Modeling virtual machines misprediction overhead

Abstract

Virtual machines are versatile systems that can support innovative solutions to many problems. These systems usually rely on emulation techniques, such as interpretation and dynamic binary translation, to execute guest application code. Usually, in order to select the best emulation technique for each code segment, the system must predict whether the code is worth compiling (frequently executed) or not, known as hotness prediction. In this paper we show that the threshold-based hot code predictor, frequently mispredicts the code hotness and as a result the VM emulation performance become dominated by miscompilations. To do so, we developed a mathematical model to simulate the behavior of such predictor and using it we quantify and characterize the impact of mispredictions in several benchmarks. We also show how the threshold choice can affect the predictor, what are the major overhead components and how using SPEC to analyze a VM performance can lead to misleading results.