R2CC: Reliable and Resilient Collective Communication for Distributed Learning
Wei Wang, Nengneng Yu, Sixian Xiong, Zaoxing Liu
Modern ML training and inference now span hundreds to tens of thousands of GPUs, where failures can waste 10-15% of GPU hours due to slow recovery. Common network errors and link fluctuations can cause up to 50% of job interruptions, triggering timeouts that often terminate entire jobs, forcing expensive checkpoint rollback during training and request reprocessing during inference. We present R2CC, a fault-tolerant communication library that provides lossless, low-overhead failover by exploiting multi-NIC hardware. R2CC performs rapid connection migration, bandwidth-aware load redistribution, and resilient collective algorithms to maintain progress under failures. We evaluate R2CC on four 8-GPU H100 InfiniBand servers and via large-scale ML simulators modeling up to 1024 GPUs with diverse failure patterns. Experiments show that R2CC is highly robust to inter-node network failures, incurring less than 1.1% training and 3% inference overhead under active failure scenarios. Compared to existing fault-tolerant approaches, R2CC reduces failure-induced overhead by up to 92% for training and 98% for inference. R2CC is open-sourced at https://github.com/r2cc-project/R2CC.
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