Chapter 1 - 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 an application over the whole number of available compute units. Hence, acquiring parallel programming skills is nowadays an important part of many undergraduate and graduate curricula. More importantly, education of concurrent concepts is not limited to the field of High Performance Computing (HPC). The emergence of deep learning and big data lectures requires teachers and students to adopt HPC as an integral part of their knowledge domain. An understanding of basic concepts is indispensable for acquiring a deep understanding of fundamental parallelization techniques.