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Traditionally courses in scientific computing teach a large number of methods and algorithms for different mathematical problems. The algorithms are applied on simplified problems and not on real applications. The result is that the students can't see the main thread, they focus only on the details of the methods and don't see the entirety. The students can not put what they learn into perspective and their motivation to study becomes diminished. In this paper we suggest a case-centered approach for learning scientific computing. We use a real-life case, weather prediction, as a starting point for learning. The case is analyzed and discussed in class. To follow up the discussions the students are assigned learning tasks in scientific computing defined from the case analysis. The real-life application connects the different topics in scientific computing together and motivates the students. The response from the students has been positive and the case has increased their understanding of what scientific computing is and what it can be used for.

On cc-NUMA multi-processors, the non-uniformity of main memory latencies motivates the need for co-location of threads and data. We call this special form of data locality, geographical locality. In this article, we study the performance of a parallel PDE solver with adaptive mesh refinement. The solver is parallelized using OpenMP and the adaptive mesh refinement makes dynamic load balancing necessary. Due to the dynamically changing memory access pattern caused by the runtime adaption, it is a challenging task to achieve a high degree of geographical locality.

The main conclusions of the study are: (1) that geographical locality is very important for the performance of the solver, (2) that the performance can be improved significantly using dynamic page migration of misplaced data, (3) that a migrate-on-next-touch directive works well whereas the first-touch strategy is less advantageous for programs exhibiting a dynamically changing memory access patterns, and (4) that the overhead for such migration is low compared to the total execution time.