Sampling-Based Binary-Level Cross-Platform Performance Estimation

Xinnian Zhenga, Haris Vikalob, Shuang Songc, Lizy K. Johnd and Andreas Gerstlauere
The University of Texas at Austin, TX, USA.


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, binarylevel 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 38 215; faster than instrumentationbased prediction, and approximately twice the speed of executing the same applications natively on our ARM target.

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