high performance computing on graphics processing units: hgpu.org

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

Signal Processing and General Purpose Computing on GPU

Muge Guher

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Tags: CUDA, nVidia, Signal processing, Tesla C2050

March 9, 2013 by hgpu

Full Covariance Gaussian Mixture Models Evaluation on GPU

Jan Vanek, Jan Trmal, Josef V. Psutka, Josef Psutka

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Tags: Algorithms, ATI, ATI Mobility Radeon HD 5470, ATI Radeon HD 5670, ATI Radeon HD 5870, CUDA, nVidia, nVidia GeForce GT 240, nVidia GeForce GTX 580, nVidia Quadro FX 770 M, OpenCL, Signal processing, Speech recognition

March 2, 2013 by hgpu

Multi-GPU based on multicriteria optimization for motion estimation system

Carlos Garcia, Guillermo Botella, Fermin Ayuso, Manuel Prieto, Francisco Tirado

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Tags: Algorithms, Computer vision, CUDA, Data parallelism, nVidia, Optical flow, Signal processing, Tesla C1060, Tesla M2070

February 27, 2013 by hgpu

Advanced Video Coding on CPUs and GPUs: Parallelization and RD Analysis

Svetislav Momcilovic, Aleksandar Ilic, Nuno Roma, Leonel Sousa

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Tags: Algorithms, Compression, CUDA, H.264/AVC, Image processing, nVidia, Signal processing, Video encoding

February 25, 2013 by hgpu

MATLAB and Python for GPU Computing

Jose Unpingco, Juan Carlos Chaves

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Tags: Computer science, CUDA, Image processing, nVidia, nVidia GeForce 9800 GTS, nVidia GeForce 9800 GX2, Python, Signal processing, Tesla C1060, Tesla C2050

February 15, 2013 by hgpu

The Implement of Common Beam Forming Using GPU

Jing-hong Jiang, Qi-xiang Han, Hui-zhi Cai, Wei-min Hou

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Tags: CUDA, nVidia, nVidia GeForce GTX 285, Signal processing

February 12, 2013 by hgpu

A Networked Dataflow Simulation Environment for Signal Processing and Data Mining Applications

Stephen Won

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Tags: Computer vision, CUDA, Data mining, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, SIFT, Signal processing, Thesis

February 12, 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 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

A Multi-GPU Programming Library for Real-Time Applications

Sebastian Schaetz, Martin Uecker

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Tags: Algorithms, CUDA, GPU cluster, Image reconstruction, Magnetic resonance imaging, MPI, MRI, nVidia, nVidia GeForce GTX 580, Performance, Signal processing

January 8, 2013 by hgpu

High Performance Multi-dimensional (2D/3D) FFT-Shift Implementation on Graphics Processing Units (GPUs)

Marwan Abdellah, Salah Saleh, Ayman Eldeib, Amr Shaarawi

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Tags: CUDA, FFT, nVidia, nVidia GeForce GTX 570, Rendering, Signal processing

January 8, 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

Signal Processing and General Purpose Computing on GPU

Full Covariance Gaussian Mixture Models Evaluation on GPU

Multi-GPU based on multicriteria optimization for motion estimation system

Advanced Video Coding on CPUs and GPUs: Parallelization and RD Analysis

MATLAB and Python for GPU Computing

The Implement of Common Beam Forming Using GPU

A Networked Dataflow Simulation Environment for Signal Processing and Data Mining Applications

A Multi-GPU Sources Reconstruction Method for Imaging Applications

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

A Multi-GPU Programming Library for Real-Time Applications

High Performance Multi-dimensional (2D/3D) FFT-Shift Implementation on Graphics Processing Units (GPUs)

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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