high performance computing on graphics processing units: hgpu.org

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Theano-based Large-Scale Visual Recognition with Multiple GPUs

Weiguang Ding, Ruoyan Wang, Fei Mao, Graham Taylor

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Tags: Computer science, CUDA, Data parallelism, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Package, Python

December 9, 2014 by hgpu

MLitB: Machine Learning in the Browser

Edward Meeds, Remco Hendriks, Said al Faraby, Magiel Bruntink, Max Welling

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Tags: Algorithms, Computer science, Distributed computing, Heterogeneous systems, Java, Machine learning, Neural networks, Package

December 9, 2014 by hgpu

Neural Networks through Shared Maps in Mobile Devices

William Raveane, Maria Angelica Gonzalez Arrieta

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Tags: Computer science, Computer vision, Image recognition, Machine learning, Neural networks, OpenGL

December 7, 2014 by hgpu

An Approach for Maximizing Performance on Heterogeneous Clusters of CPU and GPU

Nam-Luc Tran, Sabri Skhiri, Arnaud Schils, Edgar Isaac Hiroshi Leon Saiki

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan, Package

December 2, 2014 by hgpu

An Open-Source GPU-Accelerated Feature Extraction Tool

Josef Michalek, Jan Vanek

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 660, OpenCL, Speech recognition

December 1, 2014 by hgpu

Dogwild! – Distributed Hogwild for CPU & GPU

Cyprien Noel, Simon Osindero

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Tags: Computer science, Computer vision, CUDA, Distributed computing, Machine learning, Neural networks, nVidia

November 9, 2014 by hgpu

Sparse Recovery on GPUs: Accelerating the Iterative Soft-Thresholding Algorithm

Achal Shah, Angshul Majumdar

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Tags: Algorithms, CUDA, Machine learning, nVidia, nVidia GeForce GTX 680, Signal processing

October 29, 2014 by hgpu

Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

Daniel Povey, Xiaohui Zhang, Sanjeev Khudanpur

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, Speech recognition, Tesla K10, Tesla K20

October 29, 2014 by hgpu

Gaussian Process Models with Parallelization and GPU acceleration

Zhenwen Dai, Andreas Damianou, James Hensman, Neil Lawrence

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Tags: Algorithms, Computer science, Machine learning, nVidia, nVidia GeForce GTX 480

October 24, 2014 by hgpu

Scalable approximate k-NN in multidimensional big data

Hisham Mohamed

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Tags: Algorithms, Computer science, CUDA, Data mining, Data Structures and Algorithms, Information Retrieval, Machine learning, MapReduce, MPI, Nearest neighbour, nVidia, nVidia Quadro NVS 5200 M, Thesis, Visualization

October 13, 2014 by hgpu

cuDNN: Efficient Primitives for Deep Learning

Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer

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Tags: Computer science, CUDA, Machine learning, Mathematical Software, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 980, Tesla K40

October 8, 2014 by hgpu

Load Balancing in Data Warehouse – Evolution and Perspectives

Michal Gorawski

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Tags: Algorithms, Cloud, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce 9600 GT, nVidia GeForce GTX 260

October 6, 2014 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

See all packages

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high performance computing on graphics processing units: hgpu.org

Theano-based Large-Scale Visual Recognition with Multiple GPUs

MLitB: Machine Learning in the Browser

Neural Networks through Shared Maps in Mobile Devices

An Approach for Maximizing Performance on Heterogeneous Clusters of CPU and GPU

An Open-Source GPU-Accelerated Feature Extraction Tool

Dogwild! – Distributed Hogwild for CPU & GPU

Sparse Recovery on GPUs: Accelerating the Iterative Soft-Thresholding Algorithm

Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

Gaussian Process Models with Parallelization and GPU acceleration

Scalable approximate k-NN in multidimensional big data

cuDNN: Efficient Primitives for Deep Learning

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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