high performance computing on graphics processing units: hgpu.org

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Fast computation of MadGraph amplitudes on graphics processing unit (GPU)

K. Hagiwara, J. Kanzaki, Q. Li, N. Okamura, T. Stelzer

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Tags: Computational Physics, CUDA, Fortran, Monte Carlo integration, nVidia, Physics, Pseudo-random number generators, QCD, Tesla C2075

May 6, 2013 by hgpu

GPU-Optimized Molecular Dynamics Simulations

Tyson J. Lipscomb

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Tags: Biology, Biomolecules, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Optimization, Pseudo-random number generators, Review, RNA, Tesla C2070, Thesis

July 8, 2012 by hgpu

Parallel random variates generator for GPUs based on normal numbers

Gleb Beliakov, Michael Johnstone, Doug Creighton, Tim Wilkin

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Tags: Algorithms, Computer science, CUDA, Mathematical Software, Numerical Analysis, nVidia, Probability, Pseudo-random number generators, Tesla C2050

June 8, 2012 by hgpu

Random number generators for massively parallel simulations on GPU

Markus Manssen, Martin Weigel, Alexander K. Hartmann

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Tags: Algorithms, Computational Physics, CUDA, High Energy Physics - Lattice, Molecular dynamics, Monte Carlo simulation, nVidia, nVidia GeForce GTX 480, Physics, Pseudo-random number generators, Review, Statistical Mechanics

May 1, 2012 by hgpu

An On-Demand Fast Parallel Pseudo Random Number Generator with Applications

Dip Sankar Banerjee, Aman Kumar Bahl, Kishore Kothapalli

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Tags: Algorithms, Computer science, CUDA, List ranking, Monte Carlo simulation, nVidia, Pseudo-random number generators, Tesla C1060

April 21, 2012 by hgpu

A High-Performance Brownian Bridge for GPUs: Lessons for Bandwidth Bound Applications

Jacques Du Toit

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Tags: Computer science, CUDA, nVidia, Package, Pseudo-random number generators, Tesla C2050

April 4, 2012 by hgpu

Variants of Mersenne Twister Suitable for Graphic Processors

Mutsuo Saito, Makoto Matsumoto

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

February 21, 2012 by hgpu

Stochastic Differential Equations simulation using GPU

Narayana Darapaneni, Prakalp Somawanshi, Meghana Joshi

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Tags: Algorithms, Computer science, CUDA, Differential equations, Finance, Monte Carlo simulation, nVidia, nVidia GeForce GTX 275, Pseudo-random number generators, Stochastic simulation

February 7, 2012 by hgpu

A GPU implementation of EGSnrc’s Monte Carlo photon transport for imaging applications

Jonas Lippuner, Idris A Elbakri

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Tags: CUDA, Medical Physics, Monte Carlo simulation, nVidia, nVidia GeForce GTX 470, Physics, Pseudo-random number generators

December 27, 2011 by hgpu

Efficient and Cryptographically Secure Generation of Chaotic Pseudorandom Numbers on GPU

Jacques M. Bahi, Raphael Couturier, Christophe Guyeux, Pierre-Cyrille Heam

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Pseudo-random number generators, Security, Tesla C1060

December 23, 2011 by hgpu

Performance and Quality of Random Number Generators

V. du Preez, M.G.B. Johnson, A. Leist, K.A. Hawick

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Tags: Algorithms, Computer science, CUDA, Monte Carlo simulation, Numerical simulation, nVidia, nVidia GeForce GTX 580, Pseudo-random number generators, Review

December 20, 2011 by hgpu

Parallel Pseudo-Random Number Generation

Nimalan Nandapalan, Richard Brent

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Tags: Algorithms, Computer science, CUDA, nVidia, OpenCL, Pseudo-random number generators, Review

November 28, 2011 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

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

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

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Fast computation of MadGraph amplitudes on graphics processing unit (GPU)

GPU-Optimized Molecular Dynamics Simulations

Parallel random variates generator for GPUs based on normal numbers

Random number generators for massively parallel simulations on GPU

A High-Performance Brownian Bridge for GPUs: Lessons for Bandwidth Bound Applications

Variants of Mersenne Twister Suitable for Graphic Processors

Stochastic Differential Equations simulation using GPU

A GPU implementation of EGSnrc’s Monte Carlo photon transport for imaging applications

Efficient and Cryptographically Secure Generation of Chaotic Pseudorandom Numbers on GPU

Performance and Quality of Random Number Generators

Parallel Pseudo-Random Number Generation

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

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

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