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Face Recognition: A Tutorial on Computational Aspects

Alexander Alling, Nathaniel Powers, Tolga Soyata

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 760, nVidia GeForce GTX 780, Tutorial

January 10, 2015 by hgpu

Disjunctive Normal Networks

Mehdi Sajjadi, Mojtaba Seyedhosseini, Tolga Tasdizen

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Tags: Computer science, Computer vision, CUDA, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia

January 2, 2015 by hgpu

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille

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Tags: Algorithms, CNN, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan

December 26, 2014 by hgpu

Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification

Hongsheng Li, Rui Zhao, Xiaogang Wang

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Tags: Algorithms, CNN, Computer science, Computer vision, CUDA, Machine learning, Neural networks, nVidia, Tesla K40

December 16, 2014 by hgpu

Risk Estimation Without Using Stein’s Lemma — Application to Image Denoising

Sagar Venkatesh Gubbi, Chandra Sekhar Seelamantula

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Tags: ATI, ATI Radeon HD 7950, Computer vision, Image processing, OpenCL, Package, Signal denoising

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

SiftCU: An Accelerated Cuda Based Implementation of SIFT

Mahdi S. Mohammadi, Mehdi Rezaeian

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GT 440 GT, nVidia GeForce GTX 670, Package

December 2, 2014 by hgpu

GPU accelerated feature algorithms for mobile devices

Seth Hall

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Tags: Algorithms, Augmented reality, Clustering, Computer science, Computer vision, OpenCV, OpenGL, Thesis

December 2, 2014 by hgpu

A Framework for Composing High-Performance OpenCL from Python Descriptions

Michael Anderson

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce GTX 480, OpenCL, Optical flow, Python, Thesis

November 29, 2014 by hgpu

Real-time 3D Reconstruction for FPGAs: A Case Study for Evaluating the Performance, Area, and Programmability Trade-offs of the Altera OpenCL SDK

Quentin Gautier, Alexandria Shearer, Janarbek Matai, Dustin Richmond, Pingfan Meng, Ryan Kastner

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Tags: Computer science, Computer vision, FPGA, nVidia, nVidia GeForce GTX 760, OpenCL

November 9, 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

GPU Acceleration of k-Nearest Neighbor Search in Face Classifier based on Eigenfaces

Jennifer Dawn Rouan

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Tags: Computer science, Computer vision, CUDA, Nearest neighbour, nVidia, Security, Tesla K20, Thesis

November 5, 2014 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

See all packages

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high performance computing on graphics processing units: hgpu.org

Face Recognition: A Tutorial on Computational Aspects

Disjunctive Normal Networks

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification

Risk Estimation Without Using Stein’s Lemma — Application to Image Denoising

Neural Networks through Shared Maps in Mobile Devices

SiftCU: An Accelerated Cuda Based Implementation of SIFT

GPU accelerated feature algorithms for mobile devices

A Framework for Composing High-Performance OpenCL from Python Descriptions

Real-time 3D Reconstruction for FPGAs: A Case Study for Evaluating the Performance, Area, and Programmability Trade-offs of the Altera OpenCL SDK

Dogwild! – Distributed Hogwild for CPU & GPU

GPU Acceleration of k-Nearest Neighbor Search in Face Classifier based on Eigenfaces

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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