Orientador: Prof. Raphael Yokoingawa de Camargo / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Neurociência e Cognição, 2014. / Nowadays, utilizing co-processors, accelerators and specially GPGPU computation are widely accepted as a new paradigm of High Performance Computing (HPC). However, developing softwares that can utilize available resources still remains a challenging task. In other side, scientist have used legacy CPU-based simulators for decades and many of them are still the main tools in different fields of science. In fact, any activity that can combine the legacy simulators with powerful co-processors devices is in the main interest. In this project, we design and develop a simulation engine, Parallel Neural Network Simulator (PN2S), to communicate with MOOSE simulator (A well-known tools by Neuroscientists) and provide CUDA based execution for simulating realistic neural network models. The simulation engine maps the voltage distribution in neuron¿s body to sets of linear systems and solve them on GPU. To provide usable functionality, we also developed solver for active channels which support Hodgkin-Huxley model of ionic channels. We compared the engine with CPU version for both homogeneous simple models and randomly generated heterogeneous network. The evaluation focused on performance and also covered the accuracy of the simulation. The experimental results, showed that by facilitating PN2S engine, we can significantly increase the performance of a simulation engine, since its execution is quite transparent to the users and major parts of the host simulator.