By Topic

Predicting the performance of synchronous discrete event 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
$31 $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

2 Author(s)
Jinsheng Xu ; Dept. of Comput. Sci., North Carolina A&T State Univ., Greensboro, NC, USA ; Moon Jung Chung

We develop a model to predict the performance of synchronous discrete event simulation. Our model considers the two most important factors for the performance of synchronous simulation: load balancing and communication. The effect of load balancing in a synchronous simulation is computed using probability distribution models. We derive a formula that computes the cost of synchronous simulation by combining a communication model called LogGP and computation granularity. Even though the formula is simple, it is effective in capturing the most important factors for the synchronous simulation. The formula helps us to predict the maximum speed up achievable by synchronous simulation. In order to examine the prediction model, we have simulated several large ISCAS logic circuits and a simple PCS network simulation on an SGI Origin 2000 and Terascale Computing System (TCS) at the Pittsburgh Supercomputing Center. The results of the experiment show that our performance model accurately predicts the performance of synchronous simulation. The performance model developed is used to analyze the effect of several factors that may improve the performance of synchronous simulation. The factors include problem size, load balancing, granularity, communication overhead, and partitioning.

Published in:

Parallel and Distributed Systems, IEEE Transactions on  (Volume:15 ,  Issue: 12 )