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hgpu.org » nVidia GeForce GTX 485

OptiX: a general purpose ray tracing engine

Steven G. Parker, James Bigler, Andreas Dietrich, Heiko Friedrich, Jared Hoberock, David Luebke, David McAllister, Morgan McGuire, Keith Morley, Austin Robison, Martin Stich
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September 14, 2011 by hgpu

A Hybrid Programming Model for Compressible Gas Dynamics Using OpenCL

B.K. Bergen, M.G. Daniels, P.M. Weber
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April 1, 2011 by hgpu

Recent source codes

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks
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Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications
ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

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ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications
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Efficient and Scalable k-Means on GPUs
Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

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pyJac: a Python package that generates source code used to analytically calculate chemical kinetics Jacobian matrices

pyJac: a Python package that generates source code used to analytically calculate chemical kinetics Jacobian matrices

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms
CUTLASS: CUDA Templates for Linear Algebra Subroutines

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Kernel Tuner: A search-optimizing GPU code auto-tuner

Kernel Tuner: A search-optimizing GPU code auto-tuner

Kernel Tuner: A search-optimizing GPU code auto-tuner
libxsmm: Library targeting Intel Architecture for specialized dense and sparse matrix operations, and deep learning primitives

libxsmm: Library targeting Intel Architecture for specialized dense and sparse matrix operations, and deep learning primitives

Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures
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