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Accelerating Image Retrieval Using Factorial Correspondence Analysis on GPU

Nguyen-Khang Pham, Annie Morin, Patrick Gros

Tags: CUBLAS, CUDA, Factorial correspondence analysis, Image processing, nVidia, SIFT

October 29, 2010 by hgpu

CUSA and CUDE: GPU-accelerated methods for estimating solvent accessible surface area and desolvation

David Dynerman, Erick Butzlaff, Julie C. Mitchell

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Tags: Bioinformatics, Biology, Biophysics, Chemistry, Computational biology, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Solvent effects

October 29, 2010 by hgpu

An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness

Sunpyo Hong, Hyesoon Kim

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Tags: Analytical model, Computer science, CUDA, Memory level parallelism, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTX, nVidia Quadro FX 5600, Warp level parallelism

October 29, 2010 by hgpu

GPU acceleration of cutoff pair potentials for molecular modeling applications

Christopher I. Rodrigues, David J. Hardy, John E. Stone, Klaus Schulten, Wen Mei

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Tags: Algorithms, Biology, Chemistry, CUDA, Medicine, Molecular dynamics, nVidia, Tesla C870

October 28, 2010 by hgpu

AUTO-GC: Automatic translation of data mining applications to GPU clusters

Wenjing Ma, Gagan Agrawal

Tags: Code generation, Computer science, CUDA, GPU cluster, nVidia

October 28, 2010 by hgpu

CUDA cuts: Fast graph cuts on the GPU

Vibhav Vineet, P. J. Narayanan

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Tags: Computer science, CUDA, Image processing, nVidia, nVidia GeForce 8800 GTX

October 28, 2010 by hgpu

GPU packet classification using OpenCL: a consideration of viable classification methods

Alastair Nottingham, Barry Irwin

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Tags: Algorithms, Computer science, nVidia, OpenCL

October 28, 2010 by hgpu

An adaptive performance modeling tool for GPU architectures

Sara S. Baghsorkhi, Matthieu Delahaye, Sanjay J. Patel, William D. Gropp, Wen mei

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Tags: Analytical model, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Performance

October 28, 2010 by hgpu

An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases

Lukasz Ligowski, Witold Rudnicki

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, Sequence alignment, Smith-Waterman algorithm

October 28, 2010 by hgpu

Fast Simulations of Gravitational Many-body Problem on RV770 GPU

Kazuki Fujiwara, Naohito Nakasato

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Tags: Astrophysics, ATI, ATI CAL, ATI Radeon HD 4850, ATI Radeon HD 4870, ATI Stream, Galaxy Astrophysics, Instrumentation and Methods for Astrophysics, RV770

October 28, 2010 by hgpu

String Matching on a Multicore GPU Using CUDA

Charalampos S. Kouzinopoulos, Konstantinos G. Margaritis

Tags: Algorithms, Computer science, CUDA, nVidia, String matching

October 28, 2010 by hgpu

Pseudo-random number generators for Monte Carlo simulations on Graphics Processing Units

Vadim Demchik

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Tags: ATI, ATI IL, ATI Radeon HD 4850, ATI Radeon HD 4870, ATI Radeon HD 5850, ATI Stream, Computational Physics, High Energy Physics - Lattice, Monte Carlo simulation, Physics, Pseudo-random number generators

October 28, 2010 by hgpu

PyTorch: An Imperative Style, High-Performance Deep Learning Library

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Masivo: Parallel public transport routes simulation

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

FBLAS: BLAS implementation for Intel FPGA

FBLAS: Streaming Linear Algebra Kernels on FPGA

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Titan: A CUDA-based physics simulation sandbox for high-performance robotics, physics, and reinforcement learning applications

Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

onYourMarks: A deep learning benchmark implementation for Nvidia's Jetson TX2

Benchmarking Deep Learning Models on Jetson TX2

Hacking Neural Networks: A small course on exploiting and defending neural networks

Hacking Neural Networks: A Short Introduction

A Computing Kernel for Network Binarization on PyTorch

A Computing Kernel for Network Binarization on PyTorch

Deep Learning Based FPGA-CPU Acceleration

Deep Learning Based FPGA-CPU Acceleration

KTT: Kernel Tuning Toolkit

Framework for Parallel Kernels Auto-tuning

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2020

April

27-29

Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

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

2019

July

26-28

Rome, Italy

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

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