Ant Mill: an adversarial traffic pattern for low-diameter direct networks

• Published in: The Journal of supercomputing Vol. 80; no. 12; pp. 18062 - 18080
• Main Authors: Camarero, Cristóbal, Martínez, Carmen, Beivide, Ramón
• Format: Journal Article
• Language: English
• Published: New York Springer US 2024; Springer Nature B.V
• Subjects: Compilers; Computer Science; Interpreters; Network topologies; Performance evaluation; Processor Architectures; Programming Languages; Traffic; Interconnection network; Random regular graphs; Data center; Adversarial traffic pattern
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-024-06146-1

Since today’s HPC and data center systems can comprise hundreds of thousands of servers and beyond, it is crucial to equip them with a network that provides high performance. New topologies proposed to achieve such performance need to be evaluated under different traffic conditions, aiming to closely replicate real-world scenarios. While most optimizations should be guided by common traffic patterns, it is essential to ensure that no pathological traffic pattern can compromise the entire system. Determining synthetic adversarial traffic patterns for a network typically relies on a thorough understanding of its topology and routing. In this paper, we address the problem of identifying a generic adversarial traffic pattern for low-diameter direct interconnection networks. We first focus on Random Regular Graphs (RRGs), which represent a typical case for these networks. Moreover, RRGs have been proposed as topologies for interconnection networks due to their superior scalability and expandability, among other advantages. We introduce Ant Mill, an adversarial traffic pattern for RRGs when using routes of minimal length. Secondly, we demonstrate that the Ant Mill traffic pattern is also adversarial in other low-diameter direct interconnection networks such as Slimfly, Dragonfly, and Projective networks. Ant Mill is thoroughly motivated and evaluated, enabling future studies of low-diameter direct interconnection networks to leverage its findings.