Scalable Triadic Analysis of Large-Scale Graphs: Multi-Core vs. Multi- Processor vs. Multi-Threaded Shared Memory Architectures

TL;DR

This paper presents an optimized parallel triad census algorithm designed for large-scale graphs, evaluated across different shared memory architectures to analyze performance scalability and efficiency.

Contribution

The paper introduces a scalable triadic analysis algorithm optimized for shared memory systems and compares its performance across three distinct hardware architectures.

Findings

Algorithm performs efficiently on large graphs with tens of millions of nodes.

Performance varies significantly across different shared memory architectures.

Shared memory architecture impacts the scalability and speedup of triadic analysis.

Abstract

Triadic analysis encompasses a useful set of graph mining methods that are centered on the concept of a triad, which is a subgraph of three nodes. Such methods are often applied in the social sciences as well as many other diverse fields. Triadic methods commonly operate on a triad census that counts the number of triads of every possible edge configuration in a graph. Like other graph algorithms, triadic census algorithms do not scale well when graphs reach tens of millions to billions of nodes. To enable the triadic analysis of large-scale graphs, we developed and optimized a triad census algorithm to efficiently execute on shared memory architectures. We then conducted performance evaluations of the parallel triad census algorithm on three specific systems: Cray XMT, HP Superdome, and AMD multi-core NUMA machine. These three systems have shared memory architectures but with markedly different hardware capabilities to manage parallelism.