high performance computing on graphics processing units: hgpu.org

hgpu.org » Machine learning

Articulated object tracking by rendering consistent appearance parts

Zachary Pezzementi, Sandrine Voros, Gregory D. Hager

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Tags: Computer science, Filtering, GLSL, Machine learning, nVidia, nVidia GeForce 8600 GT, nVidia GeForce 8800 GTX, OpenGL, Rendering

July 30, 2011 by hgpu

Fast cell detection in high-throughput imagery using GPU-accelerated machine learning

David Mayerich, Jaerock Kwon, Aaron Panchal, John Keyser, Yoonsuck Choe

Tags: Biology, Image processing, Machine learning, nVidia

June 24, 2011 by hgpu

GPUMLib: A new Library to combine Machine Learning algorithms with Graphics Processing Units

N. Lopes, B. Ribeiro, R. Quintas

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Tags: Computer science, CUDA, Machine learning, nVidia, Package

June 1, 2011 by hgpu

Performance comparison of GPU and FPGA architectures for the SVM training problem

Markos Papadonikolakis, Christos-Savvas Bouganis, George Constantinides

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Tags: Computer science, CUDA, FPGA, Machine learning, nVidia, nVidia GeForce 8500 GT, Performance

May 29, 2011 by hgpu

Bio-inspired computer visual system using GPU and Visual Pattern Assessment Language (ViPAL): Application on breast cancer prognosis

Chao-Hui Huang, Daniel Racoceanu, Ludovic Roux, Thomas C. Putti

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Tags: Computer vision, CUDA, Machine learning, Medicine, nVidia, nVidia GeForce 9400 M

May 20, 2011 by hgpu

OpenCL based machine learning labeling of biomedical datasets

Oscar Amoros, Sergio Escalera, Anna Puig

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Tags: GLSL, Image processing, Machine learning, Medicine, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 470, OpenCL, OpenGL

May 12, 2011 by hgpu

Support Vector Machines on GPU with Sparse Matrix Format

Tsung-Kai Lin, Shao-Yi Chien

Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX 470

April 8, 2011 by hgpu

Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning

A El Zein, E McCreath, Alistair Rendell, Alex Smola

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce 8800 GTX, Presentation, Sparse matrix

March 20, 2011 by hgpu

Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs

Mark Van Heeswijk, Yoan Miche, Erkki Oja, Amaury Lendasse

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Tags: Computer science, CUDA, CULA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 295, Tesla C1060

March 20, 2011 by hgpu

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

PortLBM: A Portable Lattice Boltzmann Tool Leveraging SYCL on AMD, NVIDIA, and Intel GPUs

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

NVIDIA Object Oriented Agents

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

Probe-and-Refine Tuning of Repository Guidance for Coding Agents

CUDAnalyst (CUDA + Analyst)

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

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high performance computing on graphics processing units: hgpu.org

Articulated object tracking by rendering consistent appearance parts

Fast cell detection in high-throughput imagery using GPU-accelerated machine learning

GPUMLib: A new Library to combine Machine Learning algorithms with Graphics Processing Units

Performance comparison of GPU and FPGA architectures for the SVM training problem

Bio-inspired computer visual system using GPU and Visual Pattern Assessment Language (ViPAL): Application on breast cancer prognosis

OpenCL based machine learning labeling of biomedical datasets

Support Vector Machines on GPU with Sparse Matrix Format

Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning

Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs

Recent source codes

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

NVIDIA Object Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

CUDAnalyst (CUDA + Analyst)

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