In-situ parallel workflows couple multiple component applications, such as simulation and analysis, via streaming data transfer. in order to avoid data exchange via shared file systems. Such workflows are challenging to configure for optimal performance due to the large space of possible configurations. Expert experience is rarely sufficient to identify optimal configurations, and existing empirical auto-tuning approaches are inefficient due to the high cost of obtaining training data for machine learning models. It is also infeasible to optimize individual components independently, due to component interactions. We propose here a new auto-tuning method, Component-based Ensemble Active Learning (CEAL), that combines machine learning techniques with knowledge of in-situ workflow structure to enable automated workflow configuration with a limited number of performance measurements.

中文翻译：

---

通过组合组件应用程序的性能模型进行原位工作流自动调整

原位并行工作流通过流数据传输耦合多个组件应用程序，例如模拟和分析。以避免通过共享文件系统进行数据交换。由于可能的配置空间很大，这样的工作流程很难配置以获得最佳性能。专家经验很少足以识别最佳配置，并且由于获取机器学习模型的训练数据成本高，现有的经验自动调整方法效率低下。由于组件交互，独立优化单个组件也是不可行的。我们在这里提出了一种新的自动调整方法，基于组件的集成主动学习（CEAL），