high performance computing on graphics processing units: hgpu.org

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The Reduction Problem in CUDA and Its Simulation with P Systems

Rodica Ceterchi, Miguel Angel Martinez-del-Amor, Mario J. Perez-Jimenez

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Tags: Computer science, CUDA, Membrane computing, nVidia

August 5, 2014 by hgpu

Improved Implementation of Simulation for Membrane Computing on the Graphic Processing Unit

Ali Maroosi, Ravie Chandren Muniyandi, Elankovan A. Sundararajan, Abdullah Mohd Zin

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Tags: Computer science, CUDA, Membrane computing, nVidia, nVidia GeForce GTX 480

February 5, 2014 by hgpu

Simulating Active Membrane Systems Using GPUs

Miguel A. Martinez-del-Amor, Ignacio Perez-Hurtado, Mario J. Perez-Jimenez, Jose M. Cecilia, Gines D. Guerrero, Jose M. Garcia

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Tags: Computer science, CUDA, Membrane computing, nVidia, Tesla C1060

October 13, 2013 by hgpu

Simulating a Family of Tissue P Systems Solving SAT on the GPU

Miguel A. Martinez-del-Amor, Jesus Perez-Carrasco, Mario J. Perez-Jimenez

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Source codes

Tags: Computer science, CUDA, Membrane computing, nVidia, Package, SAT, Tesla C1060

August 5, 2013 by hgpu

Using P System with GPU Model to Design and Implement a Public Key Cryptography

Salah Zaher, Amr Badr, Ibrahim Farag, Tarek AbdElmaged

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Tags: Algorithms, ATI, ATI Radeon HD 4650, Computer science, Membrane computing, P systems, RSA, Security

January 2, 2013 by hgpu

Improving GPU Simulations of Spiking Neural P Systems

Francis George C. Cabarle, Henry N. Adorna, Miguel A. Martinez-Del-Amor, Mario J. Perez-Jimenez

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Tags: Computer science, CUDA, Linear Algebra, Membrane computing, Neural networks, nVidia, P systems, Tesla C1060

June 25, 2012 by hgpu

On the Simulations of Evolution-Communication P Systems with Energy without Antiport Rules for GPUs

Richelle Ann B. Juayong, Francis George C. Cabarle, Henry N. Adorna, Miguel A. Martinez-del-Amor

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Tags: Computer science, CUDA, Membrane computing, nVidia, P systems

May 25, 2012 by hgpu

A Spiking Neural P system simulator based on CUDA

Francis Cabarle, Henry Adorna, Miguel A. Martinez-del-Amor

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Tags: Algorithms, Computer science, CUDA, Membrane computing, nVidia, nVidia GeForce 9400 M, Package

March 15, 2012 by hgpu

The GPU on the simulation of cellular computing models

Jose Cecilia, Jose Garcia, Gines Guerrero, Miguel Martinez-del-Amor, Mario Perez-Jimenez, Manuel Ujaldon

Tags: Computer science, Membrane computing, nVidia, P systems, SAT, Tesla S2050

May 10, 2011 by hgpu

Simulating Spiking Neural P systems without delays using GPUs

Francis Cabarle, Henry Adorna, Miguel A. Martinez-del-Amor

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Tags: Computer science, CUDA, Membrane computing, Neural and Evolutionary Computing, nVidia

April 12, 2011 by hgpu

Simulations of Large Membrane Regions using GPU-enabled Computations – Preliminary Results

Narayan Ganesan, Michela Taufer, Sandeep Patel

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Tags: Chemistry, CUDA, Membrane computing, Molecular dynamics, nVidia, Performance

February 17, 2011 by hgpu

Simulating a P system based efficient solution to SAT by using GPUs

Jose M. Cecilia, Jose M. Garcia, Gines D. Guerrero, Miguel A. Martinez-del-Amor, Ignacio Perez-Hurtado, Mario J. Perez-Jimenez

Tags: Computer science, Membrane computing, P systems, SAT

November 20, 2010 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

The Reduction Problem in CUDA and Its Simulation with P Systems

Improved Implementation of Simulation for Membrane Computing on the Graphic Processing Unit

Simulating Active Membrane Systems Using GPUs

Simulating a Family of Tissue P Systems Solving SAT on the GPU

Using P System with GPU Model to Design and Implement a Public Key Cryptography

Improving GPU Simulations of Spiking Neural P Systems

On the Simulations of Evolution-Communication P Systems with Energy without Antiport Rules for GPUs

A Spiking Neural P system simulator based on CUDA

The GPU on the simulation of cellular computing models

Simulating Spiking Neural P systems without delays using GPUs

Simulations of Large Membrane Regions using GPU-enabled Computations – Preliminary Results

Simulating a P system based efficient solution to SAT by using GPUs

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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