Sampling-based binary-level cross-platform performance estimation
Xinnian Zheng, Haris Vikalo, Shuang Song, Lizy K. John, Andreas Gerstlauer · 2017
Fast and accurate performance estimation is a key challenge in modern system design. Recently, machine learning-based approaches have emerged that allow predicting the performance of an application on a target platform from executions on a different host. However, existing approaches rely on expensive instrumentation that requires source code to be available. We propose a novel sampling-based, binary-level cross-platform prediction method that accurately predicts performance of a workload on a target by relying on various performance statistics sampled on a host using built-in hardware counters. In our proposed framework, samples acquired from the host and target do not satisfy straightforward one-to-one correspondence that characterizes prior instrumentation-based approaches. The resulting alignment problem is NP-hard; to solve it efficiently, we develop a stochastic dynamic coupling (SDC) algorithm which, under mild assumptions, with high probability closely approximates optimal alignment. The prediction model constructed using SDC-aligned samples achieves on average 96.5% accuracy for 45 benchmarks at speeds of over 3 GIPS. At similar accuracies, this is up to 6× faster than instrumentation-based prediction, and approximately twice the speed of executing the same applications natively on our ARM target.