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In other designs, the interaction between tasks is not so chronologically regular An example is the discrete event simulation described previously, in which the events that lead to interaction between tasks can be chronologically irregular Are the tasks grouped in the best way The temporal relations are easy: Tasks that can run at the same time are naturally grouped together But an effective design will also group tasks together based on their logical relationship in the overall problem As an example of grouping tasks, consider the molecular dynamics problem discussed in the Examples section of the Group Tasks, Order Tasks, and Data Sharing patterns The grouping we eventually arrive at (in the Group Tasks pattern) is hierarchical: groups of related tasks based on the high level operations of the problem, further grouped on the basis of which ones can execute concurrently Such an approach makes it easier to reason about whether the design meets the necessary constraints (because the constraints can be stated in terms of the task groups defined by the high level operations) while allowing for scheduling flexibility

When creating a complete data type two possibilities are open to us One is to create the data type from scratch Although the data type will inherit object (as all Python classes do), every data attribute and method that the data type requires (apart from __new__()) must be provided The other possibility is to inherit from an existing data type that is similar to the one we want to create In this case the work usually involves reimplementing those methods we want to behave differently and unimplementing those methods we don t want at all In the following subsubsection we will implement a FuzzyBool data type from scratch, and in the subsubsection after that we will implement the same type but will use inheritance to reduce the work we must do The built-in bool type is two-valued (True and False), but in some areas of AI (Arti cial Intelligence), fuzzy Booleans are used, which have values corresponding to true and false , and also to intermediates between them In our implementations we will use oating-point values with 00 denoting False and 10 denoting True In this system, 05 means 50 percent true, and 025 means 25 percent true, and so on Here are some usage examples (they work the same with either implementation):

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# returns: True # returns: (True, False) # returns: FuzzyBool(0125)

In case it hasn t been said enough in this book so far, now is a good time to remind you: Applications, and their databases, exist only to meet the needs of an enterprise, whether it s a business, a school, or a nonprofit venture This means that one of the most important aspects of application design, and the design of the application s supporting database, is to develop a strong understanding of the organization s needs and to figure out how your design will meet those needs To identify the business needs, you usually meet with the key stakeholders Usually, the organization has already identified a business need (or needs) before initiating a development project It is your job, however, to identify the specific needs that are being addressed by the application that

int done[MAX]; // an array of flags dimensioned for the // boundary data // // Create Team of Threads // init (Nsize, Bsize, field, boundary, incoming); #pragma omp parallel shared(boundary, field, Bsize, Nsize) // // Set up the SPMD program Note: by declaring ID and Num_threads // inside the parallel region, we make them private to each thread // int ID, nprocs, i, left; ID = omp_get_thread_num(); nprocs = omp_get_num_threads(); if (nprocs > MAX) { exit (-1); } // // assume a ring of processors and a communication pattern // where boundaries are shifted to the right // left = (ID-1); if(left<0) left = nprocs-1; done[ID] = 0; // set flag stating "buffer ready to fill" for(i = 0; i < Nsteps; i++){ #pragma omp barrier // all visible variables flushed, so we // don't need to flush "done" extract_boundary(Nsize, Bsize, ID, num_procs, boundary[ID]);

a & b b |= FuzzyBoolFuzzyBool(5) "a={0:1%} b={1:0%}"format(a, b) # returns: FuzzyBool(025) # b is now: FuzzyBool(05) # returns: 'a=875% b=50%'

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