high performance computing on graphics processing units: hgpu.org

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Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

Balazs Nagy, Philipp Foehn, Davide Scaramuzza

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Tags: Algorithms, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 960 M, nVidia Jetson TX2, OpenCV, Package

April 5, 2020 by hgpu

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

Murad Qasaimeh, Kristof Denolf, Jack Lo, Kees Vissers, Joseph Zambreno, Phillip H. Jones

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Tags: ARM, Benchmarking, Computer science, Computer vision, CUDA, FPGA, nVidia, nVidia Jetson TX2, OpenCV, Package

May 26, 2019 by hgpu

CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++

Xiaolin Wang

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, OpenCV, Package

April 22, 2018 by hgpu

A Test Drive of the NVIDIA Jetson TX1 Developer Kit for Deep Learning and Computer Vision Applications

BDTI staff

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Tags: Algorithms, Caffe, Computer science, Computer vision, CUDA, Deep learning, nVidia, nVidia Jetson TK1, nVidia Tegra TX1, OpenCV

July 5, 2016 by hgpu

Hierarchical Semantic Parsing for Object Pose Estimation in Densely Cluttered Scenes

Chi Li, Jonathan Bohren, Eric Carlson, Gregory D. Hager

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Tags: Computer science, Computer vision, CUDA, nVidia, OpenCV, Package, SIFT

March 3, 2016 by hgpu

Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs

Liang-Chieh Chen

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 650 M, OpenCV, Tesla K40, Thesis

November 24, 2015 by hgpu

gSLICr: SLIC superpixels at over 250Hz

Carl Yuheng Ren, Victor Adrian Prisacariu, Ian D Reid

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Tags: Clustering, Computer science, Computer vision, CUDA, nVidia, OpenCV, Package

September 17, 2015 by hgpu

High-Speed Object Detection: Design, Study and Implementation of a Detection Framework using Channel Features and Boosting

Tom Runia

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Tags: ATI, ATI Radeon HD 6450, Computer science, Computer vision, Image processing, Machine learning, nVidia, nVidia Jetson TK1, nVidia Tegra, OpenCL, OpenCV, Thesis

August 28, 2015 by hgpu

Target Marker: A Visual Marker for Long Distances and Detection in Realtime on Mobile Devices

Oliver Christen, Edwin Naroska, Alexander Micheel, Shanq-Jang Ruan

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Tags: Computer science, Computer vision, Image recognition, OpenCL, OpenCV

August 5, 2015 by hgpu

Raspberry Pi based System for Visual Object Detection and Tracking

Aleksei Tepljakov

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Tags: ARM, Computer science, Computer vision, OpenCV, Thesis

July 22, 2015 by hgpu

Robust real time face recognition and tracking on gpu using fusion of rgb and depth image

Narmada Naik, G.N Rathna

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Tags: Algorithms, ATI, ATI Radeon HD 7670 M, Computer science, Computer vision, Face detection, OpenCL, OpenCV

April 12, 2015 by hgpu

Document Image Binarization Using Image Segmentation Algorithm in Parallel Environment

Prakash N Ekhande, Sharad A Rumane, Mayur A Ahire

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Tags: Algorithms, CUDA, Image processing, nVidia, OpenCV

February 23, 2015 by hgpu

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

GSParLib

GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

KSig: GPU-accelerated computation of popular time series kernels

A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel

IterApp: Iterative Application Skeleton

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

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

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

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high performance computing on graphics processing units: hgpu.org

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++

A Test Drive of the NVIDIA Jetson TX1 Developer Kit for Deep Learning and Computer Vision Applications

Hierarchical Semantic Parsing for Object Pose Estimation in Densely Cluttered Scenes

Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs

gSLICr: SLIC superpixels at over 250Hz

High-Speed Object Detection: Design, Study and Implementation of a Detection Framework using Channel Features and Boosting

Target Marker: A Visual Marker for Long Distances and Detection in Realtime on Mobile Devices

Raspberry Pi based System for Visual Object Detection and Tracking

Robust real time face recognition and tracking on gpu using fusion of rgb and depth image

Document Image Binarization Using Image Segmentation Algorithm in Parallel Environment

Recent source codes

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

GSParLib

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

KSig: GPU-accelerated computation of popular time series kernels

IterApp: Iterative Application Skeleton

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

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

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