/usr/share/doc/python-netcdf/examples/BSP/example4.py is in python-netcdf 2.9.4-1.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | from Scientific.BSP import ParClass, ParBase, numberOfProcessors
import Numeric, operator
class DistributedMatrix(ParBase):
def __parinit__(self, pid, nprocs, matrix, distribution_mode):
self.full_shape = matrix.shape
self.mode = distribution_mode
if distribution_mode == 'row':
chunk_size = (matrix.shape[0]+numberOfProcessors-1) \
/ numberOfProcessors
self.submatrix = matrix[pid*chunk_size:(pid+1)*chunk_size, :]
elif distribution_mode == 'column':
chunk_size = (matrix.shape[1]+numberOfProcessors-1) \
/ numberOfProcessors
self.submatrix = matrix[:, pid*chunk_size:(pid+1)*chunk_size]
else:
raise ValueError, "undefined mode " + distribution_mode
def __init__(self, local_submatrix, full_shape, distribution_mode):
self.submatrix = local_submatrix
self.full_shape = full_shape
self.mode = distribution_mode
def __repr__(self):
return "\n" + repr(self.submatrix)
def __add__(self, other):
if self.full_shape == other.full_shape and self.mode == other.mode:
return DistributedMatrix(self.submatrix+other.submatrix,
self.full_shape, self.mode)
else:
raise ValueError, "incompatible matrices"
def __mul__(self, other):
if self.full_shape[1] != other.full_shape[0]:
raise ValueError, "incompatible matrix shapes"
if self.mode == 'row' or other.mode == 'column':
raise ValueError, "not implemented"
product = Numeric.dot(self.submatrix, other.submatrix)
full_shape = product.shape
chunk_size = (full_shape[0]+numberOfProcessors-1)/numberOfProcessors
messages = []
for i in range(numberOfProcessors):
messages.append((i, product[i*chunk_size:(i+1)*chunk_size, :]))
data = self.exchangeMessages(messages)
sum = reduce(operator.add, data, 0)
return DistributedMatrix(sum, full_shape, 'row')
gDistributedMatrix = ParClass(DistributedMatrix)
m = Numeric.resize(Numeric.arange(10), (8, 8))
v = Numeric.ones((8, 1))
matrix = gDistributedMatrix(m, 'column')
vector = gDistributedMatrix(v, 'row')
product = matrix*vector
print product
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