After successful completion of the course, students are able to

• Understand and express asymptotic running time and work of parallel algorithms
• Understand parallel algorithm using the PRAM model with respect to running time and work
• Understand and appreciate characteristics of thread models for parallel computing
• Read and write programs in OpenMP
• Read and write programs in MPI
• Understand and appreciate task parallel models for parallel computing

Motivation, goals of parallel computing. Parallel algorithms, architectures, programming models, performance measurement and analysis. Problems in parallel algorithms. Introduction to MPI (Message-Passing interface), hreads and OpenMP. Task-parallel models and interfaces (Cilk). Other languages for multi-core processors.

Lectures, own study, home exercises, programming project.

For current plan, see course Homepage.

Literature:

• Rauber, Rünger: Parallel programming. Second Edition, Springer 2013.
• Schmidt, Gonzalez-Dominguez, Hundt, Schlarb: Parallel Programming. Concepts and Practice. Morgan Kaufmann 2018.

Additional literature will be announced. Course material (slides) should suffice for the programming projects.

ECTS Breakdown:

• Lectures: 1,5 ECTS
• Study: 1,5 ECTS
• Project work (implementations, test, benchmarking): 3 ECTS

• Lectures 13x2h = 26h
• Exercises plenary 3x2h = 6h
• Self-study  30h
• Written exam 10+2h = 12h
• Home exercises 3x8h = 24h
• Projects 2x26h = 52h

 Total: 150h = 6 ECTS

Exercises, projects, written exam(s). Planned are 3 exercises with both theoretical and practical parts. Breakdown for final grade will be announced.

Knowledge of programming languages, computer architectures, operating systems. Basic Algorithms and Datastructures (asymptotic worst-case analysis). Programming skills in C, C++, Fortran or Java.