Automatic latency optimization for cpu-based dnn serving

Abstract

Systems and methods for optimizing thread allocation in a model serving system include estimating a batch size for inference requests. An optimal configuration is then determined that defines a number of inference instances, a number of threads per inference instance, and a sub-batch size per inference instance for processing a batch of inference requests of the batch size using intra-operator parallelism that minimizes average per-batch latency. The optimal configuration is determined with reference to a plurality of predetermined model profiles that define single-inference average batch latencies for different combinations of thread counts and batch sizes, the predetermined model profiles being used as input to a dynamic programming algorithm that identifies optimal configurations that minimize the average per-batch latency.