PairSight: Characterizing and Predicting Operator-Level Interference in GPU Sharing for DNN Inference

Abstract

Serving DNN inference is costly, yet a single task seldom saturates both the compute units and the memory bandwidth of a GPU. GPU sharing raises utilization by co-locating several tasks on one GPU, at the cost of interference among them. Existing systems estimate this interference with binary compute-memory rules, standalone scaling curves, or model-level predictors. However, these estimates rest on a limited understanding of interference. They leave the software and hardware properties of co-located operators largely unexplored and give no slowdown prediction that is both interpretable and accurate. We conduct an operator-level study of interference under SM partitioning and use hardware counters to trace it to contention on the shared memory path, where the slowdown depends jointly on both co-located operators. Based on these findings, we propose PairSight, a hardware-aware interference predictor that learns how the hardware behaviors of co-located tasks interact and predicts the latency of each task before co-location. On six DNN models including LLMs, PairSight predicts latency under sharing with 4.3% mean error, against 19.0% for a compute-memory intensity rule, and adapts to unseen models and inputs with a few samples.