12609
Rodica Ceterchi, Miguel Angel Martinez-del-Amor, Mario J. Perez-Jimenez
We introduce P systems with dynamic communication graphs which simulate the functioning of the CUDA architecture when solving the parallel reduction problem.
View View   Download Download (PDF)   
Ali Maroosi, Ravie Chandren Muniyandi, Elankovan A. Sundararajan, Abdullah Mohd Zin
Membrane computing is a theoretical model of computation that inspired from the structure and functioning of cells. Membrane computing models naturally have parallel structure. Most of the simulations of membrane computing have been done in a serial way on a machine with a central processing unit (CPU). This has neglected the advantage of parallelism in […]
View View   Download Download (PDF)   
Miguel A. Martinez-del-Amor, Ignacio Perez-Hurtado, Mario J. Perez-Jimenez, Jose M. Cecilia, Gines D. Guerrero, Jose M. Garcia
Software development for cellular computing is growing up yielding new applications. In this paper, we describe a simulator for the class of recognizer P systems with active membranes, which exploits the massively parallel nature of P systems computations by using GPUs (Graphics Processing Units). The newest generation of GPUs provide a massively parallel framework to […]
View View   Download Download (PDF)   
Miguel A. Martinez-del-Amor, Jesus Perez-Carrasco, Mario J. Perez-Jimenez
In order to provide efficient software tools to deal with large membrane systems, high-throughput simulators are required. Parallel computing platforms are good candidates, since they are capable of partially implementing the inherently parallel nature of the model. In this concern, today GPUs (Graphics Processing Unit) are considered as highly parallel processors, and they are being […]
Salah Zaher, Amr Badr, Ibrahim Farag, Tarek AbdElmaged
Simulators are limited by the available resources on the GPU as well as the CPU. Simulation of P systems with active membrane using GPUs is a new concept in the development of applications for membrane computing. P systems are an alternative approach to extract all performance available on GPUs due to its parallel nature. In […]
View View   Download Download (PDF)   
Francis George C. Cabarle, Henry N. Adorna, Miguel A. Martinez-Del-Amor, Mario J. Perez-Jimenez
In this work we present further extensions and improvements of a Spiking Neural P system (for short, SNP systems) simulator on graphics processing units (for short, GPUs). Using previous results on representing SNP system computations using linear algebra, we analyze and implement a computation simulation algorithm on the GPU. A two-level parallelism is introduced for […]
View View   Download Download (PDF)   
Richelle Ann B. Juayong, Francis George C. Cabarle, Henry N. Adorna, Miguel A. Martinez-del-Amor
In this report, we present our initial proposal on simulating computations on a restricted variant of Evolution-Communication P system with energy (ECPe system) which will then be implemented in Graphics Processing Units (GPUs). This ECPe systems variant prohibits the use of antiport rules for communication. Several possible levels of parallelizations for simulating ECPe systems computations […]
View View   Download Download (PDF)   
Francis Cabarle, Henry Adorna, Miguel A. Martinez-del-Amor
In this paper we present a Spiking Neural P system (SNP system) simulator based on graphics processing units (GPUs). In particular we implement the simulator using NVIDIA CUDA enabled GPUs. The massively parallel architecture of current GPUs is very suitable for the maximally parallel computations of SNP systems. We simulate a wider variety of SNP […]
Jose Cecilia, Jose Garcia, Gines Guerrero, Miguel Martinez-del-Amor, Mario Perez-Jimenez, Manuel Ujaldon
Membrane Computing is a discipline aiming to abstract formal computing models, called membrane systems or P systems, from the structure and functioning of the living cells as well as from the cooperation of cells in tissues, organs, and other higher order structures. This framework provides polynomial time solutions to NP-complete problems by trading space for […]
Francis Cabarle, Henry Adorna, Miguel A. Martinez-del-Amor
We present in this paper our work regarding simulating a type of P system known as a spiking neural P system (SNP system) using graphics processing units (GPUs). GPUs, because of their architectural optimization for parallel computations, are well-suited for highly parallelizable problems. Due to the advent of general purpose GPU computing in recent years, […]
View View   Download Download (PDF)   
Narayan Ganesan, Michela Taufer, Sandeep Patel
In this short paper we present a GPU code for MD simulations of large membrane regions in the NVT and NVE ensembles with explicit solvent. We give an overview of the code and present preliminary performance results.
View View   Download Download (PDF)   
Jose M. Cecilia, Jose M. Garcia, Gines D. Guerrero, Miguel A. Martinez-del-Amor, Ignacio Perez-Hurtado, Mario J. Perez-Jimenez
P systems are inherently parallel and non-deterministic theoretical computing devices defined inside the field of Membrane Computing. Many P system simulators have been presented in this area, but they are inefficient since they can not handle the parallelism of these devices. Nowadays, we are witnessing the consolidation of the GPUs as a parallel framework to […]
Page 1 of 212

* * *

* * *

Like us on Facebook

HGPU group

141 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1220 peoples are following HGPU @twitter

Featured events

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: