high performance computing on graphics processing units: hgpu.org

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Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors

Eric M. Aldrich, Jesus Fernandez-Villaverde, A. Ronald Gallant, Juan F. Rubio-Ramirez

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Tags: Algorithms, CUDA, Finance, nVidia, nVidia GeForce GTX 280, Package

November 18, 2011 by hgpu

Accelerating The Cloud with Heterogeneous Computing

Sahil Suneja, Elliott Baron, Eyal de Lara, Ryan Johnson

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Tags: ATI, ATI Radeon HD 5870, Cloud, Computer science, Hashing, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, OpenCL

November 18, 2011 by hgpu

GPU Acceleration of Matrix-based Methods in Computational Electromagnetics

Evan Lezar

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Tags: CUDA, Electrodynamics, FEM, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 465, OpenCL, Tesla M1060, Thesis

November 10, 2011 by hgpu

Low Complexity Corner Detector Using CUDA for Multimedia Applications

Rajat Phull, Pradip Mainali, Qiong Yang, Patrice Rondao Alface, Henk Sips

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 280

November 9, 2011 by hgpu

Parallelization of algorithms for solving the Boltzmann equation for GPU-based computations

E.A. Malkov, M.S. Ivanov

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Tags: Algorithms, Boltzmann equation, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Physics

November 6, 2011 by hgpu

Advanced MRI reconstruction toolbox with accelerating on GPU

Xiao-Long Wu, Yue Zhuo, Fan Lam, Maojing Fu, Justin P. Haldar, Wen-Mei Hwu, Zhi-Pei Liang, Bradley P. Sutton, Jiading Gai

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Tags: Algorithms, CUDA, FFT, Image reconstruction, Magnetic resonance imaging, MRI, nVidia, nVidia GeForce GTX 280, Optimization, Physics

November 6, 2011 by hgpu

Accelerating a TV based JPEG decompression algorithm with Cuda

Martin Holler

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Tags: Algorithms, Compression, CUDA, Image processing, nVidia, nVidia GeForce GTX 280

November 4, 2011 by hgpu

Parallelization of the Generalized Hough Transform on GPU

Juan Gomez-Luna, Jose Maria Gonzalez-Linares, Jose Ignacio Benavides, Emilio L. Zapata, Nicolas Guil

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Tags: CUDA, Image processing, nVidia, nVidia GeForce GTX 280, Sorting

November 3, 2011 by hgpu

GrIP: A Framework for Experiments with Screen Space Algorithms

Thorsten Roth, Andre Hinkenjann

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480

November 3, 2011 by hgpu

GPU Computations in Heterogeneous Grid Environments

Marcus Hinders

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Tags: ATI, ATI Radeon HD 5870, Biology, Computational biology, Computer science, Grid, Heterogeneous systems, nVidia, nVidia GeForce 8600 GT, nVidia GeForce GTX 280, OpenCL, Thesis

October 30, 2011 by hgpu

CPU and GPU Co-processing for Sound

Aleksander Gjermundsen

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Computer science, Filtering, Linear Algebra, nVidia, nVidia GeForce GTX 280, OpenCL, Package, Signal processing, Tesla C2050, Thesis

October 30, 2011 by hgpu

The CUDA implementation of the method of lines for the curvature dependent flows

Tomas Oberhuber, Atsushi Suzuki, Vitezslav Zabka

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Tags: Algorithms, CUDA, Differential equations, Finite volume method, Image processing, Mathematics, nVidia, nVidia GeForce GTX 280, ODEs, Ordinary differential equations

October 27, 2011 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

Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors

GPU Acceleration of Matrix-based Methods in Computational Electromagnetics

Low Complexity Corner Detector Using CUDA for Multimedia Applications

Parallelization of algorithms for solving the Boltzmann equation for GPU-based computations

Advanced MRI reconstruction toolbox with accelerating on GPU

Accelerating a TV based JPEG decompression algorithm with Cuda

Parallelization of the Generalized Hough Transform on GPU

GrIP: A Framework for Experiments with Screen Space Algorithms

GPU Computations in Heterogeneous Grid Environments

CPU and GPU Co-processing for Sound

The CUDA implementation of the method of lines for the curvature dependent flows

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)