Introducing computational thinking, parallel programming and performance engineering in interdisciplinary studies

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© 2017 Elsevier Inc. Nowadays, many fields of science and engineering are evolving through the joint contribution of complementary fields. Computer science, and especially High Performance Computing, has become a key factor in the development of many research fields, establishing a new paradigm called computational science. Researchers and professionals from many different fields require knowledge of High Performance Computing, including parallel programming, to develop fruitful and efficient work in their particular field. Therefore, at Universitat Autònoma of Barcelona (Spain), an interdisciplinary Master on “Modeling for Science and Engineering” was started 5 years ago to provide a thorough knowledge of the application of modeling and simulation to graduate students in different fields (Mathematics, Physics, Chemistry, Engineering, Geology, etc.). In this Master's degree, “Parallel Programming” appears as a compulsory subject because it is a key topic for them. The concepts learned in this subject must be applied to real applications. Therefore, a complementary subject on “Applied Modeling and Simulation” has also been included. It is very important to show the students how to analyze their particular problems, think about them from a computational perspective and consider the related performance issues. So, in this paper, the methodology and the experience in introducing computational thinking, parallel programming and performance engineering in this interdisciplinary Master's degree are shown. This overall approach has been refined through the Master's life, leading to excellent academic results and improving the industry and students appraisal of this programme.
Original languageEnglish
Pages (from-to)116-126
JournalJournal of Parallel and Distributed Computing
Publication statusPublished - 1 Jul 2017


  • Agent-based models
  • CUDA
  • GPUs
  • MPI
  • Message passing
  • Model simulation
  • OpenMP
  • Parallel programming
  • Shared memory


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