DSA

1. CONNECTED COMPOUNDS

2.  Studied implemented and evaluated
various parallel algorithms for
connected components labeling in
graphs.
 Two Architectures
 CPU– (OpenMp) and (CUDA)
 Different types of graphs.
 Propose simple auto tuned approach for
choosing best technique for a graph.

3.  Motivation
 Definitions
 Basic Algorithm
 Optimizations
 Datasets and Experiments
 Auto tuning
 Future Scope

4.  Identify vertices that form a
connected set in a graph.
 Used in:
 Pattern Recognition
 Physics
 Biology
 Social Network Analysis

5.  Image Processing
 Pattern Recognition
 Gesture Recognition
Spee
d
Limit
40
Speed
Limit
40
Spee
d
Limit
40

6.  Disjoint Set Union
Make Set
Union
Link
Find Set
 Depth First Search

7.  Directed tree of h = 1
 Root points to itself
 All children point to the root
 Root is called the representative of a
connected components.

8.  ( I,J ) is an edge in the graph.
 If i and j are currently in
differently trees.
 Merge the two trees in to one.
 Make representation of one,
point to the representative of
the other.

9.  Merging two trees T1 and T2.
 Whose representative should be
changed?
 Toss a coin and choose a winner
 Tree with lower index wins always
 Alternate between iterations
(Even, Odd)
 Tree with greater height wins

10.  Move a node higher in the tree.
Single level
Multi level
Final Aim
 EXAMPLE:
 Singletons
 Hooking
 Point jumping

11.  Single instance edge storage
( u,v ) is same as (u,v)
Reduced Memory Footprint
Smaller traversal overhead
 Unconditional Hooking
Calling at appropriate iteration
helps in decreasing the number of
iterations.

12.  Only form stars in every iteration.
 No overhead in determining if a node is
part of a star.

13.  Random graphs
1Mto 7M nodes, average degree 5
 RMAT Graphs
Synthetic Social Networking
1M to 7M nodes
 Real world Data
 Web graphs
Google web
Berkeley Stanford domains

14. Thank you!!!