Transcript Boost Graph Library Performance Numbers

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
– 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)
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
BC Clustering Performance
BGL vs. JUNG
Wall clock time (minutes)
60
50
40
BGL
30
JUNG
20
10
0
200 225 250 275 300 325 350 375 400
# of Movies
Actor Collaboration Database
Betweenness Centrality Clustering (threshold=0.01)
Single Processor
Scalability of Parallel BGL Betweenness C entrality
Wall clock time (seconds)
1000
100
10
1
1
10
# of Processors
Actor Collaboration Database
Betweenness Centrality
100
Dot Plot Performance Results
Base
SIMD 1
SIMD 2
Thread
Ideal
140
1163
1163
2193
NFS
88
370
400
-
NFS Touch
88
-
446
891
-
500
731
-
90
-
881
1868
Local
Local Touch
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%
• 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