e.g.

    In [1]: multiply.reduce((1<<30, 1<<30, 1<<30))
    Out[1]: 0

instead of

    In [2]: (1<<30) * (1<<30) * (1<<30)
    Out[2]: 1237940039285380274899124224

    In [3]: 1<<90
    Out[3]: 1237940039285380274899124224

also multiply.reduce returns int64, instead of python int:

    In [4]: type( multiply.reduce([1,2,3]) )
    Out[4]: numpy.int64

which also leads to overflow-related problems if we further compute with
this value and other integers and results exceeds int64 - it becomes
float:

    In [5]: idx0_stop = 18446744073709551615

    In [6]: stride0   = numpy.int64(1)

    In [7]: byte0_stop = idx0_stop * stride0

    In [8]: byte0_stop
    Out[8]: 1.8446744073709552e+19

and then it becomes a real problem for BigArray.__getitem__()

    wendelin.core/bigarray/__init__.py:326: RuntimeWarning: overflow encountered in long_scalars
      page0_min  = min(byte0_start, byte0_stop+byte0_stride) // pagesize # TODO -> fileh.pagesize

and then

    >           vma0 = self._fileh.mmap(page0_min, page0_max-page0_min+1)
    E           TypeError: integer argument expected, got float

~~~~

So just avoid multiple.reduce() and do our own mul() properly the same
way sum() is builtin into python, and we avoid overflow-related
problems.