Towards Lightweight Data Integration using Multi-workflow Provenance and
  Data Observability
Renan Souza Tyler J. Skluzacek Sean R. Wilkinson Maxim Ziatdinov Rafael Ferreira da Silva
Modern large-scale scientific discovery requires multidisciplinary
collaboration across diverse computing facilities, including High Performance
Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data
analysis plays a crucial role in scientific discovery, especially in the
current AI era, by enabling Responsible AI development, FAIR, Reproducibility,
and User Steering. However, the heterogeneous nature of science poses
challenges such as dealing with multiple supporting tools, cross-facility
environments, and efficient HPC execution. Building on data observability,
adapter system design, and provenance, we propose MIDA: an approach for
lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data
observability strategies and adaptability methods for various parallel systems
and machine learning tools. With observability, it intercepts the dataflows in
the background without requiring instrumentation while integrating domain,
provenance, and telemetry data at runtime into a unified database ready for
user steering queries. We conduct experiments showing end-to-end multi-workflow
analysis integrating data from Dask and MLFlow in a real distributed deep
learning use case for materials science that runs on multiple environments with
up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000
tasks on 1,680 CPU cores on the Summit supercomputer.