PReCCL: Performant and Resilient Collective Communication via Integrated Inband Telemetry and Workload Reallocation
TL;DR
PReCCL is a drop-in NCCL replacement that combines software inband telemetry with cross-VT workload reallocation, and implements in-band monitoring within the CCL, and precisely measures the stall counts of each VT, and piggybacks the telemetry meta-data on existing collective traffic.
Abstract
Modern collective communication libraries (CCLs) execute a collective communication task (CCT) by decomposing it into multiple sub-tasks, each mapped to a specific Virtual Topology (VT), which is an ordered graph of GPUs (e.g., a ring or a tree), to maximize parallelism and link utilization. As AI training scales to larger clusters, network anomalies (congestion and failures) are unavoidable, and a single straggling VT can delay the entire CCT. Existing solutions either rely on low-level transport-layer solutions which lacks a cross-sub-task perspective, or static CCL scheduling, failing to adapt to the dynamic and heterogeneous networks. We present PReCCL, a drop-in NCCL replacement that combines software inband telemetry with cross-VT workload reallocation. Unlike hardware-dependent telemetry (e.g., P4-INT), PReCCL implements in-band monitoring within the CCL. It precisely measures the stall counts of each VT, and piggybacks the telemetry meta-data on existing collective traffic. With this fine-grained visibility, PReCCL employs a dynamic cross-VT reallocation mechanism that shifts workloads from straggler VTs (suffering from congestion or faults) to healthy ones. Reallocation occurs only at CCT boundaries using an epoch-based deterministic protocol, avoiding cross-rank consistency races. We implement PReCCL in NCCL and evaluate it on a 32-GPU testbed, large-scale simulation, and a 1024-GPU production cluster. On the testbed, PReCCL improves CCT completion by up to 2.1X and end-to-end training by 1.21X, with low overhead above tuned message-size thresholds.