Network simulation plays a critical role in improving the efficiency of large-scale AI clusters for design validation, parameter tuning, and protocol development. However, high-fidelity network simulation becomes prohibitively slow at scale, especially when running large batches of experiments on topologies with tens or hundreds of thousands of accelerators. We observe that a key bottleneck comes from the control plane. Existing network simulators typically compute routes and install forwarding tables at initialization, which can consume hundreds of GB of memory before packet-event execution begins and limit overall simulation throughput. In this paper, we present Nüwa, which views routing as a compilation problem, it leverages the hierarchical and symmetric structure common in AI fabrics and compiles a declarative topology description together with routing policies into compact forwarding artifacts that are fast to generate and efficient to look up. Evaluations show that Nüwa can reduce simulation initialization time from hours to only 25 seconds for a 65,536-GPU cluster. For end-to-end simulation time, Nüwa takes only 20% of that required by existing approaches in a 40K+ GPU cluster, and Nüwa can scale to a 221,184-GPU cluster.
Wenkai Li, Ran Shu, Peng Zhang et al.· Proceedings of the ACM SIGCO...· 0 citations
Training trillion-parameter models has made collective communication a dominant bottleneck in GPU clusters. Existing approaches face a fundamental tension: optimization-based synthesis techniques suffer from combinatorial complexity that can require minutes to hours, while analytic heuristics rely on rigid assumptions that often fail under production variability. To the best of our knowledge, we present OptiFlow, among the first LLM-driven frameworks for automated design of high-performance collective communication algorithms. Our key insight is a two-layer decomposition: the LLM generates compact data-movement intent expressed in a domain-specific language, while deterministic scheduling algorithms compile these programs into executable schedules. We further employ an iterative pipeline that uses real-hardware feedback to refine LLM-generated proposals, without updating model parameters or relying on manually designed search heuristics. Evaluated on a 32-GPU NVIDIA A100 cluster, OptiFlow discovers All-Gather schedules that outperform NCCL by up to 3.75 × , while it also surpasses TACCL and TE-CCL with gains up to 5.12 × and 3.28 × , and shows better optimization-time scalability.
Fei Long, Ziyue Yang, Kaihui Gao et al.· Asia-Pacific Workshop on Net...· 0 citations
STORM is presented, a NIC-level scheduler for all types of RDMA workloads using NIC-only information: the known RDMA request size, and per-queue-pair backlog, and converts these signals into a small number of extra priority levels on the wire and prioritizes requests that are either near completion or blocking queued dependent work.
Jichun Wu, Ran Shu, Gianni Antichi et al.· Proceedings of the ACM SIGCO...· 0 citations