high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Image and Signal Processing

A Co-Prime Blur Scheme for Data Security in Video Surveillance

Christopher Thorpe, Feng Li, Zijia Li, Zhan Yu, David Saunders, Jingyi Yu

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

March 23, 2012 by hgpu

Real-time execution of image change detection

Hugo van Lint

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Tags: Algorithms, CUDA, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 480, Thesis

March 13, 2012 by hgpu

Hybrid general-purpose computation on GPU (GPGPU) and computer graphics synthetic aperture radar simulation for complex scenes

Fan Zhang, Zheng Li, Bingnan Wang, Maosheng Xiang, Wen Hong

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Tags: CUDA, nVidia, nVidia GeForce 9500 GT, Physics, Signal processing

March 12, 2012 by hgpu

Performance Analysis of a Novel GPU Computation-to-core Mapping Scheme for Robust Facet Image Modeling

Seung In Park, Yong Cao, Layne T. Watson, Francis Quek

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 295, Performance

March 10, 2012 by hgpu

Dynamic Task-Scheduling and Resource Management for GPU Accelerators in Medical Imaging

Richard Membarth, Jan-Hugo Lupp, Frank Hannig, Jurgen Teich, Mario Korner, Wieland Eckert

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Tags: CUDA, Image processing, Medicine, nVidia, nVidia Quadro FX 1800, Task scheduling, Tesla C2050

March 8, 2012 by hgpu

Parallel Implementation of Similarity Measures on GPU Architecture using CUDA

Kuldeep Yadav, Ankush Mittal, M.A Ansari, Vennktesh Vishwarup

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Tags: Algorithms, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce 9500 GT, Pattern recognition

March 2, 2012 by hgpu

Benchmarking Next Generation Hardware Platforms: An Experimental Approach

Vishakha Gupta, Adit Ranadive, Ada Gavrilovska, Karsten Schwan

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Tags: Benchmarking, CUDA, Finance, Heterogeneous systems, Image processing, nVidia, nVidia GeForce 8800 GTX

March 1, 2012 by hgpu

Parallel Computation for Discrete Orthogonal Moments of Images Using Graphic Processing Unit

Dongfeng Xu, Yehan Zheng, Yuefang Gao, Dong Wang

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce 9600 GSO

February 28, 2012 by hgpu

Use of Multiple GPUs on Shared Memory Multiprocessors for Ultrasound Propagation Simulations

Jiri Jaros, Bradley E. Treeby, Alistair P. Rendell

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Tags: CUDA, FFT, Image processing, nVidia, nVidia GeForce GTX 580, Ultrasound

February 23, 2012 by hgpu

GPU-Based Iterative Relative Fuzzy Connectedness Image Segmentation

Ying Zhuge, Jayaram K. Udupa, Krzysztof C. Ciesielski, Alexandre X. Falcao, Paulo A. V. Miranda, Robert W. Miller

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

February 22, 2012 by hgpu

Image segmentation using CUDA implementations of the Runge-Kutta-Merson and GMRES methods

Tomas Oberhuber, Atsushi Suzuki, Jan Vacata, Vitezslav Zabka

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Tags: Algorithms, CUDA, Differential equations, Image processing, MRI, nVidia, nVidia GeForce GTX 480, ODEs, Ordinary differential equations, Partial differential equations, PDEs

February 22, 2012 by hgpu

High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures

Daehyun Kim, Joshua Trzasko, Mikhail Smelyanskiy, Clifton Haider, Pradeep Dubey, Armando Manduca

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Tags: Algorithms, CUDA, Image processing, Medicine, MRI, nVidia, Sensing, Tesla C2050

February 21, 2012 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

A Co-Prime Blur Scheme for Data Security in Video Surveillance

Real-time execution of image change detection

Hybrid general-purpose computation on GPU (GPGPU) and computer graphics synthetic aperture radar simulation for complex scenes

Performance Analysis of a Novel GPU Computation-to-core Mapping Scheme for Robust Facet Image Modeling

Dynamic Task-Scheduling and Resource Management for GPU Accelerators in Medical Imaging

Parallel Implementation of Similarity Measures on GPU Architecture using CUDA

Benchmarking Next Generation Hardware Platforms: An Experimental Approach

Parallel Computation for Discrete Orthogonal Moments of Images Using Graphic Processing Unit

Use of Multiple GPUs on Shared Memory Multiprocessors for Ultrasound Propagation Simulations

GPU-Based Iterative Relative Fuzzy Connectedness Image Segmentation

Image segmentation using CUDA implementations of the Runge-Kutta-Merson and GMRES methods

High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures

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)