Engineering Scalable Distributed List Ranking

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 the problem was intensively studied in the early days of parallel computing, it has not received much attention 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 allow scaling to billions of elements on up to 24 576 cores.