Efficient parallel algorithms and data structures for discrete-event simulation


Discrete-event simulation, which is a major tool for analysis, prediction, and training, has been demanding increasingly high computational resources. It has continued to defy an efficient parallel solution. A long-st anding serial bottleneck in parallelizing event-driven simulation ,va.s the shared data structure - event-queue which holds all thP future events. The event-queue is an instance of the abstract data structure~ priority queue. \Ve have developed a noYel parallel data structure called parallel heap for exclusive-read exclusive-write parallel randon1-access 1nachines (ER.EvV PRA11). The parallel heap is the first such data structure to provide an optimal in1plen1entation of the priority quPues. Using p processors, it a.llows deletion of O(p) highest priority items and insertion of O(p) ne,v items, each in O(log n) time, where n is size of the parallel heap. The number of processors can be optimally varied from l through n. Employing th~ parallel heap as the underlying data structure~ we have developed four efficient ERE\\' PR.AN! algorithms for discrete-event simulation. Our three algorithms can sitnula.te O(p) events in O(log n) time using p processors, 1 < p < n, where n is the number of events scheduled in a parallf'l heap. All of our algorithms ensure balanced computational load among the procPssors. Thus .. they are superior to the existing parallel simulation algorith1ns which do not have the advantage of a parallel heap. The parallel h('a.p data structure seems ·applicable in a variety of other application areas also including branch-and-bound algorithms .. nutltiprocessor scheduling, and shortest-pa.th a.lgorithn1s. To explore the experimental aspects of simulation. a parallel time-driven battlefield simulation program has been i1nplemented on an Intel's iPSC-1 simulator. This program is being used to study dynamic sharing of a processor's computational load among its immediate neighbors.


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Graduation Date





Deo, Narsingh


Doctor of Philosophy (Ph.D.)


College of Arts and Sciences


Computer Science




124 p.



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Doctoral Dissertation (Open Access)




Arts and Sciences -- Dissertations, Academic; Dissertations, Academic -- Arts and Sciences

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