang Yuxi, Najeeb Ahmad, Zhang Bingchen

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Tags: Algorithms, Compression, CUDA, Image processing, nVidia, Sensing, Tesla C1060

January 4, 2012 by hgpu

Parallelism, Patterns, and Performance in Iterative MRI Reconstruction

Mark Murphy

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Tags: Algorithms, Compression, CUDA, FFT, Image processing, Magnetic resonance imaging, Medicine, MRI, nVidia, nVidia GeForce GTX 580, Package, Thesis, Wavelet

December 29, 2011 by hgpu

Efficient parallel lists intersection and index compression algorithms using graphics processing units

Naiyong Ao, Fan Zhang, Di Wu, Douglas S. Stones, Gang Wang, Xiaoguang Liu, Jing Liu, Sheng Lin

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Tags: Algorithms, Compression, Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX 480, Performance

December 28, 2011 by hgpu

Simple Geometry Compression for Ray Tracing on GPU

Kirill Garanzha, Alexander Bely, Vladimir Galaktionov

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Tags: Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Raytracing

December 26, 2011 by hgpu

Designing Fast Architecture Sensitive Tree Search on Modern Multi-Core/Many-Core Processors

Changkyu Kim, Jatin Chhugani, Nadathur Satish, Eric Sedlar, Anthony D. Nguyen, Tim Kaldewey, Victor W. Lee, Scott A. Brandt, Pradeep Dubey

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Tags: Compression, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 280, Search, Tesla C2050

December 25, 2011 by hgpu

GPU implementation of JPEG2000 for hyperspectral image compression

Milosz Ciznicki, Krzysztof Kurowski, Antonio Plaza

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Tags: Algorithms, Compression, CUDA, Image processing, JPEG2000, nVidia, nVidia GeForce GTX 480

December 17, 2011 by hgpu

Domain-Specific Optimizations Supporting Real-Time Image Compression

Jiri Matela

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Tags: Compression, CUDA, Image processing, nVidia, nVidia GeForce GTX 580, Optimization, Thesis

November 28, 2011 by hgpu

Decryption-decompression of AES protected ZIP files on GPUs

Tan Nhat Duong, Phong Hong Pham, Duc Huu Nguyen, Thuy Thanh Nguyen, Hung Duc Le

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Tags: AES, Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Security

November 25, 2011 by hgpu

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

See all packages

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high performance computing on graphics processing units: hgpu.org

A Highly Efficient GPU-CPU Hybrid Parallel Implementation of Sparse LU Factorization

Artifact-Free JPEG Decompression with Total Generalized Variation

Hyper neural network on OpenCL

Lossless Compression of Variable-Precision Floating-Point Buffers on GPUs

Parallel Implementation of Compressive Sensing Based SAR Imaging with GPU

Parallelism, Patterns, and Performance in Iterative MRI Reconstruction

Efficient parallel lists intersection and index compression algorithms using graphics processing units

Simple Geometry Compression for Ray Tracing on GPU

Designing Fast Architecture Sensitive Tree Search on Modern Multi-Core/Many-Core Processors

GPU implementation of JPEG2000 for hyperspectral image compression

Domain-Specific Optimizations Supporting Real-Time Image Compression

Decryption-decompression of AES protected ZIP files on GPUs

Recent source codes

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

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