133dc97f67
Former-commit-id:06a8b51d6d[formerly9f19e3f712[formerly 64fa9254b946eae7e61bbc3f513b7c3696c4f54f]] Former-commit-id:9f19e3f712Former-commit-id:a02aeb236c
37 lines
1.2 KiB
Python
Executable file
37 lines
1.2 KiB
Python
Executable file
from Scientific.BSP import ParData, ParSequence, ParAccumulator, \
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ParFunction, ParRootFunction, numberOfProcessors
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import operator
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# Local and global computation functions.
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def makepairs(sequence1, sequence2):
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pairs = []
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for item1 in sequence1:
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for item2 in sequence2:
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pairs.append((item1, item2))
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return pairs
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global_makepairs = ParFunction(makepairs)
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# Local and global output functions.
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def output(result):
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print result
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global_output = ParRootFunction(output)
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# A list of data items (here letters) distributed over the processors.
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my_items = ParSequence('abcdef')
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# The number of the neighbour to the right (circular).
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neighbour_pid = ParData(lambda pid, nprocs: [(pid+1)%nprocs])
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# Loop to construct all pairs.
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pairs = ParAccumulator(operator.add, [])
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pairs.addValue(global_makepairs(my_items, my_items))
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other_items = my_items
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for i in range(numberOfProcessors-1):
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other_items = other_items.put(neighbour_pid)[0]
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pairs.addValue(global_makepairs(my_items, other_items))
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# Collect results on processor 0.
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all_pairs = pairs.calculateTotal()
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# Output results from processor 0.
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global_output(all_pairs)
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