graph-csr-openmp

Design of high-performance OpenMP-based parallel Graph Edgelist and Compressed Sparse Row (CSR) loader, aka GVEL.

https://github.com/puzzlef/graph-csr-openmp

Science Score: 67.0%

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    Found 2 DOI reference(s) in README
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    Links to: arxiv.org, springer.com, zenodo.org
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    Low similarity (9.7%) to scientific vocabulary

Keywords

algorithm csr edgelist graph gvel load openmp parallel parser
Last synced: 4 months ago · JSON representation ·

Repository

Design of high-performance OpenMP-based parallel Graph Edgelist and Compressed Sparse Row (CSR) loader, aka GVEL.

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algorithm csr edgelist graph gvel load openmp parallel parser
Created about 2 years ago · Last pushed 9 months ago
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Readme License Citation

README.md

Design of high-performance OpenMP-based parallel Graph CSR loader.

High-performance graph processing frameworks like Gunrock, Hornet, Ligra, and Galois help accelerate graph analytics tasks. However, graph loading is a major bottleneck in these frameworks. Fast graph loading is crucial for improving response time and reducing system/cloud usage charges. To address this, we introduce GVEL, a highly optimized method for reading Edgelists from text files and converting them into Compressed Sparse Row (CSR) format.

Below we plot the time taken by Hornet, Gunrock, PIGO, and GVEL for reading Edgelist and converting it to CSR on 13 different graphs. PIGO and GVEL are not visible on this scale - they are significantly faster than Hornet and Gunrock. The graph loading time for Hornet is not shown for uk-2002, it-2004, and sk-2005 graphs as it crashed while loading. GVEL surpasses Hornet, Gunrock, and PIGO in CSR reading by 78×, 112×, and 1.8×, respectively.

Below we plot only the time taken by PIGO and GVEL for reading Edgelist and converting it to CSR.

Next, we plot the time taken by PIGO and GVEL for reading Edgelist. Here, GVEL outperforms PIGO by 2.6×, achieving a read rate of 1.9 billion edges/s with 64 threads.

Finally, we plot the strong scaling behaviour of GVEL for reading Edgelist, and for reading CSR.

Refer to our technical report for more details: \ GVEL: Fast Graph Loading in Edgelist and Compressed Sparse Row (CSR) formats.


[!NOTE] You can just copy main.sh to your system and run it. \ For the code, refer to main.cxx.



Code structure

The code structure of GVEL is as follows:

bash - inc/_cctype.hxx: Character classification/conversion - inc/debug.hxx: Debugging macros (LOG, ASSERT, ...) - inc/exception.hxx: Custom exception class (FormatError) - inc/_mman.hxx: Memory mapping/allcation functions - inc/_openmp.hxx: OpenMP utility functions - inc/_string.hxx: Number parsing/string tokenization - inc/_utility.hxx: Runtime measurement functions - inc/_vector.hxx: Vector utility functions - inc/io.hxx: COO/MTX file reading functions - main.cxx: Experimentation code - process.js: Node.js script for processing output logs

Note that each branch in this repository contains code for a specific experiment. The main branch contains code for the final experiment. If the intention of a branch in unclear, or if you have comments on our technical report, feel free to open an issue.



References

Graph formats

Graph frameworks

Memory mapping

String tokenization

Parsing numbers

Parsing numbers (SIMD)

Stringifying numbers

Performance optimization

Smart pointers

Exception handling

Basics

Testing




ORG DOI

Owner

  • Name: puzzlef
  • Login: puzzlef
  • Kind: organization

A summary of experiments.

Citation (CITATION.bib)

@article{sahu2024gvel,
  title={GVEL: Fast Graph Loading in Edgelist and Compressed Sparse Row (CSR) formats},
  author={Sahu, Subhajit},
  journal={arXiv preprint arXiv:2311.14650},
  year={2023}
}

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Subhajit Sahu w****7@g****m 46

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