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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >VarCatcher: A Framework for Tackling Performance Variability of Parallel Workloads on Multi-Core
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VarCatcher: A Framework for Tackling Performance Variability of Parallel Workloads on Multi-Core

机译：VarCatcher：处理多核并行工作负载性能差异的框架

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摘要

The non-deterministic nature of multi-threaded workloads running on multi-core platforms often leads to notable performance variability from run to run. Such variability makes experimental results prone to misinterpretations or misguided claims. To deal with such variability, statistical inference methods are usually used to summarize the experimental results with certain confidence levels by running the experiments or measurements a large number of times. However, such statistical results are often too vague or too simplistic. They are not sufficient to help users understand the causes of such variability, and allow more in-depth analysis on the results or reproduce the results for validation during design space exploration. To allow better analyzability and reproducibility, we propose a framework to tackle such variability, called VarCatcher. The key to VarCatcher is to characterize a parallel execution using Parallel Characteristics Vector (PCV). A clustering-based approach is then used to group runs with similar execution characteristics that can later be used to analyze results in-depth, to customize different evaluation strategies, reproduce the result for variability, to determine the impact of features, or to assist performance diagnosis. We have built a prototype of VarCatcher that includes a user-level toolset for runtime monitoring and measurements using the Intel Processor Trace feature on commodity Intel processors as well as an architecture extension with very low runtime overheads (around 3 and 0.01 percent accordingly). Several case studies confirm that VarCatcher enables several appealing features such as in-depth result analysis, customized evaluation strategies, and reproducibility.
机译：在多核平台上运行的多线程工作负载的不确定性通常会导致不同运行之间的显着性能差异。这种可变性使实验结果易于产生误解或错误的主张。为了应对这种可变性，通常使用统计推断方法通过多次运行实验或测量结果，以一定的置信度来总结实验结果。但是，此类统计结果通常过于模糊或过于简单。它们不足以帮助用户理解这种可变性的原因，并不能对结果进行更深入的分析，也不能在设计空间探索期间对结果进行再现以进行验证。为了实现更好的分析性和可重复性，我们提出了一个解决这种可变性的框架，称为VarCatcher。 VarCatcher的关键是使用并行特征向量（PCV）表征并行执行。然后使用基于聚类的方法对具有相似执行特征的运行进行分组，随后可以将其用于深入分析结果，定制不同的评估策略，再现结果的可变性，确定功能的影响或辅助性能诊断。我们已经构建了VarCatcher的原型，该原型包括用于在商用Intel处理器上使用Intel Processor Trace功能进行运行时监视和测量的用户级工具集，以及具有非常低的运行时开销（分别约为3％和0.01％）的体系结构扩展。若干案例研究证实，VarCatcher具有多种吸引人的功能，例如深入的结果分析，定制的评估策略和可重复性。

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