New Performance Modeling Methods for Parallel Data Processing Applications
New Performance Modeling Methods for Parallel Data Processing Applications
复制标题
并行数据处理应用程序的新性能建模方法
DOI：
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复制
发表时间：
2019
影响因子：
0.9
通讯作者：
Zhengyu Yang
中科院分区：
文献类型：
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作者：
J. Bhimani;N. Mi;M. Leeser;Zhengyu Yang
预测在并行计算平台上运行的应用程序的性能变得越来越重要，因为它对开发时间和资源管理有影响。然而，对于迭代和多阶段应用程序来说，预测并行进程的性能很复杂。本研究提出了一种性能近似方法 FiM，用于预测分布式框架上运行的应用程序的 FiM-Cal 计算时间和 FiM-Com 通信时间。 FiM-Cal 由两个相互耦合的关键组件组成：(1) 随机马尔可夫模型，用于捕获通常依赖于并行资源（例如进程数量）的非确定性运行时间；(2) 机器学习模型，当应用程序参数（例如数据集）发生变化时，该模型可推断用于校准马尔可夫模型的参数。除了并行计算时间之外，并行计算平台还消耗一些数据传输时间来在不同节点之间进行通信。 FiM-Com 包含一个模拟排队模型，可快速估计通信时间。我们的新建模方法考虑了多个维度的不同设计选择，即（i）进程级并行性，（ii）多处理器平台上的核心分布，（iii）应用程序相关参数，以及（iv）数据集的特征。我们的预测方法的主要贡献在于，FiM 可以为比训练数据集大得多的数据集提供并行处理时间的准确预测。我们使用 NAS 并行基准和真实的迭代数据处理应用程序来评估我们的方法。我们将预测结果（例如，端到端执行时间）与真实分布式平台上的实际实验测量结果进行比较。我们还将我们的工作与基于机器学习的现有预测技术进行比较。我们根据 FiM 的实际结果和预测结果对进程数进行排名，并计算实际排名和预测排名之间的相关性。我们的结果表明 FiM 获得了 0.80 至 0.99 范围内的高相关性，这表明我们的技术具有相当高的准确性。这种预测为数据分析师提供了有关并行资源（例如进程数量和内核数量）最佳配置的有用见解，并帮助系统设计人员研究应用程序参数变化对系统性能的影响。
Predicting the performance of an application running on parallel computing platforms is increasingly becoming important because of its influence on development time and resource management. However, predicting the performance with respect to parallel processes is complex for iterative and multi-stage applications. This research proposes a performance approximation approach FiM to predict the calculation time with FiM-Cal and communication time with FiM-Com of an application running on a distributed framework. FiM-Cal consists of two key components that are coupled with each other: (1) a Stochastic Markov Model to capture non-deterministic runtime that often depends on parallel resources, e.g., number of processes, and (2) a machine-learning model that extrapolates the parameters for calibrating our Markov model when we have changes in application parameters such as dataset. Along with the parallel calculation time, parallel computing platforms consume some data transfer time to communicate among different nodes. FiM-Com consists of a simulation queuing model to quickly estimate communication time. Our new modeling approach considers different design choices along multiple dimensions, namely (i) process-level parallelism, (ii) distribution of cores on multi-processor platform, (iii) application related parameters, and (iv) characteristics of datasets. The major contribution of our prediction approach is that FiM can provide an accurate prediction of parallel processing time for the datasets that have a much larger size than that of the training datasets. We evaluate our approach with NAS Parallel Benchmarks and real iterative data processing applications. We compare the predicted results (e.g., end-to-end execution time) with actual experimental measurements on a real distributed platform. We also compare our work with an existing prediction technique based on machine learning. We rank the number of processes according to the actual and predicted results from FiM and calculate the correlation between the actual and predicted rankings. Our results show that FiM obtains a high correlation in the range of 0.80 to 0.99, which indicates considerable accuracy of our technique. Such prediction provides data analysts a useful insight of optimal configuration of parallel resources (e.g., number of processes and number of cores) and also helps system designers to investigate the impact of changes in application parameters on system performance.