high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Image and Signal Processing

CUDA-Accelerated ODETLAP: A Parallel Lossy Compression Implementation

Daniel N. Benedetti, W. Randolph Franklin, Wenli Li

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Tags: Algorithms, Compression, CUDA, Differential equations, Image processing, nVidia, Partial differential equations, PDEs, Thrust

October 10, 2013 by hgpu

3D Non-Local Means denoising via multi-GPU

Giuseppe Palma, Francesco Piccialli, Pasquale De Michele, Salvatore Cuomo, Marco Comerci, Pasquale Borrelli, Bruno Alfano

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Tags: Algorithms, CUDA, Filtering, Image processing, nVidia, Signal denoising, Tesla S2050

October 4, 2013 by hgpu

CUDA Enhanced Filtering in a Pipelined Video Processing Framework

Austin Aaron Dworaczyk Wiltshire

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Tags: CUDA, Filtering, Image processing, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX Titan, Thesis

October 2, 2013 by hgpu

Parallel Hyperspectral Unmixing on GPUs

Jose M. P. Nascimento, Jose M. Bioucas-Dias, Jose M. Rodriguez Alves, Vitor Silva, Antonio Plaza

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Tags: Algorithms, Image processing, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 680, OpenCL, Tesla C2050

October 2, 2013 by hgpu

GPU-Accelerated Real-Time Surveillance De-Weathering

Niklas Pettersson

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Tags: Computer science, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 680, Thesis

October 1, 2013 by hgpu

Re-Introduction of Communication-Avoiding FMM-Accelerated FFTs with GPU Acceleration

M. Harper Langston, Muthu Baskaran, Benoit Meister, Nicolas Vasilache, Richard Lethin

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Tags: Algorithms, CUDA, Fast multipole method, FFT, MPI, nVidia, Signal processing, Tesla M2070

September 29, 2013 by hgpu

Multi-Scale, Multi-Level, Heterogeneous Features Extraction and Classification of Volumetric Medical Images

Shuai Li, Qinping Zhao, Shengfa Wang, Aimin Hao, Hong Qin

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Tags: Algorithms, CUDA, Diffusion tensor, Image processing, nVidia, nVidia GeForce GTX 330 M

September 27, 2013 by hgpu

Automatic Software Synthesis from High-Level ForSyDe Models Targeting Massively Parallel Processors

George Ungureanu

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Heterogeneous systems, Image processing, nVidia, Thesis

September 25, 2013 by hgpu

Investigating the Performance of Motion Estimation Block-Matching Algorithms on GPU Cards

Eralda Nishani, Betim Cico, Neki Frasheri

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Tags: CUDA, Image processing, nVidia, nVidia Quadro 400

September 22, 2013 by hgpu

Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs

Zebin Wu, Shun Ye, Jie Wei, Zhihui Wei, Le Sun, Jianjun Liu

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Tags: CUDA, Image processing, nVidia, nVidia Quadro 600, Sensing, Tesla C2050

September 22, 2013 by hgpu

Paralleling Variable Block Size Motion Estimation of HEVC on Multi- Core CPU Plus GPU Platform

Xiang-wen Wang, Li Song, Min Chen, Jun-jie Yang

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Tags: Compression, CUDA, H.264/AVC, Image processing, Motion compensation, nVidia, Tesla C2050

September 22, 2013 by hgpu

Performing DCT8x8 Computation on GPU Using NVIDIA CUDA Technology

Jagdamb Behari Srivastava, R. B. Singh, Jitendra Jain

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Tags: CUDA, Image processing, nVidia, nVidia GeForce GT 630 M

September 21, 2013 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

CUDA-Accelerated ODETLAP: A Parallel Lossy Compression Implementation

3D Non-Local Means denoising via multi-GPU

CUDA Enhanced Filtering in a Pipelined Video Processing Framework

Parallel Hyperspectral Unmixing on GPUs

GPU-Accelerated Real-Time Surveillance De-Weathering

Re-Introduction of Communication-Avoiding FMM-Accelerated FFTs with GPU Acceleration

Multi-Scale, Multi-Level, Heterogeneous Features Extraction and Classification of Volumetric Medical Images

Automatic Software Synthesis from High-Level ForSyDe Models Targeting Massively Parallel Processors

Investigating the Performance of Motion Estimation Block-Matching Algorithms on GPU Cards

Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs

Paralleling Variable Block Size Motion Estimation of HEVC on Multi- Core CPU Plus GPU Platform

Performing DCT8x8 Computation on GPU Using NVIDIA CUDA Technology

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Most viewed papers (last 30 days)