Introduction
Abstract
In the recent past, teaching and learning of parallel programming has become increasingly important due to the ubiquity of parallel processors in portable devices, workstations, and compute clusters. Stagnating single-threaded performance of modern CPUs requires future computer scientists and engineers to write highly parallelized code in order to fully utilize the compute capabilities of current hardware architectures. The design of parallel algorithms, however, can be challenging especially for inexperienced students due to common pitfalls such as race conditions when concurrently accessing shared resources, defective communication patterns causing deadlocks, or the non-trivial task of efficiently scaling ...
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