# A Locality-Aware Bruck Allgather

arXiv: https://arxiv.org/abs/2206.03564 · HTML: https://mlsanity.com/paper/2206.03564/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2022.

## Built on by (most cited first, 1 of 1)

- [Bine Trees: Enhancing Collective Operations by Optimizing Communication Locality](https://mlsanity.com/paper/2508.17311/) (2025)

## Abstract (arXiv)

Collective algorithms are an essential part of MPI, allowing application programmers to utilize underlying optimizations of common distributed operations. The MPI_Allgather gathers data, which is originally distributed across all processes, so that all data is available to each process. For small data sizes, the Bruck algorithm is commonly implemented to minimize the maximum number of messages communicated by any process. However, the cost of each step of communication is dependent upon the relative locations of source and destination processes, with non-local messages, such as inter-node, significantly more costly than local messages, such as intra-node. This paper optimizes the Bruck algorithm with locality-awareness, minimizing the number and size of non-local messages to improve performance and scalability of the allgather operation