PLATFORM: Parallel Linear Algebra Tool FOr Reduced Modeling - Nicholas Arnold-Medabalimi; Christopher R. Wentland; Cheng Huang; Karthik Duraisamy (SoftwareX, 2023)
With advances in the scope of computational modeling methodologies, an increased focus is being placed on the application of data-driven techniques to increasingly complex problems. Due to the associated scale of the application, processing large datasets has emerged as a development bottleneck in practical applications of data-driven methods. While large-scale partial differential equation solvers are optimized for sparse linear algebra, many data-decomposition techniques (e.g. the singular value decomposition) require dense linear algebra operations. This work presents the tool PLATFORM which has enabled the application of modal decomposition and data-driven reduced-order modeling techniques for moderate (giga-) and large (tera-) scale data processing. The I/O and computing strategies and priorities are described. Most importantly, this framework uses abstraction techniques which allow users with limited understanding of distributed linear algebra computations and I/O to flexibly prototype and test methods on memory-intensive problems that are demanding in memory for the scripting environment.