high performance computing on graphics processing units: hgpu.org

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Implementation of 3D Monte Carlo PET reconstruction algorithm on GPU

A. Wirth, A. Cserkaszky, B. Kari, D. Legrady, S. Feher, S. Czifrus, B. Domonkos

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Tags: Computed tomography, CT, Image processing, Image reconstruction, Medicine, Monte Carlo simulation, nVidia, nVidia GeForce 9600 GT, nVidia GeForce GTX 280, nVidia GeForce GTX 285, PET

May 19, 2011 by hgpu

Object oriented framework for real-time image processing on GPU

Nicolas Seiller, Nitin Singhal, In Kyu Park

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Tags: CUDA, GLSL, Image processing, nVidia, nVidia GeForce GTX 280, Real-time graphics

May 15, 2011 by hgpu

A GPU-based architecture for real-time data assessment at synchrotron experiments

Suren Chilingaryan, Alessandro Mirone, Andrew Hammersley,Claudio Ferrero, Lukas Helfen, Andreas Kopmann, Tomy dos Santos Rolo

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Tags: Biology, Image reconstruction, Materials Science, Medicine, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295, nVidia GeForce GTX 480, Poster, Tomography

May 8, 2011 by hgpu

Large-scale multi-dimensional document clustering on GPU clusters

Yongpeng Zhang, Frank Mueller, Xiaohui Cui, Thomas Potok

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Tags: Computer science, CUDA, Data Structures and Algorithms, GPU cluster, MPI, nVidia, nVidia GeForce GTX 280

May 6, 2011 by hgpu

Graphics hardware & GPU computing: past, present, and future

David Luebke

Tags: Computer science, CUDA, Games, nVidia, nVidia GeForce GTX 280, Review

May 4, 2011 by hgpu

Realtime affine-photometric KLT feature tracker on GPU in CUDA framework

Jun-Sik Kim, Myung Hwangbo, Takeo Kanade

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Tags: Computer vision, CUDA, Image processing, nVidia, nVidia GeForce 8600 M, nVidia GeForce 8800 GTX, nVidia GeForce 8800 Ultra, nVidia GeForce GTX 280, Optical flow, Video tracking

April 27, 2011 by hgpu

Tracking and Clustering Salient Features in Image Sequences

Werner Bailer, Hannes Fassold, Felix Lee, Jakub Rosner

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Rendering, Video tracking

April 25, 2011 by hgpu

Physically-Based Interactive Flow Visualization Based on Schlieren and Interferometry Experimental Techniques

Carson Brownlee, Vincent Pegoraro, Siddharth Shankar, Patrick S. McCormick, Charles D. Hansen

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Tags: CUDA, Fluid dynamics, Fluid simulation, nVidia, nVidia GeForce GTX 280

April 23, 2011 by hgpu

A performance prediction model for the CUDA GPGPU platform

Kishore Kothapalli, Rishabh Mukherjee, M. Suhail Rehman, Suryakant Patidar, P. J. Narayanan, Kannan Srinathan

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Performance

April 21, 2011 by hgpu

Linear optimization on modern GPUs

Daniele G. Spampinato, Anne C. Elster

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Tags: CUDA, Finance, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Optimization

April 18, 2011 by hgpu

Physically-based interactive schlieren flow visualization

C. Brownlee, V. Pegoraro, S. Shankar, P. McCormick, C. Hansen

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Tags: CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 280, Visualization

April 17, 2011 by hgpu

An Efficient Acceleration of Symmetric Key Cryptography Using General Purpose Graphics Processing Unit

Fan Wu, Chung-han Chen, Hira Narang

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Security

April 17, 2011 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_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

See all packages

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high performance computing on graphics processing units: hgpu.org

Implementation of 3D Monte Carlo PET reconstruction algorithm on GPU

Object oriented framework for real-time image processing on GPU

A GPU-based architecture for real-time data assessment at synchrotron experiments

Large-scale multi-dimensional document clustering on GPU clusters

Graphics hardware & GPU computing: past, present, and future

Realtime affine-photometric KLT feature tracker on GPU in CUDA framework

Tracking and Clustering Salient Features in Image Sequences

Physically-Based Interactive Flow Visualization Based on Schlieren and Interferometry Experimental Techniques

A performance prediction model for the CUDA GPGPU platform

Linear optimization on modern GPUs

Physically-based interactive schlieren flow visualization

An Efficient Acceleration of Symmetric Key Cryptography Using General Purpose Graphics Processing Unit

Recent source codes

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

ROCm's implementation of Gromacs

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

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