Irregular workloads, such as those stemming from Graph Neural Networks (GNNs) and Mixture-of-Experts (MoE) models, frequently induce severe incast congestion in large-scale parallel environments. Existing fabric-level congestion control mechanisms rely on reactive, link-level rate limiting that degrades overall throughput. While receiver-driven, credit-based flow control offers a proactive alternative, as process counts scale memory slots per rank fall below usable thresholds, rendering traditional rank-based accounting untenable. To address this challenge, we introduce MailRail, an out-of-order, asynchronous messaging framework, along with a novel user-space, node-based credit flow control system. By shifting credit accounting from individual processes to shared per-node pools, our proactive, fabric-agnostic approach maintains sufficient credit availability even as process density grows, enabling processes to dynamically utilize freed credits without waiting for receiver-side redistribution. Experimental evaluations demonstrate that node-based flow control achieves up to a 1132x speedup over unconstrained execution at an adversarial incast ratio of 4096:1 while adding minimal overhead on uniform workloads.

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