SigmaTrace: Library-Agnostic Observability for CPU/GPU-Initiated RDMA Communication

Abstract

RDMA networking enables low-latency, high-bandwidth communication on large systems. Despite its adoption, however, observability is lacking. Existing tools target specific communication libraries or only report aggregated data, missing per-operation behavior. The gap is widest for GPU-initiated networking, whose in-kernel RDMA operations are invisible to the host. We present SigmaTrace, a library-agnostic RDMA profiling framework providing fine-grained, per-operation visibility for CPU and GPU-initiated networking. It traces operations at the libibverbs/mlx5dv layers, the common denominator beneath communication libraries on InfiniBand and RoCE, and employs a low-overhead memory interposition technique that, to our knowledge, gives the first observability into GPU-initiated RDMA without source patching or binary instrumentation. Host-side tracing adds 1.3% overhead on NCCL AllReduce at 128 GPUs and under 2% on RCCL at 1024 GPUs. On a persistent-kernel BFS and Mixture-of-Experts inference, SigmaTrace exposes an in-kernel barrier spread and 2.13x inter-node traffic skew invisible to existing profilers.

arXiv commentAccepted at SC26. Author's accepted manuscript. 13 pages, 10 figures, 1 table. Code: https://github.com/ParCoreLab/SigmaTrace