A Systematic Review on Teaching Parallel Programming

Abstract

This work aimed to perform a systematic review of the literature on teaching parallel programming using low-cost clusters, identifying the main programming languages, hardware platforms and software tools used in teaching-learning this type of programming. The research results showed that the most used clusters in the teaching of parallel programming were assembled from multicore machines (Cluster Beowulf) and by single board computers (SBC), in addition to multicore machines with graphics acceleration cards (GPUs). Regarding the use of programming languages, software tools and parallelism libraries used in teaching parallel programming, it is observed that most of the researched works mentioned the use of C, C++, and JAVA programming languages, and as parallelism libraries the use of MPI, OpenMP, CUDA and Apache Hadoop. Furthermore, the tests on the clusters were carried out through the implementation of parallelized generic algorithms and, in some cases, using algorithms that involve matrix operations.