Improving the Graph Challenge Reference Implementation

TL;DR

This paper refactors and benchmarks a section of the Graph Challenge reference implementation, significantly reducing code size and enhancing clarity, adaptability, and performance for large-scale graph data analysis.

Contribution

It presents a streamlined, efficient, and scalable refactoring of the Graph Challenge reference code, improving usability and performance for large-scale graph processing.

Findings

67% reduction in code size

Achieved scalable performance for large traffic matrices

Enhanced clarity and adaptability of the implementation

Abstract

The MIT/IEEE/Amazon Graph Challenge provides a venue for individuals and teams to showcase new innovations in large-scale graph and sparse data analysis. The Anonymized Network Sensing Graph Challenge processes over 100 billion network packets to construct privacy-preserving traffic matrices, with a GraphBLAS reference implementation demonstrating how hypersparse matrices can be applied to this problem. This work presents a refactoring and benchmarking of a section of the reference code to improve clarity, adaptability, and performance. The original Python implementation spanning approximately 1000 lines across 3 files has been streamlined to 325 lines across two focused modules, achieving a 67% reduction in code size while maintaining full functionality. Using pMatlab and pPython distributed array programming libraries, the addition of parallel maps allowed for parallel benchmarking of the data. Scalable performance is demonstrated for large-scale summation and analysis of traffic matrices. The resulting implementation increases the potential impact of the Graph Challenge by providing a clear and efficient foundation for participants.