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GPU-accelerated Faster Mean Shift with euclidean distance metrics

Le You, Han Jiang, Jinyong Hu, Chorng Chang, Lingxi Chen, Xintong Cui, Mengyang Zhao

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Tags: Algorithms, Clustering, CUDA, Image processing, nVidia, nVidia GeForce RTX 3070, nVidia GeForce RTX 3080 Ti, nVidia Quadro P 5000, Package, Pattern recognition, Tesla P100

January 2, 2022 by hgpu

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A tool set for random number generation on GPUs in R

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Building a Performance Model for Deep Learning Recommendation Model Training on GPUs

OpenMM: A High Performance Molecular Dynamics Library

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Fancier High-Performance API

Fancier: A Unified Framework for Java, C, and OpenCL Integration

A Compiler Framework for Optimizing Dynamic Parallelism on GPUs

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GPOEO: a micro-intrusive GPU online energy optimization framework for iterative applications

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CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs

From Loop Fusion to Kernel Fusion: A Domain-specific Approach to Locality Optimization

System-Level Optimization and Code Generation for Graphics Processors using a Domain-Specific Language

Faster-Mean-Shift-Euc

GPU-accelerated Faster Mean Shift with euclidean distance metrics

Turbo: A constraint solver purely on GPUs (CUDA)

A Variant of Concurrent Constraint Programming on GPU

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