Recommended prerequisite for participation in the module

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Must have knowledge of computer architecture classification (Flynn’s taxonomy).
• Must have knowledge about typical scientific computing problems with non-real-time constraints.
• Must have knowledge of parallel computing techniques.
• Must have knowledge of the relation between physical world problems and mathematical models.
• Must have knowledge of different computational platforms for different types of scientific computing problems.

Skills

• Must be able to select suitable hardware platforms for different computational problems.
• Must be able to program solutions for scientific computing problems by use of various computational platforms (single and multi-core processing units, graphics processing units, compute clusters etc.).
• Must be able to debug and performance optimize (e.g., time and/or memory consumption) the developed software.
• Must be able to use various computing platforms to solve different scale computational problems.
• Document the developed software including validation of the desired functionality.

Competences

• Must be able to to solve problems where scientific computing is applied.
• Using the above mentioned knowledge and skills, the student must be able to identify, prioritize, and apply in a structured manner the set of tasks needed for solving a scientific computing problem, which in its solution naturally involves or require high-performance simulation capabilities.
• The student must be able to create and plan the work and development processes as needed for solving systematically such a problem.
• The student must be able to select the most appropriate project management method(s) and tool(s) for solving the problem.
• Must be able to initiate the above mentioned task independently, critically, and responsibly.

Type of instruction

As described in the introduction to Chapter 3.

Exam

Exams