high performance computing on graphics processing units: hgpu.org

hgpu.org » Pseudo-random number generators

ShoveRand: a model-driven framework to easily generate random numbers on GP-GPU

Jonathan Passerat-Palmbach, Claude Mazel, David R.C. Hill

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Tags: Computer science, CUDA, nVidia, Pseudo-random number generators, Stochastic simulation, Tesla C1060

November 19, 2011 by hgpu

Parallel Random Numbers: As Easy as 1, 2, 3

John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw

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Tags: AES, ATI, ATI Radeon HD 6970, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Performance, Pseudo-random number generators, Security

October 28, 2011 by hgpu

A Hybrid Software Framework for the GPU Acceleration of Multi-Threaded Monte Carlo Applications

Joo Hong Lee, Mark T. Jones, Paul E. Plassmann

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Tags: ATI, ATI Radeon HD 5750, Computer science, Monte Carlo simulation, OpenCL, Pseudo-random number generators

October 24, 2011 by hgpu

An FPGA-specific algorithm for direct generation of multi-variate Gaussian random numbers

David B. Thomas, Wayne Luk

Tags: Algorithms, Computer science, CUDA, DSP, FPGA, Numerical simulation, nVidia, Pseudo-random number generators

August 30, 2011 by hgpu

A Variant of Mersenne Twister Suitable for Graphic Processors

Mutsuo Saito

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Tags: Algorithms, Computer science, CUDA, Mathematical Software, nVidia, nVidia GeForce GT 120, nVidia GeForce GTX 260, Package, Pseudo-random number generators

August 3, 2011 by hgpu

High-Performance Pseudo-Random Number Generation on Graphics Processing Units

Nimalan Nandapalan, Richard P. Brent, Lawrence M. Murray, Alistair Rendell

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Tags: Algorithms, Computer science, CUDA, Number Theory, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 480, Pseudo-random number generators

August 3, 2011 by hgpu

Pseudo-Random Number Generation on GP-GPU

Jonathan Passerat-Palmbach, Claude Mazel, David R. C. Hill

Tags: Algorithms, Computer science, Pseudo-random number generators

July 11, 2011 by hgpu

Pseudo-random number generation for Brownian Dynamics and Dissipative Particle Dynamics simulations on GPU devices

Carolyn L. Phillips, Joshua A. Anderson, Sharon C. Glotzer

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Tags: Algorithms, Computer science, Package, Physics, Pseudo-random number generators

July 6, 2011 by hgpu

Hybrid Embarrassingly Parallel on heterogeneous platform

Chunye Gong, Jie Liu, Jin Qin, Qingfeng Hu, Zhenghu Gong

Tags: Benchmarking, Computer science, Heterogeneous systems, Pseudo-random number generators

June 8, 2011 by hgpu

Fast, Processor-Cardinality Agnostic PRNG with a Tracking Application

Andrew Janowczyk, Sharat Chandran, Srinivas Aluru

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTS, Particle filtering, Pseudo-random number generators

June 7, 2011 by hgpu

Efficient Embarrassingly Parallel on Graphics Processor Unit

Chunye Gong, Jie Liu, Jin Qin, Qingfeng Hu, Zhenghu Gong

Tags: Benchmarking, Computer science, CUDA, nVidia, Performance, Pseudo-random number generators, Tesla T10

June 5, 2011 by hgpu

Impact of the Random Number generator quality on particle swarm optimization algorithm running on graphic processor units

C.J.A. Bastos-Filho, M.A.C. Oliveira, D.N.O. Nascimento, A.D. Ramos

Tags: Algorithms, Computer science, Optimization, Pseudo-random number generators

May 31, 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

See all packages

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high performance computing on graphics processing units: hgpu.org

ShoveRand: a model-driven framework to easily generate random numbers on GP-GPU

Parallel Random Numbers: As Easy as 1, 2, 3

A Hybrid Software Framework for the GPU Acceleration of Multi-Threaded Monte Carlo Applications

An FPGA-specific algorithm for direct generation of multi-variate Gaussian random numbers

A Variant of Mersenne Twister Suitable for Graphic Processors

High-Performance Pseudo-Random Number Generation on Graphics Processing Units

Pseudo-Random Number Generation on GP-GPU

Pseudo-random number generation for Brownian Dynamics and Dissipative Particle Dynamics simulations on GPU devices

Hybrid Embarrassingly Parallel on heterogeneous platform

Fast, Processor-Cardinality Agnostic PRNG with a Tracking Application

Efficient Embarrassingly Parallel on Graphics Processor Unit

Impact of the Random Number generator quality on particle swarm optimization algorithm running on graphic processor units

Recent source codes

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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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