high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Image and Signal Processing » Image processing

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

Parallel Implementation Algorithm of Motion Estimation for GPU Applications

Tian Song, Masashi Koshino, Yuya Matsunohana, Takashi Shimamoto

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Tags: Algorithms, CUDA, H.264/AVC, Image processing, nVidia, nVidia GeForce GTX 285

January 4, 2012 by hgpu

Parallelism, Patterns, and Performance in Iterative MRI Reconstruction

Mark Murphy

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Source codes

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

Multilevel Tile Load Map on Massive Terrain Visualization

Junlan Nie, Dongliang Guo, Yanfen Wang, Lingfu Kong, Yong Tang

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Tags: Algorithms, Computer science, GLSL, Image processing, nVidia, nVidia GeForce GT 240 M, OpenGL, Optimization, Rendering, Visualization

December 28, 2011 by hgpu

GPU-Parallel Implementation of Color based Medical Image Retrieval in Compressed Domain

Kuldeep Yadav, Avi Srivastava, M. A. Ansari

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce 9500 GT

December 25, 2011 by hgpu

Parallel Implementation of Shape based Image Retrieval Approach on CUDA in Compressed Domain

Kuldeep Yadav, Avi Srivastava, Ankush Mittal, M. A. Ansari

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce 9500 GT

December 25, 2011 by hgpu

A GPU-supported High-Level Programming Language for Image Processing

Ami Ono, Katsuhiko Kondo, Takafumi Inaba, Tomoaki Tsumura, Hiroshi Matsuo

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Tags: CUDA, Data parallelism, High-level Languages, Image processing, nVidia, nVidia GeForce GTX 280

December 25, 2011 by hgpu

Image processing algorithm optimization with CUDA for Pure Data

Rudi Giot, Abilio Rodrigues e Sousa

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Tags: Algorithm optimization, Algorithms, CUDA, Image processing, nVidia, Optimization

December 24, 2011 by hgpu

Improving the usability of hierarchical representations for interactively labeling large image data sets

Julia Moehrmann, Stefan Bernstein, Thomas Schlegel, Gunter Werner, Gunther Heidemann

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Tags: ATI, ATI Mobility Radeon HD 4200, Clustering, Image processing, Image recognition, nVidia, nVidia GeForce 9600 M GT, Self-organizing map, Visualization

December 22, 2011 by hgpu

A GPU framework for parallel segmentation of volumetric images using discrete deformable model

Jerome Schmid, Jose A. Iglesias Guitian, Enrico Gobbetti, Nadia Magnenat-Thalmann

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Tags: CUDA, Image processing, nVidia, nVidia GeForce 8800 GTX, Visualization

December 22, 2011 by hgpu

Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs

Linh Ha, Marcel Prastawa, Guido Gerig, John H. Gilmore, Claudio T. Silva, Sarang Joshi1

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Tags: Algorithms, CUDA, Image processing, Image registration, Medicine, MRI, nVidia, nVidia GeForce GTX 260

December 21, 2011 by hgpu

3D Recursive Gaussian IIR on GPU and FPGAs: A Case Study for Accelerating Bandwidth-Bounded Applications

Jason Cong, Muhuan Huang, Yi Zou

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Tags: CUDA, FPGA, Image processing, nVidia, Optimization, Tesla C1060

December 19, 2011 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Parallel Implementation of Compressive Sensing Based SAR Imaging with GPU

Parallel Implementation Algorithm of Motion Estimation for GPU Applications

Parallelism, Patterns, and Performance in Iterative MRI Reconstruction

Multilevel Tile Load Map on Massive Terrain Visualization

GPU-Parallel Implementation of Color based Medical Image Retrieval in Compressed Domain

Parallel Implementation of Shape based Image Retrieval Approach on CUDA in Compressed Domain

A GPU-supported High-Level Programming Language for Image Processing

Image processing algorithm optimization with CUDA for Pure Data

Improving the usability of hierarchical representations for interactively labeling large image data sets

A GPU framework for parallel segmentation of volumetric images using discrete deformable model

Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs

3D Recursive Gaussian IIR on GPU and FPGAs: A Case Study for Accelerating Bandwidth-Bounded Applications

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

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

Most viewed papers (last 30 days)