An elastic framework for ensemble-based large-scale data assimilation

TL;DR

This paper introduces Melissa-DA, an elastic, scalable, and fault-tolerant framework for large-scale ensemble data assimilation that efficiently manages computational resources and avoids I/O bottlenecks.

Contribution

It presents a novel modular framework enabling elastic resource management and online processing for ensemble-based data assimilation at large scales.

Findings

Supports up to 16,240 cores for 16,384 ensemble members

Achieves efficient load balancing and scalability

Avoids I/O bottlenecks with online data processing

Abstract

Prediction of chaotic systems relies on a floating fusion of sensor data (observations) with a numerical model to decide on a good system trajectory and to compensate nonlinear feedback effects. Ensemble-based data assimilation (DA) is a major method for this concern depending on propagating an ensemble of perturbed model realizations.In this paper we develop an elastic, online, fault-tolerant and modular framework called Melissa-DA for large-scale ensemble-based DA. Melissa-DA allows elastic addition or removal of compute resources for state propagation at runtime. Dynamic load balancing based on list scheduling ensuresefficient execution. Online processing of the data produced by ensemble members enables to avoid the I/O bottleneck of file-based approaches. Our implementation embeds the PDAF parallel DA engine, enabling the use of various DA methods. Melissa-DA can support extra ensemble-based DAmethods by implementing the transformation of member background states into analysis states. Experiments confirm the excellent scalability of Melissa-DA, running on up to 16,240 cores, to propagate 16,384 members for a regional hydrological critical zone assimilation relying on theParFlow model on a domain with about 4 M grid cells.