FWIW: Measuring CPU time in parallel is fairly meaningless; the only
reasonable measurement is wall clock time. There's too much other
stuff happening in parallel communications that are not accounted for
by user CPU time (e.g., communications and time spent in the kernel).
On Dec 15, 2007, at 4:30 AM, Scientific Computing, ISSC wrote:
> we are implementing the sparse matrix (n x n) vector (n x 1)
> multiplication in parallel where matrix is divided column wise
> (equally
> divided columns) and vector is divided accordingly. Each processor
> performs local matrix-vector multiplication. A vector of size (n x
> 1) is
> generated on each processor. To get the final resultant vector, all
> the
> local vectors are summed.
> We have used MPI_Allreduce to collect the final result.
> The matrix that we are processing is 39601 x 39601.
> The vector of size 39601 is summed using MPI_Allreduce.
> We run this code for different no. of processors.
> We get a very strange results.
> The time required for processors 1 - 19 goes on increasing and
> decreases
> suddenly for no. of processors 20. After that time remains constant.
>
> # Processors Time for MV
> 1 0.145209
> 2 0.123415
> 3 0.142032
> 4 0.153569
> 5 0.154709
> 6 0.167946
> 7 0.177953
> 8 0.195782
> 9 0.190688
> 10 0.203196
> 12 0.224951
> 14 0.252618
> 16 0.262348
> 17 0.271355
> 18 0.286621
> 19 0.298761
> 20 0.105971
> 22 0.102517
> 24 0.105836
> 26 0.102807
> 28 0.104299
> 30 0.105835
> 32 0.10602
>
>
> We are calculating time using cpu_time (Fortran).
>
> _______________________________________________
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--
Jeff Squyres
Cisco Systems
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