Advancing Computational Earth Sciences:Innovations and Challenges in Scientific HPC Workflows

The advancement of science is increasingly intertwined with complex computational processes [1]. Scientific workflows are at the heart of this evolution, acting as essential orchestrators for a vast range of experiments. Specifically, these workflows are central to the field of computational Earth Sciences, where they orchestrate a diverse range of activities, from cloud-based data preprocessing pipelines in environmental modeling to intricate multi-facility instrument-to-edge-to-HPC computational frameworks for seismic data analysis and geophysical simulations [2].

The emergence of continuum and cross-facility workflows marks a significant evolution in computational sciences [3]. Continuum workflows represent continuous computing access required for analysis pipelines, while cross-facility workflows extend across multiple sites, integrating experiments and
computing facilities. These cross-facility workflows, crucial for real-time applications, offer resiliency and stand as solutions for the demands of continuum workflows. Addressing continuum and cross-facility computing requires a focus on data, ensuring workflow systems are equipped to handle diverse data
representations and storage systems.

As we navigate the computing continuum, the pressing needs of contemporary scientific applications in Earth Sciences call for a dual approach: the recalibration of existing systems and the innovation of new workflow functionalities. This recalibration involves optimizing data-intensive operations and
incorporating advanced algorithms for spatial data analysis, while innovation may entail the integration of machine learning techniques for predictive modeling and real-time data processing in earth sciences. We offer a comprehensive overview of cutting-edge advancements in this dynamic realm, with a focus on computational Earth Sciences, including managing the increasing volume and complexity of geospatial data, ensuring the reproducibility of large-scale simulations, and adapting workflows to leverage emerging computational architectures.

 

[1] Ferreira da Silva, R., Casanova, H., Chard, K., Altintas, I., Badia, R. M., Balis, B., Coleman, T., Coppens, F., Di Natale, F., Enders, B., Fahringer, T., Filgueira, R., et al. (2021). A Community Roadmap for Scientific Workflows Research and Development. 2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS), 81–90. DOI: 10.1109/WORKS54523.2021.00016

[2] Badia Sala, R. M., Ayguadé Parra, E., & Labarta Mancho, J. J. (2017). Workflows for science: A challenge when facing the convergence of HPC and big data. Supercomputing frontiers and innovations, 4(1), 27-47. DOI: 10.14529/jsfi170102

[3] Antypas, K. B., Bard, D. J., Blaschke, J. P., Canon, R. S., Enders, B., Shankar, M. A., ... & Wilkinson, S. R. (2021, December). Enabling discovery data science through cross-facility workflows. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 3671-3680). IEEE. DOI: 10.1109/BigData52589.2021.9671421