Description
Traces and profiles produced by HPC tools such as Score-P and CUBE are essential for performance analysis and optimization. However, these datasets are rarely shared in a reusable form because important contextual information is often missing. Details about the hardware, software environment, application configuration, and scheduler settings are required to interpret and reproduce an experiment. Within Task Area I3 of NFDIxCS, the German National Research Data Infrastructure for and with Computer Science, we are developing tools and services for the FAIR management of HPC performance data that address these challenges throughout the entire workflow, from executing an experiment to publishing its results.
Automated Metadata Collection
Metaper is an open-source Python command-line tool that automatically captures metadata on HPC systems. It collects information about job allocation, hardware, software, and the execution environment with minimal user interaction. Tool-specific plugins can enrich the collected metadata and add preprocessing results. A post-job finalization step incorporates scheduler accounting data that becomes available only after the job has completed. The resulting metadata is stored in a machine-readable format based on a standardized schema for representing HPC performance analyses.
Publication and Reuse
Perfortal is a web-based platform for publishing and sharing performance datasets. Metadata generated by Metaper can be imported to pre-fill submission forms, reducing manual effort while allowing users to add project-specific information and additional results. By using a shared metadata schema, both tools provide a consistent workflow.
Towards RDMC-Based Research Objects
We further discuss how this workflow can be integrated into the Research Data Management Container (RDMC) concept of NFDIxCS. RDMCs aim to combine data, metadata, software, and the execution environment into reusable research objects. Automated metadata collection and the publication workflow reduce the effort required to generate such containers and improve reproducibility across HPC systems and performance analysis tools.
Our approach aims to lower the barriers to sharing performance data, improve reproducibility, and support the reuse of performance experiments by providing standardized metadata and publication mechanisms. We invite discussion on metadata standards, the integration of performance analysis tools, and workflows for FAIR research data management.