Introduction to eScience and eEngineering

Credits: 5

Semester: 3

Course: Elective

Language of the course: English


Sergey Kovalchuk


Students will learn: methods and technologies, current trends and approaches, the current state of eScience and eEngineering; to organize a distributed computing infrastructure to support subject-oriented research and the life cycle of engineering products, to provide support for collaborative work within the framework of e-science projects, to organize work with distributed sources of large data within the framework of tasks of electronic science and electronic engineering.
Students will study distributed computing technologies, technologies and collaborative tools within the framework of computational experiments, large data processing technologies within the BigData paradigm, network technologies, intermediate software of electronic science and engineering.


Main topics of the discipline:
The main terminology of the course: eScience, eEngineering, Science 2.0. Problems solved by electronic science. Project classes. The main problems and challenges of the subject area.
Parallel and high-performance computing. Parallel performance models. Optimal parallelization. Acquisition of knowledge. Evaluation of effectiveness based on knowledge. Management of distributed computing infrastructure. Tools for describing high-level tasks.
BigData requirements in electronic science. Integration of data. Composite applications. Description of high-level tasks Interactive composite applications. Visualization of BigData.


Laboratory works.