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InfoVis Infrastructure Workshop

InfoVis Infrastructure Workshop. Chris Mueller Open Systems Lab, Indiana University October 9, 2004 chemuell at cs dot indiana dot edu www.osl.iu.edu. Overview. Position Paper Repository style infrastructure (SourceForge, GenBank, CPAN) Standard software protocols

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InfoVis Infrastructure Workshop

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  1. InfoVis Infrastructure Workshop Chris Mueller Open Systems Lab, Indiana University October 9, 2004 chemuell at cs dot indiana dot edu www.osl.iu.edu

  2. Overview • Position Paper • Repository style infrastructure (SourceForge, GenBank, CPAN) • Standard software protocols • Guiding policies to help ensure quality • Current Work – Open Systems Lab, IU • High performance components for IVC • Boost Graph Library • Very large data sets/visualization • Interests • Understand community needs • Learn what’s available, where we’re going • Industry Viewpoint • Web-based Scientific Visualization and Analysis products • In-house visualization and analysis tools (high-throughput analytical chemistry)

  3. Boost Graph Library Algorithms Core Algorithm Patterns breadth_first_search breadth_first_visit depth_first_search depth_first_visit undirected_dfs Shortest Paths Algorithms dijkstra_shortest_paths bellman_ford_shortest_paths dag_shortest_paths johnson_all_pairs_shortest_paths Minimum Spanning Tree Algorithms kruskal_minimum_spanning_tree prim_minimum_spanning_tree connected_components strong_components Incremental Connected Components initialize_incremental_components incremental_components same_component component_index Maximum Flow Algorithms edmunds_karp_max_flow push_relabel_max_flow topological_sort transitive_closure copy_graph transpose_graph isomorphism cuthill_mckee_ordering sequential_vertex_coloring* minimum_degree_ordering sloan_ordering ith_wavefront, max_wavefront, aver_wavefront, and rms_wavefront • Recent Additions • Betweenness Centrality • Betweenness Centrality clustering • A* search • Floyd-Warshall all-pairs shortest paths • Kamada-Kawai layout

  4. Actor Collaboration Database Betweenness Centrality Clustering (threshold=0.01) Single Processor

  5. Actor Collaboration Database Betweenness Centrality

  6. Dot Plot Performance Results • Base is a direct port of the DOTTER algorithm • SIMD 1 is the SIMD algorithm using a sparse matrix data structure based on STL vectors • SIMD 2 is the SIMD algorithm using a binary format and memory mapped output files • Thread is the SIMD 2 algorithm on 2 Processors

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