high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Mathematics » Statistics

Massively parallel approximate Gaussian process regression

Robert B. Gramacy, Jarad Niemi, Robin Weiss

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Tags: Cluster computing, CUDA, nVidia, Statistics, Tesla M2090

October 22, 2013 by hgpu

vSMC: Parallel Sequential Monte Carlo in C++

Yan Zhou

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Tags: Algorithms, Bayesian, Mathematical Software, Mathematics, nVidia, nVidia Quadro FX 2000, OpenCL, Package, Physics, Signal processing, Statistics

July 1, 2013 by hgpu

Bayesian State-Space Modelling on High-Performance Hardware Using LibBi

Lawrence M. Murray

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Tags: Bayesian, CUDA, Mathematics, nVidia, Package, Statistics, Tesla C2075, Tesla S2050

June 17, 2013 by hgpu

Using GPU Simulation to Accurately Fit to the Power-Law Distribution

Efstratios Rappos, Stephan Robert

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Tags: Computational Physics, CUDA, Mathematics, nVidia, nVidia GeForce GTX 590, Statistics

May 30, 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

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

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

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

Massive parallelization of serial inference algorithms for a complex generalized linear model

Marc A. Suchard, Shawn E. Simpson, Ivan Zorych, Patrick Ryan, David Madigan

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Tags: Algorithms, CUDA, Databases, Mathematics, nVidia, Statistics, Tesla C2050

August 9, 2012 by hgpu

GPU acceleration of the particle filter: the Metropolis resampler

Lawrence Murray

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Tags: CUDA, Mathematics, nVidia, Statistics, Tesla S2050

March 1, 2012 by hgpu

gpustats: GPU Library for Statistical Computing in Python

Andrew Cron, Wes McKinney

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Tags: Algorithms, Bayesian, Code generation, CUDA, Mathematics, nVidia, nVidia GeForce GTS 250, OpenCL, Package, Probability, Python, Statistics

October 13, 2011 by hgpu

Optimizing OpenCL Kernels for Iterative Statistical Applications on GPUs

Thilina Gunarathne, Bimalee Salpitikorala, Arun Chauhan, Geoffrey Fox

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Tags: Algorithms, Code generation, Computer science, nVidia, OpenCL, Optimization, Statistics, Tesla C1060

September 25, 2011 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Applications

Massively parallel approximate Gaussian process regression

vSMC: Parallel Sequential Monte Carlo in C++

Bayesian State-Space Modelling on High-Performance Hardware Using LibBi

Using GPU Simulation to Accurately Fit to the Power-Law Distribution

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

Rethinking resampling in the particle filter on graphics processing units

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

Fully Parallel Particle Learning for GPGPUs and Other Parallel Devices

Massive parallelization of serial inference algorithms for a complex generalized linear model

GPU acceleration of the particle filter: the Metropolis resampler

gpustats: GPU Library for Statistical Computing in Python

Optimizing OpenCL Kernels for Iterative Statistical Applications on GPUs

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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