By Topic

Hybrid CPU-GPU Distributed Framework for Large Scale Mobile Networks Simulation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ben Romdhanne Bilel ; Eurecom, Sophia Antipolis, France ; Nikaein Navid ; Mohamed Said Mosli Bouksiaa

Most of the existing packet-level simulation tools are designed to perform experiments modeling a small to medium scale networks. The main reason of this limitation is the amount of available computation power and memory in quasi mono-process simulation environment. To enable efficient packet-level simulation for large scale scenario, we introduce a new CPU-GPU co-simulation framework where synchronization and experiment design are performed on CPU and node's processes are executed in parallel on GPU according to the master/worker model [13]. The framework is developed using Compute-Unified Device Architecture (CUDA) and denoted as Cunetsim, CUDA network simulator. To study the performance gain when GPU is used, we also introduce the CPU-legacy version of Cunetsim optimized for multi-core architecture. In this work, we present Cunetsim architecture, design concept, and features. We evaluate the performance of Cunetsim (both versions) compared to Sinalgo and NS-3 using benchmark scenarios. Evaluation results show that Cunetsim execution time remains stable and that it achieves significantly lower computation time than CPU-based simulators for both static and mobile networks with no degradation in the accuracy of the results. We also study the impact of the hardware configuration on the performance gain and the simulation correctness. Cunetsim presents a proof of concept, demonstrating the feasibility of a fully GPU-based simulation rather than GPUoffloading or partial acceleration, through adequate architecture.

Published in:

Distributed Simulation and Real Time Applications (DS-RT), 2012 IEEE/ACM 16th International Symposium on

Date of Conference:

25-27 Oct. 2012