high performance computing on graphics processing units: hgpu.org

Applications

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

Implementing Independent Component Analysis in General-Purpose GPU Architectures

Jacquelyne Forgette, Renata Wachowiak-Smolikova, Mark Wachowiak

Tags: Algorithms, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 260, OpenCL, Signal processing

September 8, 2011 by hgpu

GPU-based simulation of side-looking sonar images

E. Coiras, A. Ramirez-Montesinos, J. Groen

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Tags: CUDA, FFT, nVidia, nVidia GeForce 8800 GT, Signal processing

September 2, 2011 by hgpu

An open framework for rapid prototyping of signal processing applications

Maxime Pelcat, Jonathan Piat, Matthieu Wipliez, Slaheddine Aridhi, and Jean-Francois Nezan

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Tags: Algorithms, Graph theory, Signal processing

August 24, 2011 by hgpu

Accelerating wavelet-based video coding on graphics hardware using CUDA

Wladimir J. van der Laan, Jos B.T.M. Roerdink, Andrei C. Jalba

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Source codes

Tags: Compression, CUDA, Discrete Wavelet Transform, Motion compensation, nVidia, nVidia GeForce GTX 280, Package, Signal processing

August 5, 2011 by hgpu

VSIPL++ Acceleration Using Commodity Graphics Processors

Dan Campbell

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Tags: ATI, ATI Radeon X1900, Image processing, nVidia, nVidia GeForce 7800 GTX, OpenGL, Rendering, Signal processing

August 1, 2011 by hgpu

Applying graphics processor acceleration in a software defined radio prototyping environment

William Plishker, George F. Zaki, Shuvra S. Bhattacharyya, Charles Clancy, John Kuykendall

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Tags: Benchmarking, CUDA, nVidia, nVidia GeForce GTX 260, Signal processing

July 24, 2011 by hgpu

Real-time DVB-S2 LDPC decoding on many-core GPU accelerators

Gabriel Falcao, Joao Andrade, Vitor Silva, Leonel Sousa

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Tags: Algorithms, CUDA, nVidia, Parity-check codes, Signal processing, Tesla C2050

July 23, 2011 by hgpu

Parallel implementation of Multi-dimensional Ensemble Empirical Mode Decomposition

Li-Wen Chang, Men-Tzung Lo, Nasser Anssari, Ke-Hsin Hsu, Norden E. Huang, Wen-mei W. Hwu

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Tags: CUDA, nVidia, Signal processing, Tesla C2050

July 22, 2011 by hgpu

Discrete fourier transform on multicore

Franz Franchetti, Markus Puschel, Yevgen Voronenko, Srinivas Chellappa, Jose M. F. Moura

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Tags: Cell processor, CUDA, FFT, nVidia, nVidia GeForce GTX 280, Signal processing

July 19, 2011 by hgpu

Implementing LNS using filtering units of GPUs

Mark Arnold, Sylvain Collange, David Defour

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Tags: CUDA, Filtering, nVidia, nVidia GeForce GTX 280, Signal processing

July 17, 2011 by hgpu

Highly parallel decoding of space-time codes on graphics processing units

Kalyana C. Bollapalli, Yiyue Wu, Kanupriya Gulati, Sunil Khatri, A. Robert Calderbank

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Tags: CUDA, nVidia, nVidia GeForce GTX 280, Signal processing, Tesla C1060, Wireless baseband architecture

July 17, 2011 by hgpu

H- and C-level WFST-based large vocabulary continuous speech recognition on Graphics Processing Units

Jungsuk Kim, Kisun You, Wonyong Sung

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

July 17, 2011 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

Implementing Independent Component Analysis in General-Purpose GPU Architectures

GPU-based simulation of side-looking sonar images

An open framework for rapid prototyping of signal processing applications

Accelerating wavelet-based video coding on graphics hardware using CUDA

VSIPL++ Acceleration Using Commodity Graphics Processors

Applying graphics processor acceleration in a software defined radio prototyping environment

Real-time DVB-S2 LDPC decoding on many-core GPU accelerators

Parallel implementation of Multi-dimensional Ensemble Empirical Mode Decomposition

Discrete fourier transform on multicore

Implementing LNS using filtering units of GPUs

Highly parallel decoding of space-time codes on graphics processing units

H- and C-level WFST-based large vocabulary continuous speech recognition on Graphics Processing Units

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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