
handle: 2077/18096
Visualization is believed to be an effective technique for learning and understanding algorithms in traditional computer science. In this paper, we focus on parallel computing and algorithms. An inherent difficulty with parallel programming is that it requires synchronization and coordination of the concurrent activities. We want to use visualization to help students to understand how the processors work together in an algorithm and how they interact through communication. To conceptualize this we have used two different visualization techniques, computer animations and role plays. As the students can see how the processors run simultaneously in parallel, it illustrates important concepts such as processor load balance, serialization bottlenecks, synchronization and communication. The results show that both animations and role plays are better for learning and understanding algorithms than the textbook.
Note: Accepted to Third Program Visualization Workshop (PVW'2004)
Datavetenskap (datalogi), Computer Sciences, Computer Science Education, Instructional Innovation, Classroom Research, Educational Sciences, Higher Education, Utbildningsvetenskap
Datavetenskap (datalogi), Computer Sciences, Computer Science Education, Instructional Innovation, Classroom Research, Educational Sciences, Higher Education, Utbildningsvetenskap
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
