Content, progress and pedagogy of the module

Learning objectives

Knowledge

The students must have insight in:

• Must have knowledge about hardware and software platforms for scientific computing.
• Must have knowledge about the possible speedup by using parallelization (Amdahls law / Gustafson-Barsis’ law) under different conditions.
• Must have knowledge about message and data passing in distributed computing.
• Must have knowledge about programming techniques, profiling, benchmarking, code optimization etc.
• Must have knowledge about numerical accuracy in scientific computing problems.
• Must have knowledge about what typically characterizes problem-specific scientific computing software vs. general, user-oriented commercial software
• Must have knowledge about one or more software development methods of relevance to development of scientific computing software

Skills

The students must have understanding of:

• Must be able to translate the covered principles regarding scientific computing and software development to practice in the programming language(s) utilized in the course
• Must be able to implement software programs to solve scientific computational problems using parallel computing.
• Must be able to implement software programs to solve scientific computational problems using distributed computing units or high-performance specialized computing units (such as GPU)
• Must be able to debug, validate, optimize, benchmark and profile developed software modules.
• Must be able to assess the performance of different hardware architectures for scientific computing problems.

Competences

The students must be able to

• The student must be able to apply the proper terminology in oral and written communication and documentation within the scientific domains of numerical scientific computing
• Must be able to assess and weigh resources spent on software development against total subsequent computing time for concrete scientific computing problems.
• Must be able to reflect on different software development methods and independently select and combine elements thereof for use in concrete scientific computing problems.
• Must be able to independently adapt and apply the covered methods and principles for complex scientific computing problems within the students’ professional field

Type of instruction

Types of instruction are listed in §17; Structure and contents of the programme.

Exam

Exams