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It s likely that you are reading this book either because you ve been given a project that will make you responsible for building a data model, or you would like to have the skills necessary to get a job doing this type of work (Or perhaps you are reading this book for its entertainment value, in which case you should seriously consider seeking some sort of therapy) To explain the importance of bringing your customers into the design process, we like to compare data model design to automobile engine design Knowing how to design an automobile engine is not something that many people take up as a passing fancy; if you learn how to design them, it s a good bet that you plan to make a career of it There is a great deal of focus on the technical details: how the engine must run, what parts are necessary, and how to optimize the performance of the engine to meet the demands that will be placed on it However, there is no way to know what those demands will be without knowing the type of automobile in which the engine will be placed This is also true of data models; although the logical model revolves around the needs of the business, the database will be largely dependent on the application (or applications) that will load, retrieve, and allow users to manipulate data When you re gathering requirements, you must keep both of these factors in mind When you re building a new data model, the single most important thing to know is why, and for whom, you are designing the data model This requires extensive research with the users of the application that will eventually be the interface to the database, as well as a review of any existing systems (whether they are manual processes or automated processes) It s also important to effectively document the information you ve gathered and turn it into a formal set of requirements for the data model In turn, you ll need to present the information to the key project stakeholders so that everyone can agree on the purpose, scope, and key deliverables before design and development begin In this chapter, we discuss the key steps involved in gathering requirements for a project, as well as the kinds of data to look for and some 97.

Flexibility The size and number of data chunks should be flexible to support the widest range of parallel systems One approach is to define chunks whose size and number are controlled by a small number of parameters These parameters define granularity knobs that can be varied to modify the size of the data chunks to match the needs of the underlying hardware (Note, however, that many designs are not infinitely adaptable with respect to granularity) The easiest place to see the impact of granularity on the data decomposition is in the overhead required to manage dependencies between chunks The time required to manage dependencies must be small compared to the overall runtime In a good data decomposition, the dependencies scale at a lower dimension than the computational effort associated with each chunk For example, in many finite difference programs, the cells at the boundaries between chunks, that is, the surfaces of the chunks, must be shared The size of the set of dependent cells scales as the surface area, while the effort required in the computation scales as the volume of the chunk This means that the computational effort can be scaled (based on the chunk's volume) to offset overheads associated with data dependencies (based on the surface area of the chunk)

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Syntax dclear() dcopy() dfromkeys( s, v) dget(k) dget(k, v) ditems() dkeys() dpop(k) dpop(k, v) dpopitem() dsetdefault( k, v) dupdate(a)

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