I get very high memory usage when I do something like:
sage: get_memory_usage()
-> 809.9453125
sage: A=vector(range(0,10000))/1
sage: get_memory_usage()
-> 5393.2734375
Which is a whooping 4.5GB+ of memory to hold a 10000 float vector...
I would have thought more of something along the lines of 24bytes/
float * 10000 floats + some overhead for the vector object ~= 240KB.
I've tried using heapy from the guppy suite:, but it reports only 60MB
or such being used, so it does not seem to be of any help.
sage: from guppy import hpy
sage: hp=hpy()
sage: hp.heap()
which prints:
Partition of a set of 374466 objects. Total size = 60621904 bytes.
Index Count % Size % Cumulative % Kind (class / dict of
class)
0 168476 45 28098936 46 28098936 46 str
1 97099 26 8491984 14 36590920 60 tuple
2 1595 0 4074632 7 40665552 67 dict of module
3 25460 7 3055200 5 43720752 72 types.CodeType
4 24287 6 2914440 5 46635192 77 function
5 2642 1 2701232 4 49336424 81 dict of type
6 2642 1 2372344 4 51708768 85 type
7 2865 1 1792920 3 53501688 88 dict (no owner)
8 10000 3 880000 1 54381688 90
sage.modules.vector_integer_dense.Vector_integer_dense
9 760 0 668224 1 55049912 91 dict of class
<854 more rows. Type e.g. '_.more' to view.>
Total size is nonsense, so I've stopped the analysis there. ;)
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