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Optimal structure of face detection algorithm using GPU architecture

Dmitry Pertsau

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 460

October 16, 2012 by hgpu

High-Performance Computing Algorithms for Constructing Inverted Files on Emerging Multicore Processors

Zheng Wei

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Information Retrieval, List ranking, nVidia, nVidia GeForce GTX 480, Tesla C1060, Tesla C2050, Thesis

October 16, 2012 by hgpu

Particle Filters on Multi-Core Processors

Mehdi Chitchian, Alexander S. van Amesfoort, Andrea Simonetto, Tamas Keviczky, Henk J. Sips

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Tags: ATI, ATI Radeon HD 6970, Bayesian, Computer science, Computer vision, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenCL, Particle filtering

October 16, 2012 by hgpu

CUDA Based Multi Objective Parallel Genetic Algorithms: Adapting Evolutionary Algorithms for Document Searches

Jason P. Duran, Sathish AP Kumar

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Text mining

October 15, 2012 by hgpu

Geometry Based Visualization with OpenCL

Joao Pedro Martins Rogeiro

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, nVidia Quadro FX 3800, OpenCL, Tesla C1060, Tesla C2050, Thesis, Visualization

October 15, 2012 by hgpu

Parallel Programming using OpenCL on Modern Architectures

Allan Svejstrup Nielsen, Allan Peter Engsig-Karup, Bernd Dammann

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Tags: ATI, ATI Radeon HD 6990, Computer science, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 590, OpenCL, Overview, Tutorial

October 15, 2012 by hgpu

Exploiting Data Parallelism in GPUs

Yongpeng Zhang

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Tags: Clustering, Computer science, CUDA, Data parallelism, GPU cluster, MPI, nVidia, nVidia GeForce GTX 280, Thesis

October 14, 2012 by hgpu

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

Ion Lungu, Dana-Mihaela Petrosanu, Alexandru Pirjan

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Optimization, Performance

October 14, 2012 by hgpu

Mean shift for graph bundling

Ozan Ersoy, Christophe Hurter, Alexandru Telea

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Tags: Computer science, Graph theory, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 580, OpenGL

October 13, 2012 by hgpu

An implicit Tensor-Mass solver on the GPU for soft bodies simulation

X. Faure, F. Zara, F. Jaillet, J-M. Moreau

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Tags: 3D Graphics and Realism, Computer science, FEM, Finite element method, nVidia, nVidia GeForce GTX 560, OpenCL, Virtual reality

October 13, 2012 by hgpu

AeminiumGPU: An Intelligent Framework for GPU Programming

Alcides Fonseca, Bruno Cabral

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Source codes

Tags: Computer science, Heterogeneous systems, Java, MapReduce, nVidia, nVidia GeForce GTX 285, OpenCL, Package

October 13, 2012 by hgpu

Parallel H-Tree Based Data Cubing on Graphics Processors

Baoyuan Wang, Yizhou Yu

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, nVidia GeForce 8800 GTX

October 13, 2012 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

Optimal structure of face detection algorithm using GPU architecture

Particle Filters on Multi-Core Processors

CUDA Based Multi Objective Parallel Genetic Algorithms: Adapting Evolutionary Algorithms for Document Searches

Geometry Based Visualization with OpenCL

Parallel Programming using OpenCL on Modern Architectures

Exploiting Data Parallelism in GPUs

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

Mean shift for graph bundling

An implicit Tensor-Mass solver on the GPU for soft bodies simulation

AeminiumGPU: An Intelligent Framework for GPU Programming

Parallel H-Tree Based Data Cubing on Graphics Processors

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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