Programming for the near future: Concepts and pragmatic considerations

Abstract

This article deals with the concept, architecture, and scientific-organizational problems of creating a new generation of integrated software intended for predictive modeling in engineering, energy, materials science, biology, medicine, economics, nature management, ecology, sociology, etc. Mathematical formulations include interdisciplinary direct and inverse extremely resource-intensive tasks, which are solved using computational methods and technologies of scalable parallelization by hybrid programming on heterogeneous supercomputers with distributed and hierarchical shared memory. The project concept includes the development of an instrumental computational environment that supports all stages of a large-scale machine experiment: geometric and functional modeling, generating of adaptive unstructured grids of various types and orders, approximation of initial equations, solution of emerging algebraic problems, postprocessing of the obtained results, optimization methods for inverse tasks, and machine learning and decision-making on the results of calculations. The effective functionality of the instrumented computing environment is based on high-performance computing and intelligent big data tools. The architecture of the instrumental computational environment provides for automated expansion of the composition of implemented models and applied algorithms, adaptation to the evolution of supercomputer platforms, user-friendly interfaces and active reuse of external software products, and coordinated participation of different groups of developers, which together should provide a long life cycle and demand for the created ecosystem by a wide range of users from different professional fields.