Abstract:

In the past, parallel processing was not widely adopted because of the complexity and lengthy duration of parallel software development and the amazing advancement of processing speed of uniprocessors. However, clock speed of contemporary uniprocessors can hardly be further pushed. That is why the major processor manufacturers like Intel and AMD introduce multi-cores processors. But little did people know that the extra cores of a multi-core processor cannot be utilized unless programs specifically written for multi-core processors are executed. In other word, parallel software is mandatory to take advantage of the multi-core processors. In short, the age of parallel computers has arrived and parallel processing is the only way to build more powerful computer systems with current technology. Therefore, the need for parallel processing education is prominent. Most educators are hindered from initiating parallel processing courses by a misconception: affordability of costly parallel computer systems. Thanks to the contribution of open-source software developers, major parallel software libraries are successfully ported to personal computer platform. Along with other free open-source software for PC, they can make networked PC’s a cluster, an affordable platform for parallel processing education. With clusters built with the retired PC’s and free software, any institution can own its tools with minimal cost and start parallel processing education. Although an affordable cluster can be built with the free open-source software and retired PC’s, it cannot work correctly without some vital configurations. Unfortunately the free software does not provide support, necessary background knowledge, or correlated information for constructing a cluster. This paper presents the practical experience to build an affordable cluster. The free software library for multi-core programming, the parallel programming specific to multi-core processors, is also presented in the paper. Both approaches together provide low-cost solutions for all institutions to effectively offer their parallel processing courses.

Keywords:

Parallel processing, multi-core programming, cluster, computer education.