Practical Course on Parallel Computing

Overview   đź”—

As a practical course, the focus is on hands-on sessions and problem solving. Students get an introduction to the various topics covered by the course and then use the laboratory equipment to solve assignments of each section of the course. Workable solutions will be graded.

Key information   đź”—

ContactSven Bingert
VenueInformatik-Provisorium - 0.101 (exercise) and Informatik-Provisorium - 0.102 (lecture)
TimeTue, 10:00 - 12:00 (exercise), 14:00 - 16:00 (lecture), Thu, 14:00 - 16:00 (exercise)
ModuleB.Inf.1803.Mp: Fachpraktikum I ,B.Inf.1804.Mp: Fachpraktikum II , B.Inf.1805.Mp: Fachpraktikum III , B.Inf.1834.Mp: Fachpraktikum Data Science I (klein) , B.Inf.1835.Mp: Fachpraktikum Data Science II (klein) , M.Inf.1808.Mp: Practical Course on Parallel Computing , M.Phy.413.Mp: General Seminar
ECTScf. module description
Presence timecf. module description
Independent studycf. module description

Learning Objectives   đź”—

Successfully completing this course, students are able to

  • practically work with a cluster of computers (e.g., using a batch system)
  • practically utilize grid computing infrastructures and manage their jobs (e.g., Globus toolkit)
  • apply distributed memory architectures for parallelism through practical problem solving (MPI programming)
  • utilize shared memory architectures for parallelism (e.g., OpenMP and pthreads)
  • utilize heterogenous parallelism (e.g., OpenCL, CUDA and general GPU
  • programming concepts)
  • utilize their previous knowledge in data structures and algorithm

Prerequisites   đź”—

None. Hearing Parallel Computing is highly recommended.

  • Parallel Computing
  • Computer architecture
  • Data structures and algorithms
  • Programming in C(/C++)
  • Basic knowledge of computer networks
  • Basic know-how of computing clusters

Examination   đź”—

None. Assignments will be graded, detailed information are given during the course.