InfoVis Infrastructure Workshop

Chris MuellerOpen Systems Lab, Indiana University

October 9, 2004chemuell at cs dot indiana dot edu

www.osl.iu.edu

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)

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

Boost Graph Library Algorithms

BC Clustering Performance BGL vs. JUNG

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Actor Collaboration DatabaseBetweenness Centrality Clustering (threshold=0.01)

Single Processor

Scalability of Parallel BGL Betweenness Centrality

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Actor Collaboration DatabaseBetweenness Centrality

Dot Plot Performance ResultsBase SIMD 1 SIMD 2 Thread

Ideal 140 1163 1163 2193

NFS 88 370 400 -

NFS Touch 88 - 446 891

Local - 500 731 -

Local Touch 90 - 881 1868

• 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

Ideal Speedup Real Speedup Ideal/Real Throughput

SIMD 8.3x 9.7x 75%

Thread 15x 18.1x 77%

Thread (large data) 13.3 21.2 85%