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Using Deep Learning to Configure Parallel Distributed Discrete-Event Simulators 39

Input and Output Vectors for a Sample

This research implements cognitive weights to measure the complexity of a parallel
discrete event simulation with respect of implemented algorithms. Because each
simulation object in a simulation implements discrete events defined as code functions,
the complexity of each object is also computed by applying equation 11 for all
event/methods mapped to each simulation object. As a result, several parameters that
gage simulation complexity are then used as inputs to the deep belief neural network for
deep learning. These are: Total Simulation program cognitive weights, maximum
cognitive weights of all simulation objects, minimum cognitive weights of all simulation
objects, mean cognitive weights of all objects.
In addition, we have captured other parameters that define the hardware, flow
processing, potential messaging and other important characteristics that define a parallel
distributed discrete-event simulator implementation. The different components are
defined as follows:

1. Total Simulation Program Cognitive Weights: It is the total number of
cognitive weights of the simulation program.
2. Number of Simulation objects: It is the total number of simulation objects
in the simulation.
3. Types of Simulation objects: It is the number of classes of Simulation
Objects utilized in the simulation.
4. Mean Events per Simulation Object: It is the mean of the events per
simulation object.
5. STD Events per Simulation Object: It is the standard deviation of the
events per simulation object.
6. Mean of Cognitive Weights of All objects: It is the mean of the number of
cognitive weights used by the simulation objects in the simulation.
7. STD Cognitive Weights of All objects: It is the standard deviation of the
number of cognitive weights used by the simulation objects in the simulation.
8. Number of Global Nodes: It is the total number of Global Nodes in the
simulation.
9. Mean Local Nodes per Computer: It is the mean of the local nodes per
global node utilized in the simulation.
10. STD Local Nodes per Computer: It is the standard deviation of the local
nodes per global node utilized in the simulation.
11. Mean Number of cores: It is the mean number of cores/threads utilized by
each global node in the simulation.
12. STD Number of cores: It is the standard deviation of number of
cores/threads utilized by each global node in the simulation.
13. Mean processor Speed: It is the mean processor speed of the CPUs used in
the simulation.