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Parallel particle swarm optimization using GPGPU

Iliana Castro Liera, Marco Antonio Castro Liera, Jesus Antonio Castro

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Tags: Algorithms, Computer science, CUDA, Evolutionary Computations, nVidia, nVidia GeForce GTX 460, Optimization

January 26, 2012 by hgpu

Defocus Magnification with CUDA

Chih-Wei Chen, Chun-Ta Lin, Yu-Lin Sung, Chiou-Shann Fuh

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Tags: Algorithms, CUDA, Image processing, nVidia

January 26, 2012 by hgpu

GPU Nonlinear Fixed Points, with an application to GPU IFS Rendering

Orion Sky Lawlor

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Tags: Algorithms, Computer science, GLSL, nVidia, nVidia GeForce GTX 280, OpenGL, Package, Rendering

January 26, 2012 by hgpu

Accounting for Uncertainty in Medical Data: A CUDA Implementation of Normalized Convolution

Stefan Lindholm, Joel Kronander

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Tags: CUDA, Image generation, Image processing, Medicine, nVidia, nVidia GeForce GTX 560 Ti, Signal processing

January 26, 2012 by hgpu

CUDA raytracing algorithm for visualizing discrete element model output

Anders Damsgaard Christensen

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia Quadro FX 4000, Package, Raytracing

January 26, 2012 by hgpu

CUDA Fortran for Scientists and Engineers

Greg Ruetsch, Massimiliano Fatica

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Tags: Book, Computer science, CUDA, Fortran, High-level Languages, nVidia, Optimization, Tutorial

January 26, 2012 by hgpu

A Parallel Ant Colony Optimization Algorithm for the Travelling Salesman Problem: Improving Performance Using CUDA

Octavian Nitica

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Tags: Algorithms, Computer science, CUDA, Metaheuristics, nVidia, Optimization, Tesla C1060, Tesla C2050, Tesla C870, Thesis

January 26, 2012 by hgpu

Using a GPU, Online Diarization – Offline Diarization

Gerald Friedland

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Tags: Acoustics, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Speech recognition

January 26, 2012 by hgpu

Parallel Symbolic Analysis of Large Analog Circuits on GPU Platforms

Sheldon X.-D. Tan, Xue-Xin Liu, Eric Mlinar and Esteban Tlelo-Cuautle

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Tags: Computer science, CUDA, Electronic design automation, nVidia, Tesla S1070

January 26, 2012 by hgpu

Visual Data Mining Using the Point Distribution Tensor

Marcel Ritter, Werner Benger, Biagio Cosenza, Keera Pullman, Hans Moritsch, Wolfgang Leimer

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Tags: Algorithms, Data mining, Geoscience, Image processing, nVidia, nVidia Quadro FX 3800 M, OpenCL, Visualization

January 26, 2012 by hgpu

Scalable Parallel Minimum Spanning Forest Computation

Sadegh Nobari, Thanh-Tung Cao, Panagiotis Karras, Stephane Bressan

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Performance, Tesla S1070

January 25, 2012 by hgpu

Parallel LDPC Decoder Implementation on GPU Based on Unbalanced Memory Coalescing

Soonyoung Kang, Jaekyun Moon

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Parity-check codes

January 25, 2012 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

Programming

Parallel particle swarm optimization using GPGPU

Defocus Magnification with CUDA

GPU Nonlinear Fixed Points, with an application to GPU IFS Rendering

Accounting for Uncertainty in Medical Data: A CUDA Implementation of Normalized Convolution

CUDA raytracing algorithm for visualizing discrete element model output

CUDA Fortran for Scientists and Engineers

A Parallel Ant Colony Optimization Algorithm for the Travelling Salesman Problem: Improving Performance Using CUDA

Using a GPU, Online Diarization – Offline Diarization

Parallel Symbolic Analysis of Large Analog Circuits on GPU Platforms

Visual Data Mining Using the Point Distribution Tensor

Scalable Parallel Minimum Spanning Forest Computation

Parallel LDPC Decoder Implementation on GPU Based on Unbalanced Memory Coalescing

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Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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