虚拟机预热时冷时热
Virtual Machine Warmup Blows Hot and Cold
摘要：
传统上，带有即时（JIT）编译器的虚拟机（VM）被认为分两个阶段执行程序：初始预热阶段确定程序的哪些部分将最受益于动态编译，然后JIT将这些部分编译为机器码;随后程序被称为处于峰值性能的稳定状态。测量方法几乎总是丢弃在预热阶段收集的数据，因此报告的测量结果完全集中在峰值性能上。我们引入了一个完全自动化的统计方法，基于变点分析，这使我们能够确定一个程序是否已经达到稳定状态，如果是这样，是否代表峰值性能或没有。使用这一点，我们表明，即使在最受控制的情况下运行，小的，确定性的，广泛研究的微基准往往无法达到稳定状态的峰值性能在各种常见的虚拟机。在3台不同的机器上重复我们的实验，我们发现最多有43.5%的<VM，benchmark>对始终达到峰值性能的稳定状态。
英文摘要：
Virtual Machines (VMs) with Just-In-Time (JIT) compilers are traditionally thought to execute programs in two phases: the initial warmup phase determines which parts of a program would most benefit from dynamic compilation, before JIT compiling those parts into machine code; subsequently the program is said to be at a steady state of peak performance. Measurement methodologies almost always discard data collected during the warmup phase such that reported measurements focus entirely on peak performance. We introduce a fully automated statistical approach, based on changepoint analysis, which allows us to determine if a program has reached a steady state and, if so, whether that represents peak performance or not. Using this, we show that even when run in the most controlled of circumstances, small, deterministic, widely studied microbenchmarks often fail to reach a steady state of peak performance on a variety of common VMs. Repeating our experiment on 3 different machines, we found that at most 43.5% of <VM, benchmark> pairs consistently reach a steady state of peak performance.
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