Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability

TL;DR

This paper introduces MIDA, a lightweight runtime approach for multi-workflow data integration in scientific discovery, leveraging data observability and provenance to enable efficient, cross-facility analysis with minimal overhead.

Contribution

MIDA provides a novel, adaptable framework for real-time multi-workflow data integration using data observability without instrumentation, suitable for heterogeneous scientific environments.

Findings

Successfully integrated data from Dask and MLFlow in a distributed deep learning case study.

Achieved near-zero overhead for up to 100,000 tasks on 1,680 CPU cores.

Demonstrated scalability on the Summit supercomputer with up to 276 GPUs.

Abstract

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.