iBench: a Distributed Inference Simulation and Benchmark Suite

Abstract

We present a novel distributed inference benchmarking system, called “iBench”, that provides relevant performance metrics for high-performance edge computing systems using trained deep learning models. The proposed benchmark is unique in that it includes data transfer performance through a distributed system, such as a supercomputer, using clients and servers to provide a system-level benchmark. iBench is flexible and robust enough to allow for the benchmarking of custom-built inference servers. This was demonstrated through the development of a custom Flask-based inference server to serve MLPerf's official ResNet50v1.5 model. In this paper, we compare iBench against MLPerf inference performance on an 8-V100 GPU node. iBench is shown to provide two primary advantages over MLPerf: (1) the ability to measure distributed inference performance, and (2) a more realistic measure of benchmark performance for inference servers on HPC by taking into account additional factors to inference time, such as HTTP request-response time, payload pre-processing and packing time, and invest time.