Only Relative Speed Matters: Virtual Causal Profiling
Only Relative Speed Matters: Virtual Causal Profiling
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只有相对速度才重要：虚拟因果分析
DOI：
10.1145/3453953.3453979
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Chandramowlishwaran, Aparna
中科院分区：
文献类型：
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作者：
Pourghassemi, Behnam;Amiri Sani, Ardalan;Chandramowlishwaran, Aparna
因果分析是一种新颖而强大的分析技术，它量化了优化代码段对程序运行时的潜在影响。因果分析的一个关键应用是分析假设情景，这通常需要大量的实验。此外，程序的执行高度依赖于底层机器资源，例如，CPU、网络、存储，因此一个设备上的分析结果不会直接转换到另一个设备上。这是一个主要的瓶颈，我们的能力，进行可扩展的性能分析，并大大限制了跨平台的软件development.In本文中，我们解决了上述挑战，利用因果分析的一个独特的属性：只有不同的资源的相对性能影响因果分析的结果，而不是他们的绝对性能。我们首先分析模型和证明因果分析，在开创性的论文中缺失的一块。然后，我们断言的必要条件，以实现虚拟的因果分析的辅助设备。在此基础上，我们设计了VCoz，一个虚拟的因果分析器，使分析应用程序的目标设备上使用的主机设备上的测量。我们通过调整多个硬件组件来实现VCoz的原型，以保持代码段的相对执行速度。我们对强调不同系统资源的基准测试的实验表明，VCoz可以在主机MacBook（x86架构）上生成Nexus 6P（基于ARM的设备）的因果分析报告，方差小于16%。
Causal profiling is a novel and powerful profiling technique that quantifies the potential impact of optimizing a code segment on the program runtime. A key application of causal profiling is to analyze what-if scenarios which typically require a large number of experiments. Besides, the execution of a program highly depends on the underlying machine resources, e.g., CPU, network, storage, so profiling results on one device does not translate directly to another. This is a major bottleneck in our ability to perform scalable performance analysis and greatly limits cross-platform software development.In this paper, we address the above challenges by leveraging a unique property of causal profiling: only relative performance of different resources affects the result of causal profiling, not their absolute performance. We first analytically model and prove causal profiling, the missing piece in the seminal paper. Then, we assert the necessary condition to achieve virtual causal profiling on a secondary device. Building upon the theory, we design VCoz, a virtual causal profiler that enables profiling applications on target devices using measurements on the host device. We implement a prototype of VCoz by tuning multiple hardware components to preserve the relative execution speeds of code segments. Our experiments on benchmarks that stress different system resources demonstrate that VCoz can generate causal profiling reports of Nexus 6P (an ARM-based device) on a host MacBook (x86 architecture) with less than 16% variance.