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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

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

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

Multi Agent Navigation on the GPU

Avi Bleiweiss

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Tags: Artificial intelligence, Computer science, CUDA, Games, Hashing, nVidia, nVidia GeForce GTX 280, Search strategies

March 11, 2011 by hgpu

Real-time human detection using histograms of oriented gradients on a GPU

Kirt Lillywhite, Dah-Jye Lee, Dong Zhang

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

March 11, 2011 by hgpu

Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform

Michal Czapinski, Stuart Barnes

Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia

March 10, 2011 by hgpu

Skeletal rigid skinning with blending patches on the GPU

Andreas Vasilakis, Ioannis Fudos

Tags: Computer science, GLSL, nVidia, nVidia GeForce 8800 Ultra, OpenGL, Real-time graphics

March 10, 2011 by hgpu

Implementing Ultrasound Beamforming on the GPU using CUDA

Lars Gronvold

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Tags: Computer science, CUDA, nVidia, Tesla C870, Thesis, Ultrasound

March 10, 2011 by hgpu

Realtime phase-based optical flow on the GPU

K Pauwels, M M Van Hulle

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

March 10, 2011 by hgpu

An Efficient SAR Processor Based on GPU via CUDA

Bin Liu, Kaizhi Wang, Xingzhao Liu, Wenxian Yu

Tags: CUDA, nVidia, Signal processing

March 10, 2011 by hgpu

Performance analysis and optimization strategies for a D3Q19 lattice Boltzmann kernel on nVIDIA GPUs using CUDA

J. Habich, T. Zeiser, G. Hager, G. Wellein

Tags: CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia

March 10, 2011 by hgpu

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

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

* * *

high performance computing on graphics processing units: hgpu.org

Stereo depth with a Unified Architecture GPU

GPU-Accelerated Text Mining

Interactive SPH Simulation and Rendering on the GPU

Realtime Loop Subdivision on the GPU

Multi Agent Navigation on the GPU

Real-time human detection using histograms of oriented gradients on a GPU

Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform

Skeletal rigid skinning with blending patches on the GPU

Implementing Ultrasound Beamforming on the GPU using CUDA

Realtime phase-based optical flow on the GPU

An Efficient SAR Processor Based on GPU via CUDA

Performance analysis and optimization strategies for a D3Q19 lattice Boltzmann kernel on nVIDIA GPUs using CUDA

Recent source codes

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

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

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