high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Image and Signal Processing

A GPU Based Memory Optimized Parallel Method For FFT Implementation

Fan Zhang, Chen Hu, Qiang Yin, Wei Hu

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Tags: CUDA, FFT, Image processing, nVidia, Sensing, Signal processing, Tesla C2070

August 1, 2017 by hgpu

Real-time colouring and filtering with graphics shaders

Dany Vohl, Christopher J. Fluke, David G. Barnes, Amr H. Hassan

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Tags: Algorithms, Astrophysics, Filtering, GLSL, Image processing, nVidia, nVidia GeForce GT 750 M, nVidia GeForce GTX Titan X, nVidia Grid K1, Package, Rendering

July 5, 2017 by hgpu

Synthesis of Embedded Software using Dataflow Schedule Graphs

Abhay Raina

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Tags: Computer science, DSP, nVidia, nVidia GeForce GTX 680, OpenCL, Signal processing, Thesis

July 2, 2017 by hgpu

ART vs. NDK vs. GPU acceleration: A study of performance of image processing algorithms on Android

Andreas Palsson

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Tags: Algorithms, Android, Image processing, Java, OpenCL, RenderScript, Thesis

June 25, 2017 by hgpu

On the Use of a GPU-Accelerated Mobile Device Processor for Sound Source Localization

Jose A. Belloch, Jose M. Badia, Francisco D. Igual, Maximo Cobos, Enrique S. Quintana-Orti

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Tags: Algorithms, ARM, nVidia, nVidia GeForce GTX 1080, OpenCL, Signal processing, Tesla K20

June 21, 2017 by hgpu

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Keunwoo Choi, Deokjin Joo, Juho Kim

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Tags: Computer science, CUDA, Deep learning, Keras, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, Package, Python, Signal processing, Tesla K80, Tesla M60

June 21, 2017 by hgpu

Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing

Lingyu Duan, Wei Sun, Xinfeng Zhang, Jie Chen, Jianxiong Yin, Simon See, Tiejun Huang, Alex C. Kot, Wen Gao

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Tags: Benchmarking, CUDA, Hybrid computing, Image processing, Neural networks, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, nVidia Tegra TX1, Tesla M40

June 1, 2017 by hgpu

Accelerating Discrete Wavelet Transforms on GPUs

David Barina, Michal Kula, Michal Matysek, Pavel Zemcik

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Tags: ATI, ATI Radeon HD 6970, Computer science, Discrete Wavelet Transform, HLSL, Image processing, nVidia, nVidia GeForce GTX Titan X, OpenCL, Pixel shaders

May 24, 2017 by hgpu

Accelerating Discrete Wavelet Transforms on Parallel Architectures

David Barina, Michal Kula, Michal Matysek, Pavel Zemcik

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Tags: Algorithms, ATI, ATI Radeon HD 6970, Discrete Wavelet Transform, Image processing, nVidia, nVidia GeForce GTX Titan X, OpenCL, OpenGL, Performance, Pixel shaders

April 30, 2017 by hgpu

Evaluation of Libraries for Parallel Computing in Haskell – A Case Study with a Super-resolution Application

Takuya Matsumoto, Kiminori Matsuzaki

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Tags: Computer science, CUDA, Haskell, Image processing, MRI, nVidia, nVidia GeForce GTX 750 Ti

April 3, 2017 by hgpu

Large-scale image analysis using docker sandboxing

B. Sengupta, E. Vazquez, M. Sasdelli, Y. Qian, M. Peniak, L. Netherton, G. Delfino

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Tags: Computer science, Computer vision, CUDA, Image processing, nVidia, nVidia Quadro M 6000

March 14, 2017 by hgpu

Wireless Interference Identification with Convolutional Neural Networks

Malte Schmidt, Dimitri Block, Uwe Meier

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Tags: CNN, Deep learning, Keras, Neural networks, nVidia, nVidia GeForce GTX 960, Signal processing

March 5, 2017 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

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high performance computing on graphics processing units: hgpu.org

Applications

A GPU Based Memory Optimized Parallel Method For FFT Implementation

Real-time colouring and filtering with graphics shaders

Synthesis of Embedded Software using Dataflow Schedule Graphs

ART vs. NDK vs. GPU acceleration: A study of performance of image processing algorithms on Android

On the Use of a GPU-Accelerated Mobile Device Processor for Sound Source Localization

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing

Accelerating Discrete Wavelet Transforms on GPUs

Accelerating Discrete Wavelet Transforms on Parallel Architectures

Evaluation of Libraries for Parallel Computing in Haskell – A Case Study with a Super-resolution Application

Large-scale image analysis using docker sandboxing

Wireless Interference Identification with Convolutional Neural Networks

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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