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hgpu.org » Pseudo-random number generators

Comparison of Random Number Generators in Particle Swarm Optimization Algorithm

Ke Ding, Ying Tan

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 560 Ti, Particle swarm optimization, Pseudo-random number generators

June 22, 2014 by hgpu

Efficient pseudo-random number generation for monte-carlo simulations using graphic processors

Siddhant Mohanty, A K Mohanty, F Carminati

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Tags: Algorithms, Computer science, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 480, OpenCL, Pseudo-random number generators

February 17, 2014 by hgpu

Increasing precision of uniform pseudorandom number generators

Vadim Demchik, Alexey Gulov

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Tags: Algorithms, Computer science, Extended precision, Mathematical Software, Pseudo-random number generators, Security

February 12, 2014 by hgpu

Pseudo Random Number Generators on Graphics Processing Units, with Applications in Finance

Matthew Anker

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Tags: Algorithms, Computer science, CUDA, Finance, nVidia, Pseudo-random number generators, Tesla C2050, Thesis

November 25, 2013 by hgpu

A High Performance Random Number Generator Using Heterogeneous Computing Platform

Fan Li, Minglu Jin

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Pseudo-random number generators, Tesla C2070

November 11, 2013 by hgpu

QCDGPU: open-source package for Monte Carlo lattice simulations on OpenCL-compatible multi-GPU systems

Vadim Demchik, Natalia Kolomoyets

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 6970, ATI Radeon HD 7970, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce GTX 560 M, nVidia GeForce GTX 560 Ti, OpenCL, Package, Phase transition, Physics, Pseudo-random number generators, QCD

October 21, 2013 by hgpu

Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations

Jonathan Passerat-Palmbach

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Tags: Algorithms, Computer science, CUDA, Java, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 590, OpenCL, Pseudo-random number generators, Software Engineering, Stochastic simulation, Tesla C2050, Tesla C2075, Tesla K20, Thesis

October 13, 2013 by hgpu

Template Library for Multi-GPU Pseudorandom Number Recursion-based Generators

Dominik Szalkowski, Przemyslaw Stpiczynski

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Tags: Algorithms, Computer science, CUDA, nVidia, Package, Pseudo-random number generators, Tesla M2050

October 1, 2013 by hgpu

PRAND: GPU accelerated parallel random number generation library: Using most reliable algorithms and applying parallelism of modern GPUs and CPUs

L.Yu. Barash, L.N. Shchur

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Tags: Algorithms, Computational Physics, Computer science, CUDA, Mathematical Software, nVidia, Package, Pseudo-random number generators, Tesla C2050

July 25, 2013 by hgpu

MSTg: Cryptographically strong pseudorandom number generator and its realization

Pavol Svaba, Pascal Marquardt, Tran van Trung

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Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX 550 Ti, Pseudo-random number generators, Security

July 2, 2013 by hgpu

GPU Implementation of the Particle Filter

Joakim Gebart

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Tags: Algorithms, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX 570, Particle filtering, Pseudo-random number generators, Thesis

June 24, 2013 by hgpu

Blum Blum Shub on the GPU

Mikael Olsson, Niklas Gullberg

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 580, OpenCL, Pseudo-random number generators, Security, Thesis

May 11, 2013 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

Comparison of Random Number Generators in Particle Swarm Optimization Algorithm

Efficient pseudo-random number generation for monte-carlo simulations using graphic processors

Increasing precision of uniform pseudorandom number generators

Pseudo Random Number Generators on Graphics Processing Units, with Applications in Finance

A High Performance Random Number Generator Using Heterogeneous Computing Platform

QCDGPU: open-source package for Monte Carlo lattice simulations on OpenCL-compatible multi-GPU systems

Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations

Template Library for Multi-GPU Pseudorandom Number Recursion-based Generators

PRAND: GPU accelerated parallel random number generation library: Using most reliable algorithms and applying parallelism of modern GPUs and CPUs

MSTg: Cryptographically strong pseudorandom number generator and its realization

GPU Implementation of the Particle Filter

Blum Blum Shub on the GPU

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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