Hi all, I'm learning to use the distributed adjacency list and distributed dijkstra SP-algorithm. I use some test data contains about 3,800,000 edges and 2,800,000 vertices. I use a pair of iterators to pass the data to the distributed adjacency list, like that: Graph g(reader.begin(), reader.end(), reader.weight_begin(), reader.num_vertices(), pg, dist); "reader" is an user-defined class which supplys required data (edges, vertices, weights). "pg" is defined like that "mpi_process_group pg;" "dist" is supplied by the reader like that "graph::metis_distribution dist = reader.distribution();". It is based on a metis partition file coordinating the test data. I run 2 processes on my notebook computer(Intel core2 2.0G, Ram 2G, WinXPsp3). I also test the same data using a sequential bgl dijkstra SP-algorithm on this computer. The result is that: Parallel bgl: Creating of Graph g costs *10080 seconds*; dijkstra computing costs 28.281 seconds. Sequential bgl: Creating of Graph g costs *4.563 seconds*; dijkstra computing costs 1.703 seconds. As you see, when using distributed adjacency list, it costs so much time to create the graph. Is it nomal? I have done some more comparison between 2-processes parallel dijkstra on one core2 computer and sequential dijkstra on the same computer, using different scale data set. Always, the sequential bgl dijkstra costs less time than the 2-processes parallel dijkstra. What's the problem? I have read the boost document on "boost_1_46_1\libs\graph_parallel\doc\html\dijkstra_shortest_paths.html", which shows a good performance and speedup in the charts. Doesn't that mean less time costing than the sequential program? Any advice would be appreciated. Thanks, Yan -- View this message in context: http://boost.2283326.n4.nabble.com/Why-does-the-creating-of-distributed-adja... Sent from the Boost - Users mailing list archive at Nabble.com.