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Real-Time Implementation of the Pixel Purity Index Algorithm for Endmember Identification on GPUs

Xianyun Wu, Bormin Huang, Antonio Plaza, Yunsong Li, Chengke Wu

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Tags: Algorithms, Computational Complexity, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 590, Tesla C1060

March 23, 2013 by hgpu

Performance study of filtered back-projection algorithms implemented on GPUs

Bruno Faria

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Tags: Computed tomography, CT, CUDA, Image processing, Medicine, nVidia, nVidia GeForce GTX 580, Performance, Tomography

March 21, 2013 by hgpu

Automated and interactive approaches for optimal surface finding based segmentation of medical image data

Shanhui Sun

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Tags: CUDA, Image processing, Medicine, nVidia, Tesla C1060, Thesis, Ultrasound, Visualization

March 14, 2013 by hgpu

Parallel Particle Swarm Optimization for Image Segmentation

Agustinus Kristiadi, Pranowo Pranowo, Paulus Mudjihartono

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Tags: Clustering, CUDA, Image processing, nVidia, nVidia GeForce G 110 M, Particle swarm optimization

March 14, 2013 by hgpu

CPU and/or GPU: Revisiting the GPU Vs. CPU Myth

Kishore Kothapalli, Dip Sankar Banerjee, P. J. Narayanan, Surinder Sood, Aman Kumar Bahl, Shashank Sharma, Shrenik Lad, Krishna Kumar Singh, Kiran Matam, Sivaramakrishna Bharadwaj, Rohit Nigam, Parikshit Sakurikar, Aditya Deshpande, Ishan Misra, Siddharth Choudhary, Shubham Gupta

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Tags: Computer science, Databases, Hybrid computing, Image processing, nVidia, nVidia GeForce GT 520, Sparse matrix, Tesla T10

March 14, 2013 by hgpu

Efficient Detection of Sunspots with GPU Acceleration Through CUDA

Kenny May

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Tags: Astrophysics, CUDA, Image processing, nVidia, nVidia GeForce GT 430, nVidia GeForce GTX 470, Tesla C2050

March 2, 2013 by hgpu

Advanced Video Coding on CPUs and GPUs: Parallelization and RD Analysis

Svetislav Momcilovic, Aleksandar Ilic, Nuno Roma, Leonel Sousa

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Tags: Algorithms, Compression, CUDA, H.264/AVC, Image processing, nVidia, Signal processing, Video encoding

February 25, 2013 by hgpu

Investigating performance variations of an optimized GPU-ported granulometry algorithm

Vincent Boulos, Vincent Fristot, Dominique Houzet, Luc Salvo, Pierre Lhuissier

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Tags: Algorithm optimization, Algorithms, CUDA, FEM, Finite element method, Image processing, Materials Science, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, nVidia Quadro FX 4000

February 22, 2013 by hgpu

Using High Performance Computing to Improve Image Guided Cancer Treatment

Dante Gama Dessavre

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Tags: CUDA, Image processing, Medicine, nVidia, Radiotherapy, Tesla C2050, Thesis

February 18, 2013 by hgpu

MATLAB and Python for GPU Computing

Jose Unpingco, Juan Carlos Chaves

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Tags: Computer science, CUDA, Image processing, nVidia, nVidia GeForce 9800 GTS, nVidia GeForce 9800 GX2, Python, Signal processing, Tesla C1060, Tesla C2050

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

A volume segmentation approach based on GrabCut

Esmitt Ramirez J., Pablo Temoche, Rhadames Carmona

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GT 240, nVidia GeForce GTX 470

February 12, 2013 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

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

GPU plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

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