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This course introduces the students to the paradigm of parallel computing. Today almost all computer systems include so-called multi-core chips. To exploit the full performance of such systems one needs to employ parallel programming. |
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The course covers shared-memory parallelization as well as parallelization with message passing on distributed-memory architectures. The following parallelising techniques will be treated, depending on the underlying hardware
- OpenMP for shared memory multiprocessing programming.
- MPI for message-passing multiprocessing programming.
- CUDA for GPU computing and parallelising tasks on Nvidia GPUs.
Instructional Modes
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There will be 2 marked assignments worth 10% each and one exam weighing 80%.
Additional requirement: students do need to have at least a pass in the exam.
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| The course description will be finalized in the course of August-September. |
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