high performance computing on graphics processing units: hgpu.org

Julien Bert, Hector Perez-Ponce, Sebastien Jan, Ziad El Bitar, Pierre Gueth, Vesna Cuplov, Hocine Chekatt, Didier Benoit, David Sarrut, Yannick Boursier, David Brasse, Irene Buvat, Christian Morel, Dimitris Visvikis

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Tags: CUDA, Image processing, Medicine, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580

January 4, 2013 by hgpu

Computing Spectral Transforms Used in Digital Logic on the GPU

Dusan Gajic, Radomir S. Stankovic

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Tags: ATI, ATI Radeon HD 5650, FFT, OpenCL, Signal processing

December 30, 2012 by hgpu

An Overview of Miscellaneous Applications of GPU Computing

Stanislav Stankovic, Jaakko Astola

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Tags: Overview, Signal processing

December 30, 2012 by hgpu

Vision based Navigation (VBN) of Unmanned Aerial Vehicles (UAV)

Bassem Ibrahim Sheta

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Tags: Algorithms, CUDA, Earth and Space Sciences, Image processing, nVidia, nVidia GeForce GT 540 M, Thesis

December 30, 2012 by hgpu

Fast Retinal Vessel Analysis

Michael Krause, Ralph Maria Alles, Bernhard Burgeth, Joachim Weickert

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Tags: Algorithms, CUDA, Image processing, Mathematics, Medicine, nVidia, nVidia GeForce GTX 680, Radon transform

December 29, 2012 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

Applications

A Method to Improve Interest Point Detection and its GPU Implementation

Probabilistic View-based 3D Curve Skeleton Computation on the GPU

Optimal Image Upscaling Using Pixel Classification

A Fast and Accurate GHT Implementation on CUDA

A Multi-GPU Programming Library for Real-Time Applications

High Performance Multi-dimensional (2D/3D) FFT-Shift Implementation on Graphics Processing Units (GPUs)

High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms

Hybrid GATE: A GPU/CPU implementation for imaging and therapy applications

Computing Spectral Transforms Used in Digital Logic on the GPU

An Overview of Miscellaneous Applications of GPU Computing

Vision based Navigation (VBN) of Unmanned Aerial Vehicles (UAV)

Fast Retinal Vessel Analysis

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)