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TriangleCount

LSTC is a project that provides a high-performance and Memory Efficient Graph Analytical Framework for GPUs. We use Cuda and various graph partitioning-based parallel programs for counting the number of triangles in a CSR sparse matrix graphs.

Requirements

DGL is a framework that provides the program's graph transformations.

  • Download, build and install Nvidia CUDA.
    • Note: We use Cuda 11.7 for this project.
  • Download, build, and install DGL 2.x , on link Document. 
    pip install  dgl -f https://data.dgl.ai/wheels/torch-2.1/cu118/repo.html
    • Note: please see your compatible Cuda version and install dgl based on this.

How to Run

1. Download the Dataset

Download the dataset from websites like GraphChallenge or SuiteSparse.

2. Clean the Dataset and Convert to Undirected CSR

  1. Navigate to the preprocessing directory: 
    cd preprocess_GPU/generate_clean_undir_dataset/
  2. Run the preprocessing script: 
    python3 Halo_creation_preprocess.py <dataset_file>
    Example: 
    python3 Halo_creation_preprocess.py ../data/Dataset/GraphChallange/amazon0302_adj.tsv
  3. This generates the undirected CSR file <filename.csr> and partitioned node parts files <filename.csr_node_parts_<part_method>_<part_number>.
  4. Create the induced subgraph by cleaning the redundant vertex and edges: 
    cd ../../
cd preprocess_GPU/Halo_cleanned_preprocess/
./round_clean_sg <part_number> <filename.csr> <filename.csr_node_parts_<part_method>_<part_number>
    Example: 
    ./round_clean_sg 2 it-2004_convert_part.csr it-2004_convert_part.csr_node_parts_random_2
  5. The generated file is a cleaned induced subgraph for triangle counting.

3. Triangle Count with Partition

  1. Navigate to the source directory: 
    cd TriangleCount/src/WithPartition/
  2. Compile the code: 
    ./compile.sh
  3. Run the triangle count: 
    ./wo_sort_tc_256 <filename> <part_number>
    Example: 
    ./wo_sort_tc_256 ../LSTC/Dataset_part/it-2004_convert_part.csr_node_parts_random_2_output.csr 2

4. Triangle Count Without Partition

  1. Preprocess the data for triangle counting without partition: 
    cd TriangleCount/Med_dataset/
python3 without_part_preprocess.py <filename>
    Example: 
    python3 without_part_preprocess.py ../data/Dataset/GraphChallange/amazon0302_adj.tsv
  2. Navigate to the source directory: 
    cd ../src/WithoutPartition/
  3. Compile the code: 
    ./compile.sh
  4. Run the triangle count: 
    ./TC_128 <filename.csr>
    Example: 
    ./TC_128 /data/kishan/TriangleCount/Med_dataset/wikipedia/wikipedia_link_en_output.csr

5. Compare Different Parameters

  1. Navigate to the source directory: 
    cd src/WithoutPartition/
  2. Run with atomic operations: 
    ./With_Atomic_op <filename.csr>
  3. Run without shared memory operations: 
    ./Without_ShareMemory_TC <filename.csr>

6. Profiling Options

  1. Navigate to the profiling directory: 
    cd src/profiling/
  2. Compile the code: 
    ./compile.sh
  3. Run the profiling script: 
    ./run.sh
    Note: In run.sh, pass the <filename.csr> generated in Med_dataset/without_part_preprocess.py file.