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This paper presents an experience of Problem-based learning in a High Performance Computing course. The course is part of the specialization in High Performance Architectures and Supercomputing on a Master in New Technologies in Computer Science. It is supposed the students have a basic knowledge of Parallel Programming, but previous studies and the place where they were taken mean the group is heterogeneous. The Problem-based learning approach therefore has to facilitate the individual development and supervision of the students. The course focuses on HPC, matrix computation, parallel libraries, heterogeneous computing and scientific applications of parallelism. The students work on the different aspects of the course using the LU factorization, developing their own implementations, using different libraries, combining different levels of parallelism and conducting experiments in a small heterogeneous cluster composed of multicores of different characteristics and with GPU of different types

A High Performance Computing Course Guided by the LU Factorization