Practical Course on Parallel Computing (SS2018)

Teaching Staff: Ramin Yahyapour, Jens Grabowski, Arnulf Quadt, Sven Bingert, Vanessa End, Fabian Korte, Johannes Erbel, Alexander Trautsch, Gen Kawamura, Peter Chronz
Practical Course

High performance computing is a very important topic in the scientific area. It allows to distribute work over parallel and multiple instances to reduce computational time and utilized available resource in the most efficient way. Within the practical course on parallel computing, different techniques and methods as well as specific parallel architectures are considered. Utilizing libraries that are specially implemented for parallel task distribution, the course shows how to parallelize applications. The following concepts and programming models related to the different parallel architectures will be considered:

  1. Distributed memory architectures
    1. Cluster computing with Torque PBS
    2. Message Passing Interface (MPI)
    3. MapReduce
    4. Julia
    5. Spark
  2. Shared Memory architectures
    1. OpenMP
    2. Pthreads
  3. Heterogeneous parallelism (GPU, CUDA, etc.)
    1. CUDA

The maximum number of participants is limited. Therefore, you should register in StudIP in advance.

The course is taught in English.
For questions and comments, please contact Fabian Korte.

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