high performance computing on graphics processing units: hgpu.org

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GPU Computing with Orientation Maps for Extracting Local Invariant Features

Naoyuki Ichimura

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Tags: Computer science, Computer vision, CUDA, Image recognition, nVidia, nVidia GeForce GTX 280, nVidia Quadro FX 4600

March 12, 2011 by hgpu

GEMM on a GPU

Jonathan Monette

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Tags: BLAS, Computer science, CUDA, Linear Algebra, nVidia, nVidia Quadro FX 5800, Poster

March 12, 2011 by hgpu

To GPU Synchronize or Not GPU Synchronize?

Wu-Chun Feng, Shucai Xiao

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

March 12, 2011 by hgpu

GBOOST : A GPU-based tool for detecting gene-gene interactions in genome-wide case control studies

Ling Sing S. Yung, Can Yang, Xiang Wan, Weichuan Yu

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Tags: Bioinformatics, Biology, Genetics, nVidia, nVidia GeForce GTX 285, Package

March 12, 2011 by hgpu

Fast Optimal Mass Transport for Dynamic Active Contour Tracking on the GPU

Gallagher Pryor, Tauseef U. Rehman, Shawn Lankton, Patricio A. Vela, Allen Tannenbaum

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce 8800 GTX, Optical flow, Video tracking

March 12, 2011 by hgpu

Stereo depth with a Unified Architecture GPU

Joel Gibson, Oge Marques

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Tags: Computer science, Computer vision, CUDA, Image recognition, nVidia

March 11, 2011 by hgpu

GPU-Accelerated Text Mining

Yongpeng Zhang, Frank Mueller, Xiaohui Cui, Thomas Potok

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Text mining

March 11, 2011 by hgpu

The GPU as a high performance computational resource

Tor Dokken, Trond R Hagen, Jon M Hjelmervik

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Tags: Computer science, Linear Algebra, Mathematical Software, Partial differential equations, PDEs, Review

March 11, 2011 by hgpu

Interactive SPH Simulation and Rendering on the GPU

Prashant Goswami, Philipp Schlegel, Barbara Solenthaler, Renato Pajarola

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 280, Rendering

March 11, 2011 by hgpu

Using GPU shaders for visualization

Mike Bailey

Tags: Computer science, GLSL, OpenGL, Shaders, Visualization

March 11, 2011 by hgpu

Realtime Loop Subdivision on the GPU

Minho Kim, Jorg Peters

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Tags: 3D Graphics and Realism, ATI, ATI Radeon 9700 Pro, ATI Radeon X800, Computer science, nVidia, nVidia GeForce 6200, nVidia GeForce 6800 GT, Poster

March 11, 2011 by hgpu

Anisotropic Kuwahara Filtering on the GPU

Jan Eric Kyprianidis, Henry Kang, Jurgen Dollner

Tags: 3D Graphics and Realism, Algorithms, Computer science, Filtering, Rendering

March 11, 2011 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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

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

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high performance computing on graphics processing units: hgpu.org

Applications

GPU Computing with Orientation Maps for Extracting Local Invariant Features

GEMM on a GPU

To GPU Synchronize or Not GPU Synchronize?

GBOOST : A GPU-based tool for detecting gene-gene interactions in genome-wide case control studies

Fast Optimal Mass Transport for Dynamic Active Contour Tracking on the GPU

Stereo depth with a Unified Architecture GPU

GPU-Accelerated Text Mining

The GPU as a high performance computational resource

Interactive SPH Simulation and Rendering on the GPU

Using GPU shaders for visualization

Realtime Loop Subdivision on the GPU

Anisotropic Kuwahara Filtering on the GPU

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

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

Device Virtual Machine (DVM)

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

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

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

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

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

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