Thanks Ben and Lisandro.
You are right, my comparison based on the print() was misleading in terms
of precision so I probably didn't copy enough decimal places :)
I will also try to skip datatype specification and let MPI4py select the
datatype, and see what's going on.
On Tue, May 22, 2018 at
The beauty of floating point! Indeed, this is just a precision problem.
Using 16 significative digits in program2.py produces the same result as in
program1.py
@Jorge Pretty good example for your Numerical Calculus teaching, for those
kids that ask over and over again "What's the point of these na
Hi Jeff, Konstantinos,
I think you might want MPI.C_DOUBLE_COMPLEX for your datatype, since
np.complex128 is a double-precision. But I think it’s either ignoring this and
using the datatype of the object you’re sending or MPI4py is handling the
conversion in the backend somewhere. You could act
Thanks Jeff.
I ran your code and saw your point. Based on that, it seems that my
comparison by just printing the values was misleading.
I have two questions for you:
1. Can you please describe your setup i.e. Python version, Numpy version,
MPI4py version and Open MPI version? I'm asking since I
There are two issues:
1. You should be using MPI.C_COMPLEX, not MPI.COMPLEX. MPI.COMPLEX is a
Fortran datatype; MPI.C_COMPLEX is the C datatype (which is what NumPy is using
behind the scenes).
2. Somehow the received B values are different between the two.
I derived this program from your tw
Assume an Python MPI program where a master node sends a pair of complex
matrices to each worker node and the worker node is supposed to compute
their product (conventional matrix product). The input matrices are
constructed at the master node according to some algorithm which there is
no need to e
