Design and Implementation of an Efficient Priority Queue Data Structure

Abstract

Priority queues are among the most useful of all data structures. Existing priority queues have a vast amount of overhead associated with them. There is a need to have a simple data structure that can be used as a priority queue with low overhead. The data structure should have the operation where the data item with the minimum/maximum value is the next item to be deleted. The data structure should also support the function of a calendar queue where elements with the same or similar priority have the same key. For example, all of today’s appointments will have today’s date as their key. To that end, a bucket data structure has been developed that has both of these features. We address the functionality and efficiency of the data structure for the applications of adaptive multivariate integration and the 15-puzzle. In adaptive multivariate integration, the key is an error estimate, which is a floating-point number. The data for this application has many items with similar keys and the maximum is the next item to be deleted. The key for the 15-puzzle is generated by a heuristic cost function. The data for this application has many items with the same key and the minimum is the next item to be deleted. This paper presents an implementation of the bucket priority queue and discusses its performance.