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Removes all items from dict d Returns a shallow copy of dict d Returns a dict whose keys are the items in sequence s and whose values are None or v if v is given Returns key k s associated value, or None if k isn t in dict d Returns key k s associated value, or v if k isn t in dict d Returns a view of all the (key, value) pairs in dict d Returns a view of all the keys in dict d Returns key k s associated value and removes the item whose key is k, or raises a KeyError exception if k isn t in d Returns key k s associated value and removes the item whose key is k, or returns v if k isn t in dict d Returns and removes an arbitrary (key, value) pair from dict d, or raises a KeyError exception if d is empty The same as the dictget() method, except that if the key is not in dict d, a new item is inserted with the key k, and with a value of None or of v if v is given Adds every (key, value) pair from a that isn t in dict d to d, and for every key that is in both d and a, replaces the corresponding value in d with the one in a a can be a dictionary, an iterable of (key, value) pairs, or keyword arguments Returns a view of all the values in dict d

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Efficiency It is important that the data chunks be large enough that the amount of work to update the chunk offsets the overhead of managing dependencies A more subtle issue to consider is how the chunks map onto UEs An effective parallel algorithm must balance the load between UEs If this isn't done well, some PEs might have a disproportionate amount of work, and the overall scalability will suffer This may require clever ways to break up the problem For example, if the problem clears the columns in a matrix from left to right, a column mapping of the matrix will cause problems as the UEs with the leftmost columns will finish their work before the others A row based block decomposition or even a block cyclic decomposition (in which rows are assigned cyclically to PEs) would do a much better job of keeping all the processors fully occupied These issues are discussed in more detail in the Distributed Array pattern

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dvalues()

The dictitems(), dictkeys(), and dictvalues() methods all return dictionary views A dictionary view is effectively a read-only iterable object that appears to hold the dictionary s items or keys or values, depending on the view we have asked for In general, we can simply treat views as iterables However, two things make a view different from a normal iterable One is that if the dictionary the view refers to is changed, the view re ects the change The other is that key and item views support some set-like operations Given dictionary view v and set or dictionary view x, the supported operations are:

Simplicity Overly complex data decompositions can be very difficult to debug A data decomposition will usually require a mapping of a global index space onto a task local index space Making this mapping abstract allows it to be easily isolated and tested

We can use the membership operator, in, to see whether a particular key is in a dictionary, for example, x in d And we can use the intersection operator to see which keys from a given set are in a dictionary For example:

d = {}fromkeys("ABCD", 3) s = set("ACX") matches = dkeys() & s # d == {'A': 3, 'B': 3, 'C': 3, 'D': 3} # s == {'A', 'C', 'X'} # matches == {'A', 'C'}

samples of the kinds of documentation you can use Then, in 6, we discuss the compilation and distillation of the required data into design requirements

Note that in the snippet s comments we have used alphabetical order this is purely for ease of reading since dictionaries and sets are unordered Dictionaries are often used to keep counts of unique items One such example of this is counting the number of occurrences of each unique word in a le Here is a complete program (uniquewords1py) that lists every word and the number of times it occurs in alphabetical order for all the les listed on the command line:

After the data has been decomposed, if it has not already been done, the next step is to look at the task decomposition implied by the tasks The Task Decomposition pattern may help with this analysis Examples

import string import sys words = {} strip = stringwhitespace + stringpunctuation + stringdigits + "\"'" for filename in sysargv[1:]: for line in open(filename): for word in linelower()split(): word = wordstrip(strip) if len(word) > 2: words[word] = wordsget(word, 0) + 1 for word in sorted(words): print("'{0}' occurs {1} times"format(word, words[word]))

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