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Betweenness Centrality GPU

Betweenness Centrality for large sparse graphs on GPU using CUDA

Team:

  • Dibyadarshan Hota 16CO154
  • Omkar Prabhu 16CO233

Usage

  1. Random Graph Generator

    $ g++ g_generator.cpp
$ ./a.out > graph10p4
65536 65536

  2. Serial Implementation

    $ g++ serial.cc
$ ./a.out < graph10p4

  3. Parallel Implementation using using Work-efficient Method(p_imp_1)

    $ nvcc main_work_efficient_parallel.cu
$ ./a.out < graph10p4

  4. Parallel Implementation using Vertex-parallel Method (p_imp_2)

    $ nvcc main_vertex_parallel.cu
$ ./a.out < graph10p4

File Structure

Code:

  • main_work_efficient_parallel.cu or p_imp_1.cu - Parallel Implementation using Work-efficient Method
  • main_vertex_parallel.cu or p_imp_2.cu - Parallel Implementation using Vertex-parallel Method
  • main_vertex_parallel-serial.cu
  • serial.cc - Serial implementation
  • g_generator.cpp - Random Gaph Generator Our Implementation
  • parse.py - Convert 1 to 0 based node index
  • final_generator.cpp - Random Gaph Generator used by the class
  • parse.c++ - Parse output from final_generator to covert to our input format

Results and Inputs:

  • results-common-graphs/ - Results for test for common graphs for the class
  • results-g-generator/ - Results for test for graphs from our graph generator
  • input_format/ - Contains sample input format

Results and Summary

Report

References