Programming courses usually focus on software-engineering problems like software decomposition and code maintenance. While computer-science lessons emphasize algorithm complexity, technological problems are usually neglected although they may significantly affect the performance in terms of wall time. As the technological problems are best explained by hands-on experience, we present a set of homework assignments focused on a range of technologies from instruction-level parallelism to GPU programming to cluster computing. These assignments are a product of a decade of development and testing on live subjects – the students of three performance-related software courses at the Faculty of Mathematics and Physics of the Charles University in Prague.

编程课程通常着重于软件工程问题，例如软件分解和代码维护。尽管计算机科学课程强调算法的复杂性，但通常会忽略技术问题，尽管它们可能会大大影响墙时的性能。由于可以通过实践经验来最好地解释技术问题，因此我们提出了一系列作业，重点放在从指令级并行性到GPU编程再到集群计算的一系列技术上。这些作业是对十年的现场课程进行开发和测试的产物，这些课程是布拉格查尔斯大学数学与物理学院的三门与性能相关的软件课程的学生。