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The Risks of WebGL: Analysis, Evaluation and Detection

Alex Belkin, Nethanel Gelernter, Israel Cidon

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Tags: Computer science, nVidia, nVidia GeForce GTX 1080, Security, WebGL

May 1, 2019 by hgpu

TensorFlow.js: Machine Learning for the Web and Beyond

Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan, Nick Kreeger, Ping Yu, Kangyi Zhang, Shanqing Cai, Eric Nielsen, David Soergel, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado, Fernanda B. Viegas, Martin Wattenberg

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Tags: Computer science, CUDA, Deep learning, Java, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenGL, Package, TensorFlow, WebGL

February 17, 2019 by hgpu

Optimizing Web Virtual Reality

Rabimba Karanjai

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Tags: Benchmarking, Computer science, Java, OpenGL, Rendering, Thesis, Virtual reality, WebGL

August 26, 2018 by hgpu

Automated Testing of Graphics Shader Compilers

Alastair F. Donaldson, Hugues Evrard, Andrei Lascu, Paul Thomson

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Tags: AMD Radeon R9 Fury, ARM, ATI, ATI Radeon HD 7700, Compilers, Computer science, GLSL, Intel, nVidia, nVidia GeForce GTX 770, nVidia GeForce GTX Titan X, OpenGL, Rendering, Security, Shaders, WebGL

September 21, 2017 by hgpu

GPU-based visualization of domain-coloured algebraic Riemann surfaces

Stefan Kranich

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Tags: Computer science, Graphics, OpenGL, Visualization, WebGL

July 17, 2015 by hgpu

Vulkan Multi-GPU application

Multi-GPU Rendering with Vulkan API

Benchmarking Analysis of Vision Kernels on Embedded CPU, GPU and FPGA

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

Neural Query Language: A Knowledge Base Query Language for Tensorflow

TensorNetwork: A TensorFlow library for easy and efficient manipulation of tensor networks

TensorNetwork: A Library for Physics and Machine Learning

jive

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

caffe-mc-opencl: Caffe Deep Learning Software with Model Compression by Sparse Coding (Backend: OpenCL)

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

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Boston, MA, USA

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

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Larnaca, Cyprus

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

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26-28

Rome, Italy

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

2019

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Rome, Italy

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

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high performance computing on graphics processing units: hgpu.org

The Risks of WebGL: Analysis, Evaluation and Detection

TensorFlow.js: Machine Learning for the Web and Beyond

Optimizing Web Virtual Reality

Automated Testing of Graphics Shader Compilers

GPU-based visualization of domain-coloured algebraic Riemann surfaces

Recent source codes

Vulkan Multi-GPU application

Benchmarking Analysis of Vision Kernels on Embedded CPU, GPU and FPGA

Neural Query Language: A Knowledge Base Query Language for Tensorflow

NMODL: Code Generation Framework For NEURON MODeling Language

TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox

cuda-benches: collection of small CUDA benchmarking programs

Simulee: Charactering and Detecting CUDA Program Bugs

TensorNetwork: A TensorFlow library for easy and efficient manipulation of tensor networks

jive

caffe-mc-opencl: Caffe Deep Learning Software with Model Compression by Sparse Coding (Backend: OpenCL)

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Featured events

7th International Workshop on OpenCL, 2019

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

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