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

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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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

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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