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Efficient GPU implementation of the integral histogram

Mahdieh Poostchi, Kannappan Palaniappan, Filiz Bunyak, Michela Becchi, Guna Seetharaman

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 460, Tesla C2070

March 21, 2013 by hgpu

Accelerating Computer Vision Algorithms Using OpenCL on Mobile GPU – A Case Study

Guohui Wang, Yingen Xiong, Jay Yun, Joseph R. Cavallaro

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Tags: Algorithms, Computer science, Computer vision, Heterogeneous systems, OpenCL

March 16, 2013 by hgpu

GPU implementation of a deep learning network for image recognition tasks

Sean Patrick Parker

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Tags: Computer science, Computer vision, CUDA, Image recognition, Machine learning, nVidia, Thesis

March 12, 2013 by hgpu

OpenOF: Framework for Sparse Non-linear Least Squares Optimization on a GPU

Cornelius Wefelscheid, Olaf Hellwich

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Source codes

Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 570, Optimization, Package, Python

March 2, 2013 by hgpu

A Fast and Efficient SIFT Detector Using the Mobile GPU

Blaine Rister, Guohui Wang, Michael Wu, Joseph R. Cavallaro

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Tags: Computer science, Computer vision, nVidia, nVidia Tegra, OpenGL

February 28, 2013 by hgpu

Multi-GPU based on multicriteria optimization for motion estimation system

Carlos Garcia, Guillermo Botella, Fermin Ayuso, Manuel Prieto, Francisco Tirado

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Tags: Algorithms, Computer vision, CUDA, Data parallelism, nVidia, Optical flow, Signal processing, Tesla C1060, Tesla M2070

February 27, 2013 by hgpu

Parallel Computer Vision: Person Data Extraction

Lang Christian

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 460, Thesis

February 23, 2013 by hgpu

pROST : A Smoothed Lp-norm Robust Online Subspace Tracking Method for Realtime Background Subtraction in Video

Florian Seidel, Clemens Hage, Martin Kleinsteuber

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 660 Ti

February 15, 2013 by hgpu

Efficient Exploitation of Heterogeneous Platforms for Images Features Extraction

Sidi Ahmed Mahmoudi, Pierre Manneback

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Tags: Algorithms, Computer science, Computer vision, CUDA, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 280, Pattern recognition, Tesla C1060

February 14, 2013 by hgpu

Real-Time 3D Face Identification from a Depth Camera

Rui Min, Jongmoo Choi, Gerard Medioni, Jean-Luc Dugelay

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Tags: Computer science, Computer vision, CUDA, nVidia, Tesla C1060

February 14, 2013 by hgpu

A Networked Dataflow Simulation Environment for Signal Processing and Data Mining Applications

Stephen Won

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Tags: Computer vision, CUDA, Data mining, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, SIFT, Signal processing, Thesis

February 12, 2013 by hgpu

Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks

Alessandro Giusti, Dan C. Ciresan, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhuber

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Tags: Artificial intelligence, Computational Complexity, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX 580

February 12, 2013 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

Efficient GPU implementation of the integral histogram

Accelerating Computer Vision Algorithms Using OpenCL on Mobile GPU – A Case Study

GPU implementation of a deep learning network for image recognition tasks

OpenOF: Framework for Sparse Non-linear Least Squares Optimization on a GPU

A Fast and Efficient SIFT Detector Using the Mobile GPU

Multi-GPU based on multicriteria optimization for motion estimation system

Parallel Computer Vision: Person Data Extraction

pROST : A Smoothed Lp-norm Robust Online Subspace Tracking Method for Realtime Background Subtraction in Video

Efficient Exploitation of Heterogeneous Platforms for Images Features Extraction

Real-Time 3D Face Identification from a Depth Camera

A Networked Dataflow Simulation Environment for Signal Processing and Data Mining Applications

Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)