high performance computing on graphics processing units: hgpu.org

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Accelerating Stochastic Simulations on GPUs Using OpenCL

Pilsung Kang

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Tags: Heterogeneous systems, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Physics, Stochastic simulation

November 10, 2019 by hgpu

Reliable Initialization of GPU-enabled Parallel Stochastic Simulations Using Mersenne Twister for Graphics Processors

Jonathan Passerat-Palmbach, Claude Mazel, Antoine Mahul, David Hill

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

February 2, 2015 by hgpu

Harnessing Aspect Oriented Programming on GPU: Application to Warp-Level Parallelism (WLP)

Jonathan Passerat-Palmbach, Pierre Schweitzer, Jonathan Caux, Pridi Siregar, Claude Mazel, David Hill

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Tags: Computer science, CUDA, nVidia, Stochastic simulation, Tesla C2050, Warp level parallelism

January 19, 2015 by hgpu

How to Correctly Deal With Pseudorandom Numbers in Manycore Environments – Application to GPU programming with Shoverand

Jonathan Passerat-Palmbach (ISIMA, UBP, LIMOS), David Hill (LIMOS, UBP, ISIMA)

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Source codes

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

December 30, 2014 by hgpu

GPU accelerated Monte Carlo simulation of Brownian motors dynamics with CUDA

J. Spiechowicz, M. Kostur, L. Machura

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Tags: Computational Physics, CUDA, Differential equations, Monte Carlo integration, Monte Carlo simulation, nVidia, nVidia GeForce GTX 680, Physics, Stochastic simulation

September 19, 2014 by hgpu

An efficient GPU acceptance-rejection algorithm for the selection of the next reaction to occur for Stochastic Simulation Algorithms

Neri Mickael, Denis Mestivier

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Tags: Algorithms, Biology, Computer science, CUDA, nVidia, Optimization, Stochastic simulation, Tesla K10, Tesla K20, Tesla M2075, Tesla M2090

April 4, 2014 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

Approximate dynamic programming with post-decision states as a solution method for dynamic economic models

Isaiah Hull

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Tags: Algorithms, CUDA, Finance, nVidia, nVidia GeForce GTX 560 Ti, Stochastic simulation

September 30, 2013 by hgpu

Achieving Speedup in Aggregate Risk Analysis using Multiple GPUs

A. K. Bahl, O. Baltzer, A. Rau-Chaplin, B. Varghese, A. Whiteway

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Tags: Algorithms, Computer science, CUDA, Finance, nVidia, Risk Management, Stochastic simulation, Tesla C2075, Tesla M2090

August 13, 2013 by hgpu

Parallel Simulations for Analysing Portfolios of Catastrophic Event Risk

Aman Bahl, Oliver Baltzer, Andrew Rau-Chaplin, Blesson Varghese

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Tags: Algorithms, Computational Engineering, Computer science, CUDA, Finance, Monte Carlo simulation, nVidia, Stochastic simulation, Tesla C2075

August 12, 2013 by hgpu

Multi-GPU Computing for Achieving Speedup in Real-time Aggregate Risk Analysis

A. K. Bahl, O. Baltzer, A. Rau-Chaplin, B. Varghese, A. Whiteway

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Tags: Algorithms, CUDA, Finance, nVidia, Stochastic simulation, Tesla C2075, Tesla M2090

February 23, 2013 by hgpu

Massively Parallel Computing in Economics

Eric M. Aldrich

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Tags: CUDA, Finance, nVidia, Package, Review, Stochastic simulation, Tesla C2075

February 15, 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

Accelerating Stochastic Simulations on GPUs Using OpenCL

Reliable Initialization of GPU-enabled Parallel Stochastic Simulations Using Mersenne Twister for Graphics Processors

Harnessing Aspect Oriented Programming on GPU: Application to Warp-Level Parallelism (WLP)

How to Correctly Deal With Pseudorandom Numbers in Manycore Environments – Application to GPU programming with Shoverand

GPU accelerated Monte Carlo simulation of Brownian motors dynamics with CUDA

An efficient GPU acceptance-rejection algorithm for the selection of the next reaction to occur for Stochastic Simulation Algorithms

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

Approximate dynamic programming with post-decision states as a solution method for dynamic economic models

Achieving Speedup in Aggregate Risk Analysis using Multiple GPUs

Parallel Simulations for Analysing Portfolios of Catastrophic Event Risk

Multi-GPU Computing for Achieving Speedup in Real-time Aggregate Risk Analysis

Massively Parallel Computing in Economics

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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