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Dictionary keys are unique, so if we add a key value item whose key is the same as an existing key, the effect is to replace that key s value with a new value Brackets are used to access individual values for example, d["root"] returns 18, d[21] returns the string "venus", and d[91] causes a KeyError exception to be raised, given the dictionary shown in Figure 35 Brackets can also be used to add and delete dictionary items To add an item we use the = operator, for example, d["X"] = 59 And to delete an item we use the del statement for example, del d["mars"] will delete the item whose key is mars from the dictionary, or raise a KeyError exception if no item has that

'mars' (4, 11) 18 0 -14 None 45 'blue' [75, 'R', 2] 'rover'

Solution In shared memory programming environments such as OpenMP, the data decomposition will frequently be implied by the task decomposition In most cases, however, the decomposition will need to be done by hand, because the memory is physically distributed, because data dependencies are too complex without explicitly decomposing the data, or to achieve acceptable efficiency on a NUMA computer If a task based decomposition has already been done, the data decomposition is driven by the needs of each task If well defined and distinct data can be associated with each task, the decomposition should

21 'venus'

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Every relational database must be designed to meet data quality, performance, and scalability requirements For a database to be efficient, the data it contains must be maintained in a consistent and logical state Normalization helps reveal design requirements that remove potential data manipulation anomalies However, strict normalization must often be balanced against specialized query needs and must be tested for performance It may be necessary to denormalize certain aspects of a database to ensure that queries return in an acceptable time while still maintaining data integrity Every design you work on should include phases to identify normal forms and a phase to identify denormalization needs This practice will ensure that you ve removed data consistency flaws while preserving the elements of a highperformance system This completes Part I, which has laid the foundation for building an effective data model Part II begins with 5, Requirements Gathering, which launches the overall business process of designing and deploying a data model

'root' 18

be simple When starting with a data decomposition, however, we need to look not at the tasks, but at the central data structures defining the problem and consider whether they can they be broken down into chunks that can be operated on concurrently A few common examples include the following

key Items can also be removed (and returned) from the dictionary using the dictpop() method Dictionaries support the built-in len() function, and for their keys, fast membership testing with in and not in All the dictionary methods are listed in Table 33 Because dictionaries have both keys and values, we might want to iterate over a dictionary by (key, value) items, by values, or by keys For example, here are two equivalent approaches to iterating by (key, value) pairs:

for item in ditems(): print(item[0], item[1]) for key, value in ditems(): print(key, value)

Iterating over a dictionary s values is very similar:

Array based computations Concurrency can be defined in terms of updates of different segments of the array If the array is multidimensional, it can be decomposed in a variety of ways (rows, columns, or blocks of varying shapes) Recursive data structures We can think of, for example, decomposing the parallel update of a large tree data structure by decomposing the data structure into subtrees that can be updated concurrently

.

for value in dvalues(): print(value)

To iterate over a dictionary s keys we can use dictkeys(), or we can simply treat the dictionary as an iterable that iterates over its keys, as these two equivalent code snippets illustrate:

Regardless of the nature of the underlying data structure, if the data decomposition is the primary factor driving the solution to the problem, it serves as the organizing principle of the parallel algorithm When considering how to decompose the problem's data structures, keep in mind the competing forces

for key in d: print(key) for key in dkeys(): print(key)

If we want to change the values in a dictionary, the idiom to use is to iterate over the keys and change the values using the brackets operator For example, here is how we would increment every value in dictionary d, assuming that all the values are numbers:

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