Skip to content

Latest commit

 

History

History
79 lines (67 loc) · 2.36 KB

README.md

File metadata and controls

79 lines (67 loc) · 2.36 KB
Raw

GraphMini

GraphMini is a high-performance graph pattern-matching system. It supports subgraph enumeration on arbitrary patterns.

Hardware Requirements

  1. 128GB of free RAM to preprocess the graph Friendster correctly.
  2. 180GB of free disk space to store preprocessed graphs.

Software Dependencies

Operating System

  1. Ubuntu 22.04 (tested)

Libraries

  1. CMake (Version >= 3.20)
  2. Make
  3. MPI
  4. GCC compiler (>= 7)
  5. Python (>= 3.8)
  6. bc
  7. curl
  8. clang-format (optional)

Install libraries

sudo apt install curl bc cmake mpich clang-format -y

Tested Graph Data

  1. Wiki
  2. YouTube
  3. Patents
  4. LiveJournal
  5. Orkut
  6. Friendster

How to compile

mkdir -p build && cd build && cmake .. && make -j

How to download and preprocess data graph

After you successfully compile the code.

bash dataset/download.sh && bash dataset/prep.sh

How to run a single query with GraphMini

The binary takes 7 required and 1 optional input:

./build/bin/run [graph_name] [path_to_graph] [query_nickname] [query_adjmat] [query_type] [pruning_type] [parallel_type] [exp_id=-1 (optional)]
  • graph_name: nickname for tested graph (ex. wiki)
  • path_to_graph: path to the directory that contains the preprocessed graph. (ex ../Datasets/GraphMini/wiki)
  • query_nickname: nickname for tested query (ex. P1)
  • query_adjmat: adjacency matrix of the tested query (ex. "0111101111011110" 4-clique)
  • query_type:
    • 0: vertex-induced,
    • 1: edge-induced,
    • 2: edge-induced with IEP optimization
  • pruning_type:
    • 0: None: not pruning
    • 1: Static: only prune adj that must be used in the future
    • 2: Eager: prune all adj that might be used in the future
    • 3: Online: lazily prune adj that is being queried
    • 4: CostModel: using cost model to decide which adj to prune
  • parallel_type:
    • 0: OpenMP: parallel first loop only with OpenMP
    • 1: Tbb: parallel first loop only with Tbb
    • 2: Nested: nested loop for all computation
    • 3: NestedRt: nested loop + runtime information
  • exp_id: experiment id (for generating logs)

For example:

./build/bin/run wiki ./dataset/GraphMini/wiki P1 0111101111011110 0 4 3

This query runs P1 (4clique) on graph wiki. The query is vertex-induced. The executable uses CostModel to decide which adjacency lists to prune and uses nested parallelism to speed up query execution.