yes, you are right..I didn't know MPI_scan and I finally jumped into, thanks
Le Lun 14 Mai 2018 20:11, Nathan Hjelm <hje...@me.com> a écrit : > Still looks to me like MPI_Scan is what you want. Just need three > additional communicators (one for each direction). With a recurive doubling > MPI_Scan inplementation it is O(log n) compared to O(n) in time. > > > > On May 14, 2018, at 8:42 AM, Pierre Gubernatis < > pierre.guberna...@gmail.com> wrote: > > Thank you to all of you for your answers (I was off up to now). > > Actually my question was't well posed. I stated it more clearly in this > post, with the answer: > > > https://stackoverflow.com/questions/50130688/mpi-cartesian-grid-cumulate-a-scalar-value-through-the-procs-of-a-given-axis-o?noredirect=1#comment87286983_50130688 > > Thanks again. > > > > 2018-05-02 13:56 GMT+02:00 Peter Kjellström <c...@nsc.liu.se>: > >> On Wed, 2 May 2018 11:15:09 +0200 >> Pierre Gubernatis <pierre.guberna...@gmail.com> wrote: >> >> > Hello all... >> > >> > I am using a *cartesian grid* of processors which represents a spatial >> > domain (a cubic geometrical domain split into several smaller >> > cubes...), and I have communicators to address the procs, as for >> > example a comm along each of the 3 axes I,J,K, or along a plane >> > IK,JK,IJ, etc..). >> > >> > *I need to cumulate a scalar value (SCAL) through the procs which >> > belong to a given axis* (let's say the K axis, defined by I=J=0). >> > >> > Precisely, the origin proc 0-0-0 has a given value for SCAL (say >> > SCAL000). I need to update the 'following' proc (0-0-1) by doing SCAL >> > = SCAL + SCAL000, and I need to *propagate* this updating along the K >> > axis. At the end, the last proc of the axis should have the total sum >> > of SCAL over the axis. (and of course, at a given rank k along the >> > axis, the SCAL value = sum over 0,1, K of SCAL) >> > >> > Please, do you see a way to do this ? I have tried many things (with >> > MPI_SENDRECV and by looping over the procs of the axis, but I get >> > deadlocks that prove I don't handle this correctly...) >> > Thank you in any case. >> >> Why did you try SENDRECV? As far as I understand your description above >> data only flows one direction (along K)? >> >> There is no MPI collective to support the kind of reduction you >> describe but it should not be hard to do using normal SEND and RECV. >> Something like (simplified psuedo code): >> >> if (not_first_along_K) >> MPI_RECV(SCAL_tmp, previous) >> SCAL += SCAL_tmp >> >> if (not_last_along_K) >> MPI_SEND(SCAL, next) >> >> /Peter K >> > > _______________________________________________ > users mailing list > users@lists.open-mpi.org > https://lists.open-mpi.org/mailman/listinfo/users > > _______________________________________________ > users mailing list > users@lists.open-mpi.org > https://lists.open-mpi.org/mailman/listinfo/users
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