Implement feature sort() #310
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# Conflicts: # heat/core/manipulation.py # heat/core/tests/test_manipulations.py
…ning are not tested much yet
# Conflicts: # heat/core/manipulations.py # heat/core/tests/test_manipulations.py
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Bump, build fail |
Codecov Report
@@ Coverage Diff @@
## master #310 +/- ##
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- Coverage 96.73% 96.35% -0.39%
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Files 49 49
Lines 7167 7403 +236
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+ Hits 6933 7133 +200
- Misses 234 270 +36
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Targets issue #144.
Sort is implemented using some parallel sorting algorithms with pivots and a balancing afterwards. Problem case is where
axis = a.splitbecause there the sorting is affected by the values of the other processes. In the end the returned tensor has the same shape as the local input tensor. The lower processes have the lower (higher) values, if sorting is performed ascending (descending) (MPI_WORLD.rank == 0has the lowest (highest)).The function currently does not return the indices of the elements in the original tensor because it is not clear if this should be a tensor equal to
a.gshapeholding the information for all values or just for the local elements. The global shape one would mean a very high memory consumption for big matrices while the local one would mean more communication between the processes. Is this something that should be implemented and if yes, in which way?