PARALLEL DISCRETE EVENT SIMULATION OF QUEUING NETWORKS USING GPU-BASED HARDWARE ACCELERATION By HYUNGWOOK PARK A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

of dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy PARALLEL DISCRETE EVENT SIMULATION OF QUEUING NETWORKS USING GPU-BASED HARDWARE ACCELERATION By Hyungwook Park December 2009 Chair: Paul A. Fishwick Major: Computer Engineering Queuing networks are used widely in computer simulation studies. Examples of queuing networks can be found in areas such as the supply chains, manufacturing work flow, and internet routing. If the networks are fairly small in size and complexity, it is possible to create discrete event simulations of the networks without incurring significant delays in analyzing the system. However, as the networks grow in size, such analysis can be time consuming and thus require more expensive parallel processing computers or clusters. The trend in computing architectures has been toward multicore central processing units (CPUs) and graphics processing units (GPUs). A GPU is the fairly inexpensive hardware, and found in most recent computing platforms, but practical example of single instruction, multiple data (SIMD) architectures. The majority of studies using the GPU within the graphics and simulation communities have focused on the use of the GPU for models that are traditionally simulated using regular time increments, whether these increments are accomplished through the addition of a time delta (i.e., numerical integration) or event scheduling using the delta (i.e., discrete event approximations of continuous-time systems). These types of models have the property of being decomposable over a variable or parameter space. In prior studies, discrete event simulation, such as a queuing network simulation, has been characterized as being an inefficient application for the GPU primarily due to the inherent synchronicity of