Strong Scaling Achieves 15.2× Speedup for Dynamic Graph Updates with Multi-Threaded Efficiency

TLDR

Dynamic Frontier PageRank demonstrates robust strong-scaling behavior for batch updates, achieving an average speedup of 10.3× with 16 threads and 15.2× with 64 threads. It gains a 1.8× performance boost with every doubling of threads but faces NUMA limitations at higher thread counts. The method efficiently handles dynamic graph updates, scaling well across datasets and graph sizes.via the TL;DR App

Author:

(1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in).

Table of Links

Abstract and 1 Introduction

2 Related Work

3 Preliminaries

4 Approach

5.1 Experimental Setup

5.2 Performance of Dynamic Frontier PageRank

5.3 Strong Scaling of Dynamic Frontier PageRan

6 Conclusion, Acknowledgments, and References

5.3 Strong Scaling of Dynamic Frontier PageRan

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.

Written by pagerank | Algorithmic authority, ranking the web, PageRank's links and votes shape the hierarchy.

Published by HackerNoon on 2025/01/22