Flexible Data Aggregation for Performance Profiling

Almost all performance analysis tools in the HPC space perform some form of aggregation to compute summary information of a series of performance measurements, from summations to more complex operations like histograms. Aggregation not only reduces data volumes and consequently storage space requirements and overheads, but is also crucial to extract insights from recorded measurement data. In current tools, however, most aspects that control the aggregation, such as the data dimensions to be reduced, are hard-coded in the tool for a set of particular use cases identified by the tool developer and cannot be extended or modified by the user. This limits their flexibility and often results in users having to learn and use multiple tools with different aggregation options for their performance analysis needs.We present a novel approach for performance data aggregation based on a flexible key:value data model with user-defined attributes, where users can define custom aggregation schemes in a simple description language. This not only gives users the control to deploy the particular data aggregation they need, but also opens the door for aggregations along application-specific data dimensions that cannot be achieved with traditional profiling tools. We show how our approach can be applied for performance profiling at runtime, cross-process data aggregation, and interactive data analysis and demonstrate its functionality with several case studies driven by real world codes.