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Check for block shape/grid_shapes that would make empty localarrays. #444

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Check for block shape/grid_shapes that would make empty localarrays. #444

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16 changes: 16 additions & 0 deletions distarray/metadata_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -139,6 +139,9 @@ def make_grid_shape(shape, dist, comm_size):
# Trivial case: all processes used for the one distributed dimension.
if comm_size >= shape[distdims[0]]:
dist_grid_shape = (shape[distdims[0]],)
elif (('b' == shape[distdims[0]]) and
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@kwmsmith kwmsmith Jun 23, 2014

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Won't this issue show up for multi-dimensional block-distributed arrays for certain sizes? The test here needs to be more general to cover the multi-dimensional cases. If it can be refactored so it handles things on a per-dimension basis, that would be ideal.

For example, say we have a 16 engine cluster running. Currently, we get a lot of empty localarrays with a 6 x 9 block-distributed array:

In [5]: d = Distribution.from_shape(c, (6, 9), ('b', 'b'))

In [6]: d.localshapes()
Out[7]:
[(2, 3),
 (2, 3),
 (2, 3),
 (2, 0),
 (2, 3),
 (2, 3),
 (2, 3),
 (2, 0),
 (2, 3),
 (2, 3),
 (2, 3),
 (2, 0),
 (0, 3),
 (0, 3),
 (0, 3),
 (0, 0)]

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@cowlicks cowlicks Jun 23, 2014

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Yes this needs to be done on a per dimension basis.

check_bad_dims(comm_size, shape[distdims[0]])):
return make_grid_shape(shape, dist, comm_size - 1)
else:
dist_grid_shape = (comm_size,)

Expand Down Expand Up @@ -472,3 +475,16 @@ def shapes_from_dim_data_per_rank(ddpr): # ddpr = dim_data_per_rank
shape.append(size_from_dim_data(dd))
shape_list.append(tuple(shape))
return shape_list


def check_bad_dims(p, d):
""" check for block distributions that woul create empty localarrays
along some axis. For more information see gh-issue 442.
"""
n = d // p
num = d - p + 1
div = p - 1
if (num / div) == n:
return True
else:
return False