high performance computing on graphics processing units: hgpu.org

hgpu.org » Applications » Image and Signal Processing

Distributed multi-node, multi-GPU, heterogeneous system for 3D image reconstruction in Electrical Capacitance Tomography – network performance and application analysis

Pawel Kapusta, Michal Majchrowicz, Dominik Sankowski, Lidia Jackowska-Strumillo, Robert Banasiak

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Tags: ATI, ATI Radeon HD 5970, FEM, Finite element method, Heterogeneous systems, Image processing, Image reconstruction, Linear Algebra, Medicine, nVidia, Tesla C2070, Tesla S1070, Tomography

February 9, 2013 by hgpu

Fast 3D Wavelet Transform on Multicore and Manycore Computing Platforms

V. Galiano, O. Lopez-Granado, M.P. Malumbres, H. Migallon

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Tags: Algorithms, Computer science, CUDA, Image processing, nVidia, nVidia GeForce GTX 280, Optimization, Wavelet

February 9, 2013 by hgpu

Parallel k-Means Image Segmentation Using Sort, Scan & Connected Components on a GPU

Michael Backer, Jan Tunnermann, Barbel Mertsching

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

February 6, 2013 by hgpu

A Multi-GPU Sources Reconstruction Method for Imaging Applications

Miguel Lopez-Portugues, Yuri Alvarez, Jesus A. Lopez-Fernandez, Cebrian Garcia, Rafael G. Ayestaran, Fernando Las-Heras

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Tags: CUDA, Electrodynamics, GPU cluster, nVidia, nVidia GeForce GTX 560, nVidia GeForce GTX 680, Signal processing

February 6, 2013 by hgpu

Efficient Exploitation of Heterogeneous Platforms for Vertebra Detection in X-Ray Images

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

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Tags: CUDA, Heterogeneous systems, Image processing, nVidia, Task scheduling

February 1, 2013 by hgpu

Performance Study of Satellite Image Processing on Graphics Processors Unit Using CUDA

In-Kyu Jeong, Min-Gee Hong, Kwang-Soo Hahn, Joonsoo Choi, Choen Kim

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Tags: CUDA, Data acquisition, Image processing, nVidia, nVidia GeForce GTX 550 Ti, Raytracing, Sensing, Visualization

January 24, 2013 by hgpu

Accelerating Image Reconstruction in Dual-Head PET System by GPU and Symmetry Properties

Cheng-Ying Chou, Yun Dong, Yukai Hung, Yu-Jiun Kao, Weichung Wang, Chien-Min Kao, Chin-Tu Chen

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Tags: Algorithms, CUDA, Image processing, Image reconstruction, Medicine, nVidia, PET, Tesla C2070, Tomography

January 20, 2013 by hgpu

GPU-accelererated regularisation of large diffusion-tensor volumes

Tuomo Valkonen, Manfred Liebmann

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Tags: Algorithms, Diffusion tensor, Image processing, Medicine, nVidia, nVidia GeForce GTX 480, OpenACC, Tesla C2070

January 18, 2013 by hgpu

Efficient implementation of multiuser precoding algorithms on GPU for MIMO-OFDM systems

Fernando Domene, Sandra Roger, Carla Ramiro, Gema Pinero, Alberto Gonzalez

Tags: Algorithms, CUDA, MIMO, nVidia, nVidia Quadro FX 5800, Signal processing

January 16, 2013 by hgpu

On the Use of Graphic Processing Units for the Efficient Implementation of MIMO Detectors

Sandra Roger, Carla Ramiro, Alberto Gonzalez, Vicenc Almenar, Antonio M. Vidal

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Tags: Algorithms, CUDA, MIMO, nVidia, Signal processing, Tesla C2070

January 16, 2013 by hgpu

Massively parallelizable list-mode reconstruction using a Monte Carlo-based elliptical Gaussian model

G. Sportelli, J. E. Ortuno, J. J. Vaquero, M. Desco, A. Santos

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Tags: Image processing, Image reconstruction, Medicine, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, OpenCL, PET

January 15, 2013 by hgpu

Parallel Catmull-Rom Spline Interpolation Algorithm for Image Zooming Based on CUDA

Tunhua Wu, Baogang Bai, Ping Wang

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

January 12, 2013 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Distributed multi-node, multi-GPU, heterogeneous system for 3D image reconstruction in Electrical Capacitance Tomography – network performance and application analysis

Fast 3D Wavelet Transform on Multicore and Manycore Computing Platforms

Parallel k-Means Image Segmentation Using Sort, Scan & Connected Components on a GPU

A Multi-GPU Sources Reconstruction Method for Imaging Applications

Efficient Exploitation of Heterogeneous Platforms for Vertebra Detection in X-Ray Images

Performance Study of Satellite Image Processing on Graphics Processors Unit Using CUDA

Accelerating Image Reconstruction in Dual-Head PET System by GPU and Symmetry Properties

GPU-accelererated regularisation of large diffusion-tensor volumes

Efficient implementation of multiuser precoding algorithms on GPU for MIMO-OFDM systems

On the Use of Graphic Processing Units for the Efficient Implementation of MIMO Detectors

Massively parallelizable list-mode reconstruction using a Monte Carlo-based elliptical Gaussian model

Parallel Catmull-Rom Spline Interpolation Algorithm for Image Zooming Based on CUDA

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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