# SDR-RDMA: Software-Defined Reliability Architecture for Planetary Scale RDMA Communication

arXiv: https://arxiv.org/abs/2505.05366 · HTML: https://mlsanity.com/paper/2505.05366/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2025.

## Built on by (most cited first, 1 of 1)

- [ScaleAcross Explorer: Exploring Communication Optimization for Scale-Across AI Model Training](https://mlsanity.com/paper/2605.24326/) (2026)

## Abstract (arXiv)

RDMA is vital for efficient distributed training across datacenters, but millisecond-scale latencies complicate the design of its reliability layer. We show that depending on long-haul link characteristics, such as drop rate, distance and bandwidth, the widely used Selective Repeat algorithm can be inefficient, warranting alternatives like Erasure Coding. To enable such alternatives on existing hardware, we propose SDR-RDMA, a software-defined reliability stack for RDMA. Its core is a lightweight SDR SDK that extends standard point-to-point RDMA semantics -- fundamental to AI networking stacks -- with a receive buffer bitmap. SDR bitmap enables partial message completion to let applications implement custom reliability schemes tailored to specific deployments, while preserving zero-copy RDMA benefits. By offloading the SDR backend to NVIDIA's Data Path Accelerator (DPA), we achieve line-rate performance, enabling efficient inter-datacenter communication and advancing reliability innovation for inter-datacenter training.