high performance computing on graphics processing units: hgpu.org

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Harnessing the power of idle GPUs for acceleration of biological sequence alignment

Fumihiko Ino, Yuki Kotani, Yuma Munekawa, Kenichi Hagihara

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce 7900 GTX, nVidia GeForce 7950 GX2, nVidia GeForce 8800 GTX, OpenGL, Sequence alignment

July 12, 2011 by hgpu

Fast gain-adaptive KLT tracking on the GPU

Christopher Zach, David Gallup, Jan-Michael Frahm

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Tags: Cg, Computer science, Computer vision, CUDA, GLSL, nVidia, nVidia GeForce 7950 GX2, nVidia GeForce 8400 GS, nVidia GeForce 8800 Ultra, OpenGL, Package, Video tracking

May 12, 2011 by hgpu

nVidia GeForce 7950 GX2

Tags: nVidia, nVidia GeForce 7950 GX2, nVidia GeForce 7xxx

March 17, 2011 by hgpu

GPU-Based Volume Rendering for Medical Imagery

Hadjira Bentoumi, Pascal Gautron, Kadi Bouatouch

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Tags: Medicine, nVidia, nVidia GeForce 7950 GX2, OpenGL, Rendering, Visualization

March 16, 2011 by hgpu

“Local Rank Differences” Image Feature Implemented on GPU

Lukas Polok, Adam Herout, Pavel Zemcik, Michal Hradis, Roman Juranek, Radovan Josth

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Tags: Cg, Computer science, Image processing, Image recognition, nVidia, nVidia GeForce 7950 GX2

December 9, 2010 by hgpu

Using Graphics Hardware for Enhancing Edge and Circle Detection

Antonio Ruiz, Manuel Ujaldon, Nicolas Guil

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Tags: Cg, Computer vision, Image processing, nVidia, nVidia GeForce 7950 GX2, OpenGL

December 5, 2010 by hgpu

A Fast Implementation of the Octagon Abstract Domain on Graphics Hardware

Francesco Banterle, Roberto Giacobazzi

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Tags: Computer science, nVidia, nVidia GeForce 7950 GX2, Octagon Abstract Domain, OpenGL

November 4, 2010 by hgpu

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

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

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

Harnessing the power of idle GPUs for acceleration of biological sequence alignment

Fast gain-adaptive KLT tracking on the GPU

nVidia GeForce 7950 GX2

GPU-Based Volume Rendering for Medical Imagery

“Local Rank Differences” Image Feature Implemented on GPU

Using Graphics Hardware for Enhancing Edge and Circle Detection

A Fast Implementation of the Octagon Abstract Domain on Graphics Hardware

Recent source codes

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

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

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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