high performance computing on graphics processing units: hgpu.org

hgpu.org » Signal processing

Combining Multiple Optimised FPGA-based Pulsar Search Modules Using OpenCL

Haomiao Wang, Prabu Thiagaraj, Oliver Sinnen

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Tags: Astrophysics, FPGA, OpenCL, Signal processing

June 17, 2018 by hgpu

Using reconfigurable computing technology to accelerate matrix decomposition and applications

Xinying Wang

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Tags: Computer science, Data mining, Factorization, FPGA, Intel Xeon Phi, Matrix decomposition, Semantic indexing, Signal processing, Text mining, Thesis

December 28, 2017 by hgpu

A tutorial on the implementations of linear image filters in CPU and GPU

Alvaro Pardo

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 430, Signal processing, Tutorial

December 7, 2017 by hgpu

Practically efficient methods for performing bit-reversed permutation in C++11 on the x86-64 architecture

Christian Knauth, Boran Adas, Daniel Whitfield, Xuesong Wang, Lydia Ickler, Tim Conrad, Oliver Serang

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Tags: Computer science, CUDA, Mathematical Software, nVidia, OpenMP, Package, Signal processing

August 8, 2017 by hgpu

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

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

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

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

Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy

Danny C. Price, Jonathon Kocz, Matthew Bailes, Lincoln J. Greenhill

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Tags: Astrophysics, CUDA, DSP, FPGA, nVidia, Overview, Signal processing

February 7, 2017 by hgpu

Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

Daniel George, E. A. Huerta

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Tags: Algorithms, Artificial intelligence, Astrophysics, CUDA, Deep learning, Filtering, Gravitation, Gravitational waves, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, Physics, Signal processing, Tesla K40, Tesla P100

January 4, 2017 by hgpu

Parallel Digital Predistortion Design on Mobile GPU and Embedded Multicore CPU for Mobile Transmitters

Kaipeng Li, Amanullah Ghazi, Chance Tarver, Jani Boutellier, Mahmoud Abdelaziz, Lauri Anttila, Markku Juntti, Mikko Valkama, Joseph R. Cavallaro

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Tags: ARM, CUDA, nVidia, nVidia Jetson TK1, nVidia Tegra TX1, Signal processing, SoC

December 31, 2016 by hgpu

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

SWIFOLD: Smith-Waterman implementation on FPGA with OpenCL for long DNA sequences

Spatter: Benchmark for measuring the performance of sparse and irregular memory access

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

StePS: Stereographically Projected Cosmological Simulations

StePS: A Multi-GPU Cosmological N-body Code for Compactified Simulations

AMGCL:C++ library for solving large sparse linear systems with algebraic multigrid method

AMGCL: an Efficient, Flexible, and Extensible Algebraic Multigrid Implementation

PAstudy

Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?

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3rd International Conference on Vision, Image and Signal Processing (ICVISP), 2019

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high performance computing on graphics processing units: hgpu.org

Combining Multiple Optimised FPGA-based Pulsar Search Modules Using OpenCL

Using reconfigurable computing technology to accelerate matrix decomposition and applications

A tutorial on the implementations of linear image filters in CPU and GPU

Practically efficient methods for performing bit-reversed permutation in C++11 on the x86-64 architecture

A GPU Based Memory Optimized Parallel Method For FFT Implementation

Synthesis of Embedded Software using Dataflow Schedule Graphs

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

Wireless Interference Identification with Convolutional Neural Networks

Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy

Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

Parallel Digital Predistortion Design on Mobile GPU and Embedded Multicore CPU for Mobile Transmitters

Recent source codes

schnaps: a finite element solver

GPU-benchmarks

Truchas: 3D Multiphysics Simulation of Metal Casting and Processing

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

SWIFOLD: Smith-Waterman implementation on FPGA with OpenCL for long DNA sequences

Spatter: Benchmark for measuring the performance of sparse and irregular memory access

StePS: Stereographically Projected Cosmological Simulations

AMGCL:C++ library for solving large sparse linear systems with algebraic multigrid method

PAstudy

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Featured events

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

International Conference on Frontiers of Neural Networks (ICFNN), 2019

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

3rd World Conference on Clinical Research and Biomarkers, 2019

3rd International Conference on Vision, Image and Signal Processing (ICVISP), 2019