high performance computing on graphics processing units: hgpu.org

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Towards chip-on-chip neuroscience: fast mining of neuronal spike streams using graphics hardware

Yong Cao, Debprakash Patnaik, Sean Ponce, Jeremy Archuleta, Patrick Butler, Wu-chun Feng and Naren Ramakrishnan

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Tags: Computer science, CUDA, Data mining, Neuroscience, nVidia, nVidia GeForce GTX 280, Programming techniques

January 2, 2011 by hgpu

A work-efficient GPU algorithm for level set segmentation

Mike Roberts, Mario Costa Sousa, Joseph Ross Mitchell

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

December 29, 2010 by hgpu

BVH for efficient raytracing of dynamic metaballs on GPU

Olivier Gourmel, Anthony Pajot, Loic Barthe, Mathias Paulin, Pierre Poulin

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Raytracing

December 27, 2010 by hgpu

Interactive Streak Surface Visualization on the GPU

Kai Bürger, Florian Ferstl, Holger Theisel, Rudiger Westermann

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Tags: Computer science, DirectX, nVidia, nVidia GeForce GTX 280, Visualization

December 27, 2010 by hgpu

Towards Chip-on-Chip Neuroscience: Fast Mining of Frequent Episodes Using Graphics Processors

Yong Cao, Debprakash Patnaik, Sean Ponce, Jeremy Archuleta, Patrick Butler, Wu-chun Feng, Naren Ramakrishnan

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Tags: Computer science, CUDA, Databases, Neuroscience, nVidia, nVidia GeForce GTX 280

December 27, 2010 by hgpu

Spatio-temporal upsampling on the GPU

Robert Herzog, Elmar Eisemann, Karol Myszkowski, H.-P. Seidel

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Tags: 3D Graphics and Realism, Computer science, GLSL, nVidia, nVidia GeForce GTX 280, OpenGL, Rendering

December 26, 2010 by hgpu

Parallel High Resolution Real-time Visual Hull On GPU

Wolfgang Waizenegger, Ingo Feldmann, Peter Eisert, Peter Kauff

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Tags: Computer science, CUDA, Image reconstruction, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Real-time graphics

December 26, 2010 by hgpu

Singular value decomposition on GPU using CUDA

Sheetal Lahabar, P. J. Narayanan

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Tags: Computer science, Linear Algebra, nVidia, nVidia GeForce GTX 280, Tesla S1070

December 24, 2010 by hgpu

Parallel hybrid evolutionary algorithms on GPU

The Van Luong, Nouredine Melab, El-Ghazali Talbi

Tags: Computer science, CUDA, Data mining, Metaheuristics, nVidia, nVidia GeForce GTX 280

December 23, 2010 by hgpu

Real-Time Online Video Object Silhouette Extraction Using Graph Cuts on the GPU

Zachary A. Garrett, Hideo Saito

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

December 22, 2010 by hgpu

Pushing the Envelope: Extreme Network Coding on the GPU

Hassan Shojania, Baochun Li

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques

December 22, 2010 by hgpu

MIMD Interpretation on a GPU

Henry G. Dietz, B. Dalton Young

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Tags: Algorithms, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce 8600 M GT, nVidia GeForce 8800 GTS, nVidia GeForce 9800 GT, nVidia GeForce GTX 280

December 21, 2010 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

Towards chip-on-chip neuroscience: fast mining of neuronal spike streams using graphics hardware

A work-efficient GPU algorithm for level set segmentation

BVH for efficient raytracing of dynamic metaballs on GPU

Interactive Streak Surface Visualization on the GPU

Towards Chip-on-Chip Neuroscience: Fast Mining of Frequent Episodes Using Graphics Processors

Spatio-temporal upsampling on the GPU

Parallel High Resolution Real-time Visual Hull On GPU

Singular value decomposition on GPU using CUDA

Parallel hybrid evolutionary algorithms on GPU

Real-Time Online Video Object Silhouette Extraction Using Graph Cuts on the GPU

Pushing the Envelope: Extreme Network Coding on the GPU

MIMD Interpretation on a GPU

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)