Nezha: Breaking Multi-Rail Network Barriers for Distributed DNN Training

TL;DR

Nezha is a protocol-agnostic system that optimizes multi-rail network communication for distributed deep learning, significantly improving throughput and efficiency on legacy hardware without requiring hardware upgrades.

Contribution

Nezha introduces a unified, protocol-agnostic framework with dynamic load balancing and fault tolerance for multi-rail networks in distributed DNN training.

Findings

Achieves 74-80% higher throughput than MPTCP.

Delivers 2.36x higher training efficiency than Gloo on large clusters.

Reduces latency by 1.7 to 4.3 times compared to Gloo.

Abstract

In distributed deep learning, communication remains a critical bottleneck. While modern hardware advances rapidly, over 60 percent of production HPC systems still rely on legacy infrastructure (V100 GPUs, multi-plane Ethernet/InfiniBand), necessitating communication optimization without hardware upgrades. Existing approaches face three key limitations: (1) static single-rail binding underutilizes multi-rail bandwidth, (2) protocol heterogeneity (TCP-RDMA coexistence) causes synchronization delays, and (3) mainstream libraries (NCCL/MPI) lack cross-protocol coordination. We present Nezha, the first protocol-agnostic system for multi-rail networks. Our contributions include: (1) Hardware-agnostic cross-protocol coordination: A unified abstraction enabling seamless collaboration between in-network computing (SHARP), adaptive RDMA (GLEX), and TCP, achieving 1.7 to 4.3 times lower latency than Gloo. (2) Protocol-aware dynamic load balancing: A hybrid scheduling strategy with cold/hot start state machine for heterogeneous protocols, reducing startup latency for small payloads while enhancing throughput for large transfers. (3) Fault-tolerant multi-rail collaboration: A self-recovery mechanism that reroutes data flows within 200 milliseconds upon single-rail failures, ensuring uninterrupted training. Experiments on 8-node clusters demonstrate Nezha achieves 74 percent and 80 percent higher throughput than MPTCP in homogeneous (TCP-TCP) and heterogeneous (TCP-SHARP) networks, respectively. On 128-node supercomputers, Nezha delivers 2.36 times higher training efficiency than Gloo. By bridging modern DNN communication demands with legacy infrastructure, Nezha proves that systematic multi-rail optimization can unlock the potential of aging clusters.