high performance computing on graphics processing units: hgpu.org

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

Cross-Compiling Shading Languages

Lukas Hermanns

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Tags: 3D Graphics and Realism, Algorithms, DirectX, GLSL, HLSL, OpenGL, Real-time graphics, Rendering, Thesis

July 15, 2018 by hgpu

Migrating from OpenGL ES to Vulkan

Imagination Technologies Limited

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Tags: 3D Graphics and Realism, OpenGL, Rendering, Vulkan

June 28, 2018 by hgpu

Neural scene representation and rendering

S. M. Ali Eslami, Danilo J. Rezende, Frederic Besse, Fabio Viola, Ari S. Morcos, Marta Garnelo, Avraham Ruderman, Andrei A. Rusu, Ivo Danihelka, Karol Gregor, David P. Reichert, Lars Buesing, Theophane Weber, Oriol Vinyals, Dan Rosenbaum, Neil Rabinowitz, Helen King, Chloe Hillier, Matt Botvinick, Daan Wierstra, Koray Kavukcuoglu, Demis Hassabis

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Tags: Computer science, Deep learning, Neural networks, nVidia, OpenGL, Package, Rendering, Tesla K80

June 17, 2018 by hgpu

A Comparison between GPU-based Volume Ray Casting Implementations: Fragment Shader, Compute Shader, OpenCL, and CUDA

Francisco Sans, Rhadames Carmona

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 660, OpenCL, OpenGL, Performance, Rendering

March 31, 2018 by hgpu

Equalizer 2.0 – Convergence of a Parallel Rendering Framework

Stefan Eilemann, David Steiner, Renato Pajarola

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Tags: Algorithms, Computer science, Graphics, nVidia, nVidia GeForce GTX 970, OpenGL, Package, Rendering, Virtual reality, Visualization

March 3, 2018 by hgpu

DALI: a library containing both highly optimized building blocks and an execution engine for data pre-processing in deep learning applications

Importance of Data Loading Pipeline in Training Deep Neural Networks

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

Boston, MA, USA

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

2019

May

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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high performance computing on graphics processing units: hgpu.org

Performance Counters based Power Modeling of Mobile GPUs using Deep Learning

On Demand Solid Texture Synthesis Using Deep 3D Networks

Efficient Spatial Anti-Aliasing Rendering for Line Joins on Vector Maps

OpenCL vs: Accelerated Finite-Difference Digital Synthesis

Surface Compression Using Dynamic Color Palettes

TensorFlow.js: Machine Learning for the Web and Beyond

Optimizing Web Virtual Reality

Cross-Compiling Shading Languages

Migrating from OpenGL ES to Vulkan

Neural scene representation and rendering

A Comparison between GPU-based Volume Ray Casting Implementations: Fragment Shader, Compute Shader, OpenCL, and CUDA

Equalizer 2.0 – Convergence of a Parallel Rendering Framework

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Featured events

8th International Workshop on OpenCL, including SYCLCon, 2019

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