Engineering Scalable Distributed List Ranking

2026-06-08

AI Summary

This work addresses the scalability bottleneck of list ranking in large-scale distributed systems involving billions of elements and tens of thousands of processors. Building upon Sibeyn’s sparse dominating set algorithm, the authors achieve the first highly scalable solution for this classic problem at extreme scales through a co-design of algorithmic and performance optimizations, including indirect communication, exploitation of input locality, and message aggregation. The study provides a systematic quantitative analysis of how key parameters influence performance and demonstrates the approach’s high scalability and practicality across diverse structured inputs on up to 24,576 processor cores.

Abstract

The list ranking problem is one of the classical problems of parallel computing, with nontrivial algorithms and many applications as a subroutine for solving other problems. While it has been intensively studied in the early days of parallel computing, few things happened in the last 20 years. In particular, there is little work on scaling list ranking to large machines and input sizes. We reconsider list ranking starting from the ground-breaking results of Sibeyn a quarter century ago. We employ algorithm and performance engineering to improve his sparse ruling-set algorithm, making it capable of scaling to many processors, and provide a more detailed analysis of the impact of the algorithm's parameters, further guiding our practical implementation. We perform an extensive experimental study across a variety of input instances with different structural properties. We demonstrate that indirect communication, exploiting input locality, and message coalescing allows scaling to billions of elements on up to 24,576 cores.

Problem

• list ranking
• scalable
• distributed computing
• parallel algorithms
• large-scale

Innovation

• list ranking
• sparse ruling-set
• scalable distributed algorithm
• message coalescing
• input locality