Supercomputers are capable of providing tremendous computational power, but must be carefully programmed to take advantage of that power. There are several different architectures used in supercomputers, with differing computational models. These different models present a variety of resource allocation problems that must be solved. The computational needs of a program must be cast in terms of the computational model supported by the supercomputer, and this must be done in a way that makes effective use of the machine's resources. This is the resource allocation problem. The computational models of available supercomputers and the associated resource allocation techniques are surveyed. It is shown that many problems and solutions appear repeatedly in very different computing environments.

Abstruct -In this paper, we describe the development and analy,sis of a Computer Science curricuium that fu& embraces the object-oriented (00) paradigm.The curriculum addresses ihe three compofients that the ACM has reconmended for a computer science curriculum: concepts, skills, and abilities. Ow vision for the currfculum moves beyond a scheme of integrated courses to incorporate recent research from the field of cognitive psychologv with the idea of retaining more students by bolstering their cognitive skills. Good object-oriented design (OUD) requires such critical skiIIs as strategic pluming, analogical probbm solving, and mental modeling. Past approaches have assumed either that these skill,$ are learned as an incidenial by-product of truditionui training approaches or that they are inherent to the individual student. WO suggest specific ways in which the assessment and mining of these key mefacognitive skills can be incorporated into the curriculum INTRODUCTIONRecently, basic and applied psychological research on metacognition has enjoyed a surge of interest from the education and instructional research community [16]. Educators have sought ways of improving students' metacognitive abilities as a means of enhancing critical thinking and performance in a variety of fields, particularly in the sciences [13]. The greatest difficulty seems to be in getting researchers and educators to agree upon precisely which metacognitive skills are most useful for particular domains of knowiedge and upon ways in which those skills may be enhanced. #at professionals do agree upon is that the focus of instruction should be shifted away kom merely imparting domain specific knowledge and toward the assessment and training of learning skills. According to Reif [161, due to an increasingly complex and rapidly changing world, the acquisition of facts (which are expanding exponentially) must take a back seat to the more critical abilities of applying abstract concepts and adapting to novel situations.Traditionally, hypotheses about learning outcomes in camputcr science have emphasized individual differences between the learners, such as their mathematical ability, background in computing, gender, and anxiety about computers [24j While these factors may account for some differences in achievement, a great deal of the variability in

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