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SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs

Kaiwei Li, Jianfei Chen, Wenguang Chen, Jun Zhu

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Tags: Algorithms, Computer science, CUDA, Information Retrieval, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X

October 12, 2016 by hgpu

Implementation of Frequency Domain Convolution for the Caffe-Framework

Philipp Harzig

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Tags: Caffe, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, Thesis

October 8, 2016 by hgpu

A New Architecture for Optimization Modeling Frameworks

Matt Wytock, Steven Diamond, Felix Heide, Stephen Boyd

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Tags: Algorithms, CUDA, Deep learning, Linear Algebra, Mathematics, nVidia, nVidia GeForce GTX Titan X, Package, Python

September 13, 2016 by hgpu

3D Object Recognition with Convolutional Neural Networks

Alberto Garcia-Garcia

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Tags: CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GT 730, nVidia GeForce GTX Titan X, Tesla K40, Thesis

September 10, 2016 by hgpu

Optimizing Memory Efficiency for Deep Convolutional Neural Networks on GPUs

Chao Li, Yi Yang, Min Feng, Chakradhar Srimat, Huiyang Zhou

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Tags: CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia GeForce GTX Titan X

September 5, 2016 by hgpu

Ultra-Fast Detection of Higher-Order Epistatic Interactions on GPUs

Daniel Junger, Christian Hundt, Jorge Gonzalez-Dominguez, Bertil Schmidt

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Tags: Algorithms, Bioinformatics, Clustering, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX Titan X

September 3, 2016 by hgpu

Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs

Ferdinando Fioretto, Enrico Pontelli, William Yeoh, Rina Dechter

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Tags: Algorithms, Artificial intelligence, Bayesian, Computer science, nVidia, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Optimization, Package, Tesla M2075

August 23, 2016 by hgpu

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, Lei Zhang

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Tags: CNN, CUDA, Deep learning, Image processing, Matlab, nVidia, nVidia GeForce GTX Titan X, Package, Signal denoising

August 18, 2016 by hgpu

Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment

Balint Pal Toth, Balint Czeba

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Tags: CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan X, Speech recognition

August 16, 2016 by hgpu

Near Memory Similarity Search on Automata Processors

Vincent T. Lee, Justin Kotalik, Carlo C. Del Mundo, Armin Alaghi, Luis Ceze, Mark Oskin

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Tags: ARM, Computer science, CUDA, FPGA, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, nVidia Jetson TK1

August 16, 2016 by hgpu

Accelerating Eulerian Fluid Simulation With Convolutional Networks

Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin

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Tags: 3D Graphics and Realism, CNN, Computational Complexity, CUDA, Deep learning, Fluid dynamics, Fluid simulation, Machine learning, nVidia, nVidia GeForce GTX Titan X

July 16, 2016 by hgpu

Fast Predictive Image Registration

Xiao Yang, Roland Kwitt, Marc Niethammer

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Tags: Bayesian, Computer science, Computer vision, CUDA, Deep learning, Image registration, nVidia, nVidia GeForce GTX Titan X, Package, Python

July 11, 2016 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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high performance computing on graphics processing units: hgpu.org

SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs

Implementation of Frequency Domain Convolution for the Caffe-Framework

A New Architecture for Optimization Modeling Frameworks

3D Object Recognition with Convolutional Neural Networks

Optimizing Memory Efficiency for Deep Convolutional Neural Networks on GPUs

Ultra-Fast Detection of Higher-Order Epistatic Interactions on GPUs

Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment

Near Memory Similarity Search on Automata Processors

Accelerating Eulerian Fluid Simulation With Convolutional Networks

Fast Predictive Image Registration

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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