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https://github.com/gunrock/essentials-cpp

Single-Source Shortest Path (SSSP) implementation in modern C++ for 2022 IPDPS workshop on Graphs, Architectures, Programming, and Learning (GrAPL 2022) submission.
https://github.com/gunrock/essentials-cpp

cpp20 graph-algorithms parallel-programming single-source-shortest-path

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Single-Source Shortest Path (SSSP) implementation in modern C++ for 2022 IPDPS workshop on Graphs, Architectures, Programming, and Learning (GrAPL 2022) submission.

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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](https://github.com/gunrock/essentials).

| System | Version | Status |
|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|
| Ubuntu | [Ubuntu 20.04](https://docs.github.com/en/actions/using-github-hosted-runners/about-github-hosted-runners#supported-runners-and-hardware-resources) | [![Ubuntu](https://github.com/neoblizz/sssp/actions/workflows/ubuntu.yml/badge.svg)](https://github.com/neoblizz/sssp/actions/workflows/ubuntu.yml) |
| Windows | [Windows Server 2019](https://docs.github.com/en/actions/using-github-hosted-runners/about-github-hosted-runners#supported-runners-and-hardware-resources) | [![Windows](https://github.com/neoblizz/sssp/actions/workflows/windows.yml/badge.svg)](https://github.com/neoblizz/sssp/actions/workflows/windows.yml) |

## 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
```cpp
[&](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
```cpp
[&](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::max())
return false;
else
return (atomic::cas(
&distances[dst], std::numeric_limits::max(),
iteration + 1) == std::numeric_limits::max(), m_locks[dst]);
};
```

## Quick Start Guide
Before building this project, make sure your system/compiler supports **C++20** and **cmake**.

```bash
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.
```tex
@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
}
```