Scheduling Techniques to Classify Wear Particles on Multi-Computers

Processor scheduling techniques are used in conjunction to exploit the major paradigms of algorithmic parallelism, geometric parallelism, and processor farming. Static process scheduling techniques are successfully used with algorithmic and geometric parallelism, whilst dynamic process scheduling is better suited in dealing with the independent processes inherent in the processor farming paradigm. This paper investigates the application of parallel, or multi-computers to a class of problems exhibiting spatial data characteristic of the geometric paradigm that is best suited to the static scheduling scheme. However, by using the processor farming paradigm, a dynamic scheduling technique is developed to suit the MIMD structure of the multi-computers. The specific problem chosen for the investigation is the recognition and classification of microscopic wear particles generated by wear mechanisms. Experiments are performed on both schemes and compared in terms of total processing time, speedup, and efficiency