Also my inner voice is shouting that there must be an easy way to express this in Julia https://discourse.julialang.org/t/apply-reduction-along-specific-axes/3301/16
OK, these are not the same stepwise cumulative operatiosn that you want, but the idea is close. ps. Note to self - stop listening to the voices. On 2 May 2018 at 14:08, John Hearns <hear...@googlemail.com> wrote: > Peter, how large are your models, ie how many cells in each direction? > Something inside of me is shouting that if the models are small enough > then MPI is not the way here. > Assuming use of a Xeon processor there should be some AVX instructions > which can do this. > > This is rather out of date, but is it helpful? > ttps://www.quora.com/Is-there-an-SIMD-architecture-that- > supports-horizontal-cumulative-sum-Prefix-sum-as-a-single-instruction > > https://software.intel.com/sites/landingpage/IntrinsicsGuide/ > > > On 2 May 2018 at 13:56, Peter Kjellström <c...@nsc.liu.se> wrote: > >> 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 >> > >
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