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hgpu.org » Applications » Mathematics

Parallel Unsmoothed Aggregation Algebraic Multigrid Algorithms on GPUs

James Brannick, Yao Chen, Xiaozhe Hu, Ludmil Zikatanov

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Tags: CUDA, Mathematics, Numerical Analysis, nVidia, Tesla C2070

February 12, 2013 by hgpu

Real-Time Phase Masks for Interactive Stimulation of Optogenetic Neurons

Stephan C. Kramer, Johannes Hagemann, D. Russell Luke

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Tags: Biology, CUDA, Mathematics, Neurons and Cognition, nVidia, Optimization, Tesla C2070

February 5, 2013 by hgpu

Orthogonalization on a General Purpose Graphics Processing Unit with Double Double and Quad Double Arithmetic

Jan Verschelde, Genady Yoffe

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Tags: Algorithms, CUDA, Extended precision, Mathematics, nVidia, Tesla C2050

January 25, 2013 by hgpu

Regularization and nonlinearities for neural language models: when are they needed?

Marius Pachitariu, Maneesh Sahani

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Tags: CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 680, Statistics

January 25, 2013 by hgpu

A GPU-accelerated Direct-sum Boundary Integral Poisson-Boltzmann Solver

Weihua Geng, Ferosh Jacob

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Tags: Biomolecules, CUDA, Electrostatics, Linear Algebra, Mathematics, Molecular surface, Numerical Analysis, nVidia, Poisson Boltzmann, Tesla M2070

January 25, 2013 by hgpu

Rethinking resampling in the particle filter on graphics processing units

Lawrence M. Murray, Anthony Lee, Pierre E. Jacob

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Tags: CUDA, nVidia, Particle filtering, Statistics, Tesla S2050

January 18, 2013 by hgpu

Heat Load Modelling for District Heating Plants Using an OpenCL-based Algorithm

Erik Kral, Petr Capek, Lubomir Vasek

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Tags: Algorithms, Mathematics, nVidia, nVidia GeForce GT 310 M, OpenCL

January 8, 2013 by hgpu

Fast Poisson Solvers for Graphics Processing Units

Mirko Myllykoski, Tuomo Rossi, Jari Toivanen

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Tags: Algorithms, Mathematics, nVidia, nVidia GeForce GTX 580, OpenCL, Poisson equation

January 3, 2013 by hgpu

Fast Retinal Vessel Analysis

Michael Krause, Ralph Maria Alles, Bernhard Burgeth, Joachim Weickert

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Tags: Algorithms, CUDA, Image processing, Mathematics, Medicine, nVidia, nVidia GeForce GTX 680, Radon transform

December 29, 2012 by hgpu

Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes

Peter Richtarik, Martin Takac, Selin Damla Ahipasaoglu

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Tags: CUBLAS, CUDA, Machine learning, nVidia, Package, Statistics

December 18, 2012 by hgpu

CUDACLAW: a Data Parallel Solution Framework for Hyperbolic PDEs

Hrag Gorune Krikor Ohannessian

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Tags: Acoustics, CUDA, Differential equations, Finite volume method, Hyperbolic PDEs, Mathematics, nVidia, nVidia GeForce GTX 460, Partial differential equations, PDEs, Tesla C2050, Wave equation

December 12, 2012 by hgpu

Fully Parallel Particle Learning for GPGPUs and Other Parallel Devices

Kenichiro McAlinn, Hiroaki Katsura, Teruo Nakatsuma

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Tags: Algorithms, Bayesian, CUDA, nVidia, nVidia GeForce GTX 580, Particle filtering, Statistics

December 10, 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

Applications

Parallel Unsmoothed Aggregation Algebraic Multigrid Algorithms on GPUs

Real-Time Phase Masks for Interactive Stimulation of Optogenetic Neurons

Orthogonalization on a General Purpose Graphics Processing Unit with Double Double and Quad Double Arithmetic

Regularization and nonlinearities for neural language models: when are they needed?

A GPU-accelerated Direct-sum Boundary Integral Poisson-Boltzmann Solver

Rethinking resampling in the particle filter on graphics processing units

Heat Load Modelling for District Heating Plants Using an OpenCL-based Algorithm

Fast Poisson Solvers for Graphics Processing Units

Fast Retinal Vessel Analysis

Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes

CUDACLAW: a Data Parallel Solution Framework for Hyperbolic PDEs

Fully Parallel Particle Learning for GPGPUs and Other Parallel Devices

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