Beyond Traffic Matrix: DELTA -- A DAG-Aware OCS Logical Topology Optimization for AIDCs

TL;DR

DELTA is a DAG-aware topology optimization framework for AI data centers that reduces optical port usage and communication time by leveraging workload DAGs and innovative MILP modeling.

Contribution

It introduces a novel DAG-based optimization method with variable-length intervals and scalable acceleration strategies for large-scale AI data centers.

Findings

Reduces communication time by up to 17.5% compared to baselines.

Cuts optical port consumption by at least 20%.

Improves workload performance gap by up to 26.1%.

Abstract

The rapid scaling of large language models (LLMs) exacerbates communication bottlenecks in AI data centers (AIDCs). To overcome this, optical circuit switches (OCS) are increasingly adopted for their superior bandwidth capacity and energy efficiency. However, their reconfiguration overhead precludes intra-iteration topology update, necessitating a priori engineering of a static topology to absorb time-varying LLM traffic. Existing methods engineer these topologies based on traffic matrices. However, this representation obscures the bursty concurrent bandwidth demands dictated by parallelization strategies and fails to account for the independent channels required for concurrent communication. To address this, we propose DELTA, an efficient logical topology optimization framework for AIDCs that leverages the computation-communication directed acyclic graph (DAG) to encode time-varying traffic patterns into a Mixed-Integer Linear Programming (MILP) model, while exploiting the temporal slack of non-critical tasks to save optical ports without penalizing iteration makespan. By pioneering a variable-length time interval formulation, DELTA significantly reduces the solution space compared to the fixed-time-step formulation. To scale to thousand-GPU clusters, we design a dual-track acceleration strategy that combines search space pruning (reducing complexity from quadratic to linear) with heuristic hot-starting. Evaluations on large-scale LLM workloads show that DELTA reduces communication time by up to 17.5% compared to state-of-the-art traffic-matrix-based baselines. Furthermore, the framework reduces optical port consumption by at least 20%; dynamically reallocating these surplus ports to bandwidth-bottlenecked workloads reduces their performance gap relative to ideal non-blocking electrical networks by up to 26.1%, ultimately enabling most workloads to achieve near-ideal performance.