Content, progress and pedagogy of the module

Learning objectives

Knowledge

• 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

• 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 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

As described in § 17.

Extent and expected workload

137,5 hours

Exam

Exams