Arya

Major components include:

graph_decompose: subgraph decomposing logic
graph_counting: sampling to count logic
peregrine: a previous enumerating work as baseline

Graph Counting

Count number of certain patterns in a large graph

Quick Test

Single Machine multi-threading setting:

cd graph_counting/src/{branch}
make clean ; make all
./GraphCounting.out ../../graphs/mico/mico.undigraph ../../patterns/triangle 40000000 40

Distributed Replicated Graph setting:

cd graph_counting/src/{branch}
make clean ; make all
mpirun -n 4 -f host ./GraphCounting.out graphs/mico/mico.undigraph patterns/triangle 40000000 40 4

Output:

raw results after merge nodes (total_prob_inverse, total_sampled_times): 502464449642959.8 40000000
pattern_nums time_consumed(us) sampling_times sampling/second
12561611.24107399 2065807 40000000 19362893.04857617

Usage:

GraphCounting graph_file subgraph_file sampling_times(separated by \',\' 10000,20000) #threads(separated by \',\' 1,4) #processes

graph_file: edge list of the large graph ; sorted by vertex ids, each edge has two copies (e.g., (1,2), (2,1)), example:

subgraph_file: pattern to count ; example: ./patterns/triangle_2_star
- the subgraph_file is supposed to be output from the graph_decomposing logic

# first line: num of odd-cycles (k), num of stars (j) in the pattern
# following k lines: odd-cycles
# following j lines: stars (the first vertex is the center)
# following lines: remaining edges to test
1 1   # indicates 1 odd-cycle, 1 star
1 2 3 # the first and only odd-cycle
4 5 6 # the first and only star
2 4   # only one remaining edge

Memcached - Version (batched_distributed_samplers and fail_prob_aware_distributed_samplers branch)

Step 0: Install Memcached and libmemcached
- cd scripts ; bash config.sh
- remote config: cd scripts; bash remote_config.sh node1 to config node1
(Not Used) Start Memcached with:
- enable remote connect: pkill memcached ; ufw allow 11211; memcached -d -m 100000 -p 11211 -u root -M
- NOTE: no need to start with automatic loading
Step 1: load graph:
- cd scripts ; python multi_machine_load.py graph_directory
  - e.g. python multi_machine_load.py ../split_graph/mico
  - single machine load: python single_machine_load.py graph_path edge_start_id, e.g. python single_machine_load.py global_subgraph_0 0
- in the graph_directory:
  - machine_info: must contain num_machines and total_edges in the first line
  - each line is a memcached node
Step 2: distributed estimators:
- python distributed_estimators.py machine_info pattern total_num_estimators num_threads_per_node
- e.g. python ./distributed_estimators.py ./split_graph/mico/machine_info ./patterns/5_cycle_2_star 10000000 20

MPI Installation

We use MPI and multi-threading for load balancer of the replicated graph setting.

First set up ssh between node0 and other nodes.

MPI installalation:

Download MPICH3 (http://www.mpich.org/downloads/) from mpich.org and extract the contents of the MPICH package to some temporary location, before compiling the source code to binaries. We download MPICH v3.2 (http://www.mpich.org/static/downloads/3.2.1/mpich-3.2.1.tar.gz).

cd {the-directory-containing-downloaded-mpich-package}
tar -xvzf mpich-3.2.1.tar.gz
cd mpich-3.2.1

Choose a directory for installing MPICH3 (we use /usr/local/mpich), and then compile and install MPICH3.

./configure -prefix=/usr/local/mpich --disable-fortran
sudo make
sudo make install

Append the following two environment variables to the file $HOME/.bashrc. Here, $HOME, ~ and /home/{your_username} are equivalent, which is your home folder.

export MPICH_HOME=/usr/local/mpich
export PATH=$PATH:$MPICH_HOME/bin

Compile the file with the command source $HOME/.bashrc.