Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
src
 
 
 
 
 
 
 
 
 
 
 
 
 
 

PACC

  • PACC (Partition Aware Connected Components) is a tool for computing connected components in a large graph.

  • Given an undirected simple graph, PACC returns the minimum reachable node id for each node.

  • It runs in parallel, distributed manner on top of Hadoop and Spark, which are widely-used distributed computation system.

  • Authors:

  • If your work uses or refers to PACC, please cite the paper using the following bibtex entry:

    @inproceedings{ParkPACC16,
      author = {Ha-Myung Park and
                   Namyong Park and
                   Sung-Hyon Myaeng and
                   U Kang},
      title = {Partition Aware Connected Component Computation in Distributed Systems},
      booktitle = {ICDM},
      year = {2016},
    }
    

Build

PACC uses SBT (Simple Build Tool) to manage dependencies and build the whole project. To build the project, type the following command in terminal:

tools/sbt assembly

How to run PACC

  • You may type "make demo" if you want to just try PACC.
  • Hadoop and/or Spark should be installed in your system in advance.

To run PACC, you need to do the followings:

  • prepare an edge list file of a graph in a HDFS directory. The edge list file is a plain text file where each line is in "NODE1 DELIMITER NODE2" format. The delimiter can be a whitespace like a tab or a space.

    For example, this is a sample edge file 'simple.edge'

0   2
0   3
1   2
1   3
1   5
2   3
2   4
2   6
4   6
7   8
8   9
7   10
  • execute PACC on Hadoop
Usage: hadoop jar bin/pacc-0.1.jar cc.hadoop.PACC [OPTIONS] [INPUT (EDGE LIST FILE)] [OUTPUT]

Options:
    -DnumPartitions          the number of partitions
    -DlocalThreshold         the threshold for LocalCC optimization (default: 1000000)

Example:
hadoop jar pacc-0.1.jar cc.hadoop.PACC -Dmapred.reduce.tasks=120 -DnumPartitions=120 -DlocalThreshold=20000000 path/to/input/file path/to/output/file
  • execute PACC on Spark
spark-submit --class cc.spark.PACC pacc-0.1.jar [OPTIONS] [INPUT (EDGE LIST FILE)] [OUTPUT]

Options:
    -p                      the number of partitions
    -t                      the threshold for LocalCC optimization (default: 1000000)

Example:
spark-submit --master yarn \
             --deploy-mode client \
             --num-executors 80 \
             --driver-memory 10G \
             --executor-cores 1 --executor-memory 6g \
             --class cc.spark.PACC
             pacc-ext-0.1.jar -p 80 -t 20000000 path/to/input/file path/to/output/file

You can test PACC with bash do_pacc_hadoop.sh and bash do_pacc_spark.sh.

The script executes PACC with a simple graph (simple.edge).

Real world datasets

Name #Nodes #Edges Description Source
Skitter 1,696,415 11,095,298 Internet topology graph, from traceroutes run daily in 2005 SNAP
Patent 3,774,768 16,518,948 Citation network among US Patents SNAP
LiveJournal 4,847,571 68,993,773 LiveJournal online social network SNAP
Friendster 65,608,366 1,806,067,135 Friendster online social network SNAP
Twitter 41,652,230 1,468,365,182 Twitter follower-followee network Advanced Networking Lab at KAIST
SubDomain 89,247,739 2,043,203,933 Domain level hyperlink network on the Web Yahoo Webscope
YahooWeb 720,242,173 6,636,600,779 Page level hyperlink network on the Web Yahoo Webscope

Synthetic datasets

RMAT graphs are generated using parameters (a, b, c, d) = (0.57, 0.19, 0.19, 0.05).

Name #Nodes #Edges #Edges/#nodes
RMAT-S-21 731,258 29,519,203 40.36
RMAT-S-23 2,735,400 120,517,935 44.05
RMAT-S-25 10,204,129 488,843,429 47.90
RMAT-S-27 38,034,673 1,974,122,517 51.90
RMAT-S-29 141,509,689 7,947,695,690 56.16
RMAT-D-21 979,581 379,966,649 387.88
RMAT-D-23 3,377,251 455,483,172 134.86
RMAT-D-25 10,208,216 488,846,951 47.88
RMAT-D-27 26,585,821 499,793,060 18.79
RMAT-D-29 59,716,543 502,594,547 8.41

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published