ExtraPeak - Automatic Performance Modeling of HPC Applications with Multiple Model Parameters

日益复杂的应用程序与快速增长的数据量相结合，对计算能力产生了无法满足的需求。然而，运行它们所需的超级计算机的运营和采购成本是巨大的。因此，最小化代码的运行时间和能耗是经济上的当务之急。调整复杂的HPC应用程序需要巧妙地探索其设计和配置空间。然而，特别是在超级计算机上，这个空间是如此之大，以至于通过性能实验对其进行穷举遍历太昂贵了，如果不是不可能的话。性能模型描述了性能指标，如执行时间作为参数的函数，如核心数量或方程中输入问题的大小，允许更有效地探索这个空间。不幸的是，如果是为大型的实际应用程序手动创建性能模型，则会非常费力。此外，为了确保应用程序没有性能错误，分析任何单个方面（如处理器数量或问题大小）通常是不够的。一个变化的参数对性能的影响不仅必须在真空中理解，而且还必须在其他相关参数的变化的背景下理解，包括算法选项、输入特性或调谐参数（例如平铺）。基于一组有限的性能实验的性能模型的生成，渴望弥合这一差距。然而，虽然具有一个参数的模型可以很容易地处理，但是具有多个参数的性能建模提出了重大挑战，即（i）性能相关参数的识别，（ii）所需性能实验的资源感知设计，（iii）可能的模型函数的多样性，以及（iv）复杂高维模型搜索空间的有效遍历。我们将开发一种自动经验方法，该方法允许对应用程序执行参数的任何组合进行性能建模。解决上述挑战的解决方案组件包括事先的源代码分析和反馈引导的性能数据采集和模型生成过程。我们的目标是有见地的性能模型，使广泛的用途，从性能预测平衡的机器设计，以详细的性能调整。我们的方法将帮助应用程序开发人员理解复杂的性能权衡，并最终提高代码的性能。我们将把我们的方法集成到开源性能建模工具Extra-P中，该工具目前仅限于只有一个参数的模型。最后，作为我们方法的一个具体应用，我们将设计一种新的协同设计方法，该方法将取代容易出错且耗时的信封背面计算，以定义未来机器采购的要求。

Applications of ever-increasing complexity combined with rapidly growing volumes of data create an insatiable demand for computing power. However, the operational and procurement costs of the supercomputers needed to run them are tremendous. Minimizing runtime and energy consumption of a code is therefore an economic imperative. Tuning complex HPC applications requires the clever exploration of their design and configuration space. Especially on supercomputers, however, this space is so large that its exhaustive traversal via performance experiments is too expensive, if not impossible. Performance models, which describe performance metrics such as the execution time as a function of parameters such as the number of cores or the size of the input problem in an equation, allow this space to be explored more efficiently. Unfortunately, creating performance models manually is extremely laborious if done for large real-world applications. Further, to ensure that applications are free of performance bugs, it is often not enough to analyze any single aspect, such as processor count or problem size. The effect that the one varying parameter has on performance must be understood not only in a vacuum, but also in the context of the variation of other relevant parameters, including algorithmic options, input characteristics, or tuning parameters such as tiling.Recent advances in automatic empirical performance modeling, i.e., the generation of performance models based on a limited set of performance experiments, aspire to bridge this gap. However, while models with one parameter can be handled quite easily, performance modeling with multiple parameters poses significant challenges, namely (i) the identification of performance-relevant parameters, (ii) the resource-aware design of the required performance experiments, (iii) the diversity of possible model functions, and (iv) the efficient traversal of a complex high-dimensional model search space.In this project, we will develop an automatic empirical approach that allows performance modeling of any combination of application execution parameters. Solution components to tackle the above challenges include prior source-code analysis and a feedback-guided process of performance-data acquisition and model generation. The goal are insightful performance models that enable a wide range of uses from performance predictions for balanced machine design to detailed performance tuning. Our approach will help application developers understand complex performance tradeoffs and ultimately improve the performance of their code. We will integrate our method into the open-source performance-modeling tool Extra-P, which is currently restricted to models with only a single parameter. Finally, as a specific application of our approach, we will devise a novel co-design method that will replace error-prone and time-consuming back-of-the-envelope calculations to define the requirements for future machine procurements.