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 and goals of parallel computing, parallel computer architectures, programming models, performance measurement and analysis, introduction to programming paradigms such as MPI (Message Passing Interface), Pthreads, and OpenMP. Other aspects and languages for programming multi-core processors.

The content of the lecture is overlapping almost entirely with the bachelor lecture "Parallel Computing" (184.710). The two lectures are mutually exclusive.

Lectures, exercises, project work

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: 0.5 ECTS
• Project work (implementations, test, benchmarking): 1.5 ECTS

• Lectures 11x2h = 22h
• Self-study  15h
• Written exam 8+2h = 10h
• Home exercises 2x4h = 8h
• Projects 20h

 Total: 75h = 3 ECTS

Exercises, projects, written or oral examination

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