Flow-sensitive runtime estimation: an enhanced hot spot detection heuristics for embedded Java just-in-time compilers

摘要

Java just-in-time compilers often compile only hot methods because the compilation overhead is a part of the running time. This requires precise and efficient hot spot detection, which includes distinguishing hot methods from cold ones, detecting them as early as possible, and paying a small detection overhead. Hot spot detection is especially important in embedded applications because they show more of a start-up phase behavior of a regular application where methods are not executed heavily, so the hot methods are not definite. Because a long-running method is likely to be a hot method, we can detect a hot method by measuring its running time during interpretation. However, precise measurement of the running time during execution is too expensive, especially in embedded systems, so many counter-based heuristics have been proposed to estimate it such as Oracle's HotSpot heuristic. One problem is that although the overhead of these heuristics is low, they do not estimate the running time precisely, which may lead to imprecise hot spot detection.This paper proposes a new hot spot detection heuristic called flow-sensitive runtime estimation, which can estimate the running time more precisely than others with a relatively low overhead. It only counts important bytecode instructions dynamically, but it can obtain the precise count of all interpreted bytecode instructions with a simple arithmetic calculation. We also propose a static analysis technique to predict those hot methods which spends a huge execution time once invoked, so as to compile them at their first invocation. Our experimental results show that these techniques can improve the performance by as much as an average of 7.4% compared with the HotSpot heuristic for the benchmarks when they run once, which is often regarded as showing the start-up phase behavior. Even for real embedded Java applications such as the digital TV Java Xlet applications, our techniques can improve the user response time by an average of 7.1%. Copyright (c) 2015 John Wiley & Sons, Ltd.