high performance computing on graphics processing units: hgpu.org

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On-the-fly Generation and Rendering of Infinite Cities on the GPU

Markus Steinberger, Michael Kenzel, Bernhard Kainz, Peter Wonka, Dieter Schmalstieg

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia Quadro 6000, OpenGL, Rendering

January 9, 2014 by hgpu

GLSL Essentials

Jacobo Rodriguez

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Tags: 3D Graphics and Realism, Book, Computer science, GLSL, OpenGL, Rendering, Tutorial

January 5, 2014 by hgpu

Crack-free rendering of dynamically tesselated B-Rep models

Frederic Claux, Loic Barthe, David Vanderhaeghe, Jean-Pierre Jessel, Mathias Paulin

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Tags: 3D Graphics and Realism, Algorithms, Computer science, Dynamic surface, Image generation, nVidia, nVidia GeForce GTX 780, Rendering, Shaders

January 5, 2014 by hgpu

Multi-GPU Load Balancing for In-Situ Simulation and Visualization

Yong Cao, Robert Hagan

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Tags: Computer science, nVidia, nVidia GeForce GTX 295, Rendering, Task scheduling, Visualization

December 27, 2013 by hgpu

GPU hardware acceleration for industrial applications: using computation to push beyond physical limitations

Mohammadhossein Afrasiabi

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Tags: Algorithms, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 580, nVidia GeForce GTX 590, Rendering, Sensing, Software Engineering, Thesis

December 13, 2013 by hgpu

Efficient Volume Rendering in CUDA Path Tracer

Hsuan-Yueh Peng, Kristopher Wong, Kaleb Williams

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 770, Raytracing, Rendering

December 9, 2013 by hgpu

A GPU-based Multiresolution Pipeline for Compressed Volume Rendering

Julian Lamas-Rodriguez, Francisco Arguello, Dora B. Heras

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Rendering, Visualization

December 8, 2013 by hgpu

Fractal Based Method on Hardware Acceleration for Natural Environments

Divya Udayan J, HyungSeok Kim, Jun Lee, Jee-In Kim

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 650, Rendering

November 30, 2013 by hgpu

Evaluation and Improvement of GPU Ray Tracing with a Thread Migration Technique

Xingxing Zhu, Yangdong Deng

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 560 Ti, Rasterization, Raytracing, Rendering

November 26, 2013 by hgpu

Real-time rendering of large surface-scanned range data natively on a GPU

Sajid Farooq

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Tags: ATI, ATI Radeon HD 4870 X2, ATI Radeon HD 5870, Computer science, GLSL, nVidia, nVidia GeForce 8800 GTX, OpenGL, Rendering, Thesis

November 19, 2013 by hgpu

Ray-Tracing Based Interactive Camera Simulation

Damon Shing-Min Liu, Che-Wei Hsu

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Tags: Algorithms, Computer science, nVidia, nVidia Quadro 600, OpenGL, Optics, Raytracing, Rendering

November 16, 2013 by hgpu

Real-Time Screen Space Rendering of Cartoon Water

Liordino dos S. Rocha Neto, Filipe Deo Guimaraes, Antonio L. Apolinario Jr., Vinicius M. Mello

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Tags: 3D Graphics and Realism, Computer science, CUDA, Fluid simulation, Games, GLSL, nVidia, nVidia GeForce GTX 680, OpenGL, Rendering, Visualization

November 14, 2013 by hgpu

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

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

See all packages

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high performance computing on graphics processing units: hgpu.org

On-the-fly Generation and Rendering of Infinite Cities on the GPU

GLSL Essentials

Crack-free rendering of dynamically tesselated B-Rep models

Multi-GPU Load Balancing for In-Situ Simulation and Visualization

GPU hardware acceleration for industrial applications: using computation to push beyond physical limitations

Efficient Volume Rendering in CUDA Path Tracer

A GPU-based Multiresolution Pipeline for Compressed Volume Rendering

Fractal Based Method on Hardware Acceleration for Natural Environments

Evaluation and Improvement of GPU Ray Tracing with a Thread Migration Technique

Real-time rendering of large surface-scanned range data natively on a GPU

Ray-Tracing Based Interactive Camera Simulation

Real-Time Screen Space Rendering of Cartoon Water

Recent source codes

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

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

Most viewed papers (last 30 days)