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