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 am thinking of doing a fresh build and trying Python 3. What do you think? 2. When I try the following code (which manually computes the imaginary part of that same complex number) at any receiver: C_imag = np.dot(-28534314.10478436, 1.39818115e+09) + np.dot(28534314.10478439, 1.39818115e+09) print(C_imag) I see that the answer is 48 which is correct. Do you think that this fact points to MPI4py as the source of the precision loss problem, instead of numpy? Honestly, I don't understand how they have that serious bugs unresolved. On Tue, May 22, 2018 at 5:05 PM, Jeff Squyres (jsquyres) <jsquy...@cisco.com > wrote: > 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 two programs to show the difference: > > https://gist.github.com/jsquyres/2ed86736e475e9e9ccd08b66378ef968 > > I don't know offhand how mpi4py sends floating point values -- but I'm > guessing that either mpi4py or numpy are pickling the floating point values > (vs. sending the exact bitmap of the floating point value), and some > precision is being lost either in the pickling or the de-pickling. That's > a guess, though. > > > > > On May 22, 2018, at 2:51 PM, Konstantinos Konstantinidis < > kostas1...@gmail.com> wrote: > > > > 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 explain. Now imagine for simplicity that we have only 2 MPI > processes, one master and one worker. I have created two versions of this > program for this case. The first one constructs two complex numbers (1-by-1 > matrices for simplicity) and sends them to the worker to compute the > product. This program is like a skeleton for what I am trying to do with > multiple workers. In the second program, I have omitted the algorithm and > have just hard-coded these two complex numbers into the code. The programs > are supposed to give the same product shown here: > > > > a = 28534314.10478439+28534314.10478436j > > > > b = -1.39818115e+09+1.39818115e+09j > > > > a*b = -7.97922802e+16+48j > > > > This has been checked in Matlab. Instead, the first program does not > work and the worker gives a*b = -7.97922801e+16+28534416.j while the second > program works correctly. Please note that the data is transmitted correctly > from the master to the worker and the data structures are the same in both > cases (see the print() functions). > > > > The first (wrong) program is program1.py and the second (correct) is > program2.py > > > > I am using MPI4py 3.0.0. along with Python 2.7.14 and the kernel of Open > MPI 2.1.2. I have been straggling with this problem for a whole day and > still cannot figure out what's going on. I have tried numerous > initializations like np.zeros(), np.zeros_like(), np.empty_like() as well > as both np.array and np.matrix and functions np.dot(), np.matmul() and the > operator *. > > > > Finally, I think that the problem is always with the imaginary part of > the product based on other examples I tried. Any suggestions? > > <program1.py><program2.py>__________________________________ > _____________ > > users mailing list > > users@lists.open-mpi.org > > https://lists.open-mpi.org/mailman/listinfo/users > > > -- > Jeff Squyres > jsquy...@cisco.com > >
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