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Essentials of Parallel Graph Analytics

Single-Source Shortest Path (SSSP) implementation in modern C++ for 2022 IPDPS workshop on Graphs, Architectures, Programming, and Learning (GrAPL 2022) submission. For a more complete implementation of the ideas presented in the paper, please refer to the on-going work of graph analytics on GPUs at gunrock/essentials.

System Version Status
Ubuntu Ubuntu 20.04 Ubuntu
Windows Windows Server 2019 Windows

Dependencies

  • C++20 for linux (requires gcc/g++-11 or higher), C++23 for windows.
  • cmake version 3.22.2.
  • tbb library for execution policies (automatically fetched using cmake).

Implementation Detail

This code base makes use of modern C++ features such as ranges, execution_policy, and lambda expressions to implement the essential components for parallel graph analytics. We focus on a simple implementation of Single-Source Shortest Path (SSSP), but the concepts can easily be extended to support other graph algorithms such as Breadth-First Search with minor changes to the lambda expression during traversal.

SSSP Traversal Condition

[&](vertex_t const& src,    // source
    vertex_t const& dst,    // destination
    edge_t const& edge,     // edge
    weight_t const& weight  // weight
   ) {
     weight_t new_d = distances[src] + weight;
     weight_t curr_d = atomic::min(&distances[dst], new_d, m_locks[dst]);
     return new_d < curr_d;
};

BFS Traversal Condition

[&](vertex_t const& src,    // source
    vertex_t const& dst,    // destination
    edge_t const& edge,     // edge
    weight_t const& weight  // weight
   ) {
     // If the neighbor is not visited, update the distance. Returning false
     // here means that the neighbor is not added to the output frontier, and
     // instead an invalid vertex is added in its place. These invalides (-1 in
     // most cases) can be removed using a filter operator or uniquify.
     if (distances[dst] != std::numeric_limits<vertex_t>::max())
       return false;
     else
       return (atomic::cas(
                   &distances[dst], std::numeric_limits<vertex_t>::max(),
                   iteration + 1) == std::numeric_limits<vertex_t>::max(), m_locks[dst]);
};

Quick Start Guide

Before building this project, make sure your system/compiler supports C++20 and cmake.

git clone https://github.com/gunrock/essentials-cpp.git
cd essentials-cpp
mkdir build && cd build
cmake .. 
make
bin/sssp ../datasets/chesapeake/chesapeake.mtx

How to Cite

Thank you for citing our work.

@InProceedings{   Osama:2022:EOP,
  author        = {Muhammad Osama and Serban D. Porumbescu and John D.
                  Owens},
  title         = {Essentials of Parallel Graph Analytics},
  booktitle     = {Proceedings of the Workshop on Graphs, Architectures,
                  Programming, and Learning},
  year          = 2022,
  series        = {GrAPL 2022},
  month         = may
}