high performance computing on graphics processing units: hgpu.org

Applications

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

GPU Accelerated Registration of a Statistical Shape Model of the Lumbar Spine to 3D Ultrasound Images

Siavash Khallaghi, Purang Abolmaesumi, Ren Hui Gong, Elvis Chen, Sean Gill, Jonathan Boisvert, David Pichora, Dan Borschneck, Gabor Fichtinger, Parvin Mousavi

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Tags: CUDA, Image processing, Medicine, nVidia, nVidia GeForce GTX 285, Optimization, Ultrasound

January 18, 2012 by hgpu

Heterogeneous Computing for Vertebra Detection and Segmentation in X-Ray Images

Fabian Lecron, Sidi Ahmed Mahmoudi, Mohammed Benjelloun, Said Mahmoudi, Pierre Manneback

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Tags: CUDA, Heterogeneous systems, Image processing, Medicine, nVidia, Tesla C1060

January 18, 2012 by hgpu

Fast Regularization of Matrix-Valued Images

Guy Rosman, Yu Wang, Xue-Cheng Tai, Ron Kimmel, Alfred M. Bruckstein

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 580

January 16, 2012 by hgpu

Efficient Convex Optimization Approaches to Variational Image Fusion

Jing Yuan, Brandon Miles, Juan Shi, Greg Garvin, Xue-Cheng Tai, Aaron Fenster

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Tags: Algorithms, CUDA, Image processing, Medicine, nVidia, Optimization, Sensing, Tesla C1060

January 13, 2012 by hgpu

Parallel mutual information estimation for inferring gene regulatory networks on GPUs

Haixiang Shi, Bertil Schmidt, Weiguo Liu, Wolfgang Muller-Wittig

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Source codes

Tags: Algorithms, Biology, Computational biology, CUDA, Image processing, Machine learning, nVidia, Package, Tesla C2050

January 13, 2012 by hgpu

Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units

Sergio Sanchez, Abel Paz, Gabriel Martin, Antonio Plaza

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Tags: CUDA, Image processing, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 275, Tesla C1060

January 13, 2012 by hgpu

An FPGA-based processing pipeline for high definition stereo video

Pierre Greisen, Simon Heinzle, Markus Gross, Andreas Peter Burg

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Tags: Algorithms, CUDA, FPGA, Image processing, nVidia, nVidia GeForce GTX 480, Stereo vision

January 13, 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

Optimizing a Biomedical Imaging Orientation Score Framework

Chidiebere Okwudire, Martin Palatnik, Xu Zhang, Tanya Kudchadker

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Tags: Computational Complexity, CUDA, GPU cluster, Image processing, nVidia, nVidia GeForce GTX 570, Optimization

January 6, 2012 by hgpu

Implementation of a Fast Image Coding and Retrieval System Using a GPU

Prasanna Sattigeri, Jayaraman J. Thiagarajan, Karthikeyan N. Ramamurthy, Andreas Spanias

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 460

January 5, 2012 by hgpu

Pyramidal Image Blending Using CUDA Framework

Pritam Prakash Shete, Venkat P. P. K., S. K. Bose

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia Quadro FX 4600, Optimization

January 5, 2012 by hgpu

Abundance Estimation Algorithms using NVIDIA CUDA Technology

David Gonzalez, Christian Sanchez, Ricardo Veguilla, Nayda G. Santiago, Samuel Rosario-Torres, Miguel Velez-Reyes

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Tags: Algorithms, Computer science, CUDA, Image processing, nVidia, nVidia GeForce 8800 GTX

January 5, 2012 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

GPU Accelerated Registration of a Statistical Shape Model of the Lumbar Spine to 3D Ultrasound Images

Heterogeneous Computing for Vertebra Detection and Segmentation in X-Ray Images

Fast Regularization of Matrix-Valued Images

Efficient Convex Optimization Approaches to Variational Image Fusion

Parallel mutual information estimation for inferring gene regulatory networks on GPUs

Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units

An FPGA-based processing pipeline for high definition stereo video

Artifact-Free JPEG Decompression with Total Generalized Variation

Optimizing a Biomedical Imaging Orientation Score Framework

Implementation of a Fast Image Coding and Retrieval System Using a GPU

Pyramidal Image Blending Using CUDA Framework

Abundance Estimation Algorithms 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

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