133dc97f67
Former-commit-id:06a8b51d6d[formerly9f19e3f712[formerly 64fa9254b946eae7e61bbc3f513b7c3696c4f54f]] Former-commit-id:9f19e3f712Former-commit-id:a02aeb236c
59 lines
2.3 KiB
Python
Executable file
59 lines
2.3 KiB
Python
Executable file
from Scientific.BSP import ParClass, ParBase, numberOfProcessors
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import Numeric, operator
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class DistributedMatrix(ParBase):
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def __parinit__(self, pid, nprocs, matrix, distribution_mode):
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self.full_shape = matrix.shape
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self.mode = distribution_mode
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if distribution_mode == 'row':
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chunk_size = (matrix.shape[0]+numberOfProcessors-1) \
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/ numberOfProcessors
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self.submatrix = matrix[pid*chunk_size:(pid+1)*chunk_size, :]
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elif distribution_mode == 'column':
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chunk_size = (matrix.shape[1]+numberOfProcessors-1) \
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/ numberOfProcessors
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self.submatrix = matrix[:, pid*chunk_size:(pid+1)*chunk_size]
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else:
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raise ValueError, "undefined mode " + distribution_mode
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def __init__(self, local_submatrix, full_shape, distribution_mode):
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self.submatrix = local_submatrix
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self.full_shape = full_shape
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self.mode = distribution_mode
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def __repr__(self):
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return "\n" + repr(self.submatrix)
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def __add__(self, other):
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if self.full_shape == other.full_shape and self.mode == other.mode:
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return DistributedMatrix(self.submatrix+other.submatrix,
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self.full_shape, self.mode)
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else:
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raise ValueError, "incompatible matrices"
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def __mul__(self, other):
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if self.full_shape[1] != other.full_shape[0]:
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raise ValueError, "incompatible matrix shapes"
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if self.mode == 'row' or other.mode == 'column':
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raise ValueError, "not implemented"
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product = Numeric.dot(self.submatrix, other.submatrix)
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full_shape = product.shape
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chunk_size = (full_shape[0]+numberOfProcessors-1)/numberOfProcessors
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messages = []
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for i in range(numberOfProcessors):
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messages.append((i, product[i*chunk_size:(i+1)*chunk_size, :]))
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data = self.exchangeMessages(messages)
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sum = reduce(operator.add, data, 0)
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return DistributedMatrix(sum, full_shape, 'row')
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gDistributedMatrix = ParClass(DistributedMatrix)
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m = Numeric.resize(Numeric.arange(10), (8, 8))
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v = Numeric.ones((8, 1))
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matrix = gDistributedMatrix(m, 'column')
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vector = gDistributedMatrix(v, 'row')
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product = matrix*vector
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print product
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