CGMgraph/CGMlib: Implementing and testing cgm graph algorithms on PC clusters and shared memory machines

被引:26
作者
Chan, A [1 ]
Dehne, F
Taylor, R
机构
[1] Fayetteville State Univ, Dept Math & Comp Sci, Fayetteville, NC 28301 USA
[2] Griffith Univ, Sch ICT, Brisbane, Qld 4111, Australia
[3] Carleton Univ, Sch Comp Sci, Ottawa, ON K1S 5B6, Canada
关键词
coarse-grained multiprocessor; graph algorithms; library; parallel algorithms implementation; processor cluster;
D O I
10.1177/1094342005051196
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we present CGMgraph, the first integrated library of parallel graph methods for PC clusters based on Coarse Grained Multicomputer (CGM) algorithms. CGMgraph implements parallel methods for various graph problems. Our implementations of deterministic list ranking, Euler tour, connected components, spanning forest, and bipartite graph detection are, to our knowledge, the first efficient implementations for PC clusters. Our library also includes CGMlib, a library of basic CGM tools such as sorting, prefix sum, one-to-all broadcast, all-to-one gather, h-Relation, all-to-all broadcast, array balancing, and CGM partitioning. Both libraries are available for download at http: //www. scs. carleton. ca/similar tocgm. In the experimental part of this paper, we demonstrate the performance of our methods on four different architectures: a gigabit connected high performance PC cluster, a smaller PC cluster connected via fast ethernet, a network of workstations, and a shared memory machine. Our experiments show that our library provides good parallel speedup and scalability on all four platforms. The communication overhead is, in most cases, small and does not grow significantly with an increasing number of processors. This is a very important feature of CGM algorithms which makes them very efficient in practice.
引用
收藏
页码:81 / 97
页数:17
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