high performance computing on graphics processing units: hgpu.org

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Multi-dimensional Functional Principal Component Analysis

Lu-Hung Chen, Ci-Ren Jiang

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Tags: FFT, nVidia, nVidia GeForce GTX 980, nVidia Quadro K600, OpenACC, Statistics

October 16, 2015 by hgpu

Fast Algorithms for Convolutional Neural Networks

Andrew Lavin

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Tags: Algorithms, CNN, Computer science, Deep learning, FFT, Image recognition, Machine learning, Neural networks

October 3, 2015 by hgpu

The Dynamical Kernel Scheduler – Part 1

Andreas Adelmann, Uldis Locans, Andreas Suter

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Tags: Algorithms, Computational Physics, CUDA, FFT, Intel Xeon Phi, Monte Carlo simulation, nVidia, OpenCL, OpenMP, Physics

September 29, 2015 by hgpu

AccFFT: A library for distributed-memory FFT on CPU and GPU architectures

Amir Gholami, Judith Hill, Dhairya Malhotra, George Biros

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Tags: Algorithms, Computer science, CUDA, FFT, MPI, nVidia, OpemMP, Package, Tesla K20

June 30, 2015 by hgpu

Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators

Fredrik Andersson, Marcus Carlsson, Viktor V. Nikitin

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Tags: Algorithms, CUDA, FFT, Image processing, Mathematics, Numerical Analysis, nVidia, nVidia GeForce GTX 770, Radon transform

June 5, 2015 by hgpu

Research on the fast Fourier transform of image based on GPU

Feifei Shen, Zhenjian Song, Congrui Wu, Jiaqi Geng, Qingyun Wang

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Tags: Algorithms, FFT, Image processing, Mathematical Software, nVidia, nVidia GeForce 8600 GT

June 1, 2015 by hgpu

Efficient FFT mapping on GPU for radar processing application: modeling and implementation

Mohamed Amine Bergach, Emilien Kofman, Robert de Simone, Serge Tissot, Michel Syska

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Tags: FFT, GPGPU, Intel HD 5200, Intel HD Graphics 4000, OpenCL, SAR

June 1, 2015 by Aminems

AccFFT: A library for distributed-memory 3-D FFT on CPU and GPU architectures

Amir Gholami, Judith Hill, Dhairya Malhotra, George Biros

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Tags: Computer science, CUDA, FFT, MPI, nVidia, Package, Tesla K20

May 7, 2015 by hgpu

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Nicolas Vasilache, Jeff Johnson, Michael Mathieu, Soumith Chintala, Serkan Piantino, Yann LeCun

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Tags: Algorithms, CNN, Computer science, CUDA, FFT, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, Package, Tesla K40

December 26, 2014 by hgpu

Acceleration of Hessenberg Reduction for Nonsymmetric Matrix

Hesamaldin Nekouei

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Tags: Algorithms, Computer science, CUDA, FFT, Linear Algebra, nVidia, nVidia GeForce GT 635 M, Thesis

December 14, 2014 by hgpu

Big Integer Multiplication with CUDA FFT (cuFFT) Library

Hovhannes Bantikyan

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Tags: Computer science, CUDA, FFT, nVidia, nVidia GeForce GT 630

December 7, 2014 by hgpu

Software Polarization Spectrometer "PolariS"

Izumi Mizuno, Seiji Kameno, Amane Kano, Makoto Kuroo, Fumitaka Nakamura, Noriyuki Kawaguchi, Katsunori M. Shibata, Seisuke Kuji, Nario Kuno

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Tags: Astrophysics, CUDA, FFT, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GT 640, Package

December 5, 2014 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

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high performance computing on graphics processing units: hgpu.org

Multi-dimensional Functional Principal Component Analysis

Fast Algorithms for Convolutional Neural Networks

The Dynamical Kernel Scheduler – Part 1

AccFFT: A library for distributed-memory FFT on CPU and GPU architectures

Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators

Research on the fast Fourier transform of image based on GPU

Efficient FFT mapping on GPU for radar processing application: modeling and implementation

AccFFT: A library for distributed-memory 3-D FFT on CPU and GPU architectures

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Acceleration of Hessenberg Reduction for Nonsymmetric Matrix

Big Integer Multiplication with CUDA FFT (cuFFT) Library

Software Polarization Spectrometer "PolariS"

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