Page 38 - Data Science Algorithms in a Week

In: Artificial Intelligence ISBN: 978-1-53612-677-8
Editors: L. Rabelo, S. Bhide and E. Gutierrez © 2018 Nova Science Publishers, Inc.

Chapter 2

USING DEEP LEARNING TO CONFIGURE PARALLEL

DISTRIBUTED DISCRETE-EVENT SIMULATORS

2,
1
3
Edwin Cortes , Luis Rabelo and Gene Lee
*
1 Institute of Simulation and Training, Orlando, Florida, US
2 Department of Industrial Engineering and Management Systems,
University of Central Florida, Orlando, Florida, US
3 Department of Industrial Engineering and Management Systems,
University of Central Florida, Orlando, Florida, US

ABSTRACT

This research discusses the utilization of deep learning for selecting the time
synchronization scheme that optimizes the performance of a particular parallel discrete
simulation hardware/software arrangement. The deep belief neural networks are able to
use measures of software complexity and architectural features to recognize, match
patterns and therefore to predict performance. Software complexities such as simulation
objects, branching, function calls, concurrency, iterations, mathematical computations,
and messaging frequency were given a weight based on the cognitive weighted approach.
In addition, simulation objects and hardware/network features such as the distributed
pattern of simulation objects, CPUs features (e.g., multithreading/multicore), and the
degree of loosely vs tightly coupled of the utilized computer architecture were also
captured to define the parallel distributed simulation arrangement. Deep belief neural
networks (in particular the restricted Boltzmann Machines (RBMs) were then used to
perform deep learning from the complexity parameters and their corresponding time
synchronization scheme value as measured by speedup performance. The simulation

* Corresponding Author Email: luis.rabelo@ucf.edu