7 research outputs found
1996 July, University of Memphis bulletin
Vol. 85, No. 4 of the University of Memphis bulletin containing the graduate catalog for 1996-97, 1996 July.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1183/thumbnail.jp
1995 July, University of Memphis bulletin
Vol. 84, No. 4 of the University of Memphis bulletin containing the graduate catalog for 1995-96, 1995 July.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1181/thumbnail.jp
1997-1999, University of Memphis bulletin
University of Memphis bulletin containing the graduate catalog for 1997-1999.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1421/thumbnail.jp
1997 July, University of Memphis bulletin
Vol. 86, No. 4 of the University of Memphis bulletin containing the graduate catalog for 1997-99, 1997 July.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1185/thumbnail.jp
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Efficient Linked List Ranking Algorithms and Parentheses Matching as a New Strategy for Parallel Algorithm Design
The goal of a parallel algorithm is to solve a single problem using multiple processors working together and to do so in an efficient manner. In this regard, there is a need to categorize strategies in order to solve broad classes of problems with similar structures and requirements. In this dissertation, two parallel algorithm design strategies are considered: linked list ranking and parentheses matching
Some Optimally Adaptive Parallel Graph Algorithms on EREW PRAM Model
The study of graph algorithms is an important area of research in computer science, since graphs offer useful tools to model many real-world situations. The commercial availability of parallel computers have led to the development of efficient parallel graph algorithms.
Using an exclusive-read and exclusive-write (EREW) parallel random access machine (PRAM) as the computation model with a fixed number of processors, we design and analyze parallel algorithms for seven undirected graph problems, such as, connected components, spanning forest, fundamental cycle set, bridges, bipartiteness, assignment problems, and approximate vertex coloring. For all but the last two problems, the input data structure is an unordered list of edges, and divide-and-conquer is the paradigm for designing algorithms. One of the algorithms to solve the assignment problem makes use of an appropriate variant of dynamic programming strategy. An elegant data structure, called the adjacency list matrix, used in a vertex-coloring algorithm avoids the sequential nature of linked adjacency lists.
Each of the proposed algorithms achieves optimal speedup, choosing an optimal granularity (thus exploiting maximum parallelism) which depends on the density or the number of vertices of the given graph. The processor-(time)2 product has been identified as a useful parameter to measure the cost-effectiveness of a parallel algorithm. We derive a lower bound on this measure for each of our algorithms