OptiNIC: Tail-Optimized RDMA for Distributed ML

This paper addresses the critical tail latency problem in distributed ML training, a significant bottleneck as workloads scale. OptiNIC offers a novel approach by relaxing traditional RDMA reliability guarantees, leveraging ML's tolerance for data loss. This domain-specific optimization, eliminating retransmissions and in-order delivery, promises substantial performance improvements in time-to-accuracy and throughput. The evaluation across public clouds validates the effectiveness of the proposed approach, making it a valuable contribution to the field.