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GPU-Based Super-union for Minkowski Sum

Yuen- Shan Leung, Charlie C. L. Wang, Yong Chen

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Voxelization

January 10, 2013 by hgpu

A Fast and Accurate GHT Implementation on CUDA

Nikhil Jotwani, Sudhakar Sah

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

January 10, 2013 by hgpu

An implementation for quad-tree based solid object coloring using CUDA

Baha Sen, Caner Ozcan, Nesrin Aydin Atasoy

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 560 Ti, nVidia Quadro FX 2000

January 9, 2013 by hgpu

A Multi-GPU Programming Library for Real-Time Applications

Sebastian Schaetz, Martin Uecker

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Tags: Algorithms, CUDA, GPU cluster, Image reconstruction, Magnetic resonance imaging, MPI, MRI, nVidia, nVidia GeForce GTX 580, Performance, Signal processing

January 8, 2013 by hgpu

Low cost approach to real-time vehicle to vehicle communication using parallel CPU and GPU processing

Goh Chia Chieh, Dino Isa

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 M GTX, Vehicular traffic

January 8, 2013 by hgpu

Optimizations in Bioinformatics using GPU Processing on Binary Data

Manuel Belmadani

Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 285

January 8, 2013 by hgpu

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

Marwan Abdellah, Salah Saleh, Ayman Eldeib, Amr Shaarawi

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Tags: CUDA, FFT, nVidia, nVidia GeForce GTX 570, Rendering, Signal processing

January 8, 2013 by hgpu

Implementation of FDTD-Compatible Green’s Function on Heterogeneous CPU-GPU Parallel Processing System

Tomasz P. Stefanski

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Tags: CUDA, Electrodynamics, FDTD, Finite-difference time-domain, Heterogeneous systems, nVidia, nVidia GeForce GTX 680

January 8, 2013 by hgpu

Efficient Weighted Histogramming on GPUs with CUDA

Mo Xu, Ningyi Xu, Chunshui Zhao, Feng-Hsiung Hsu

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Tags: Computer science, CUDA, Data mining, Machine learning, nVidia, nVidia GeForce GTX 580

January 8, 2013 by hgpu

Distributed Massive Model Rendering

Revanth N R, P. J. Narayanan

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia Quadro FX 3700 M, Rendering, Tesla C2050, Tesla C2070, Tesla S1070

January 8, 2013 by hgpu

GPU-Optimized Coarse-Grained MD Simulations of Protein and RNA Folding and Assembly

Andrew J. Proctor, Tyson J. Lipscomb, Anqi Zou, Joshua A. Anderson, Samuel S. Cho

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Tags: Bioinformatics, Biology, CUDA, Macromolecule, Molecular dynamics, nVidia, RNA, Tesla C2070

January 8, 2013 by hgpu

CUDA based iterative methods for linear systems

Bogdan Oancea

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280

January 7, 2013 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

GPU-Based Super-union for Minkowski Sum

A Fast and Accurate GHT Implementation on CUDA

An implementation for quad-tree based solid object coloring using CUDA

A Multi-GPU Programming Library for Real-Time Applications

Low cost approach to real-time vehicle to vehicle communication using parallel CPU and GPU processing

Optimizations in Bioinformatics using GPU Processing on Binary Data

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

Implementation of FDTD-Compatible Green’s Function on Heterogeneous CPU-GPU Parallel Processing System

Efficient Weighted Histogramming on GPUs with CUDA

Distributed Massive Model Rendering

GPU-Optimized Coarse-Grained MD Simulations of Protein and RNA Folding and Assembly

CUDA based iterative methods for linear systems

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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