Hi, Steffen,
  It is probably because your laptop CPU is "weak".  I have a local machine with one Intel Core i7 processor, which has 8 cores (16 hardware threads).  I got a similar STREAM speedup.  It just means 1~2 MPI ranks can use up all the memory bandwidth. That is why with your (weak scaling) test, more MPI ranks just gave longer time. Because the MPI processes had to share the memory bandwidth.
  On another machine with two AMD EPYC 7452 32-Core processors,  there are 8 NUMA domains.  I got
$ mpirun -n 1 --bind-to numa --map-by numa ./MPIVersion
1  22594.4873   Rate (MB/s)
$ mpirun -n 8 --bind-to numa --map-by numa ./MPIVersion
8 173565.3584   Rate (MB/s) 7.68175 
  On this kind of machine, you can expect constant time of your test up to 8 MPI ranks.

--Junchao Zhang


On Fri, Jan 12, 2024 at 11:13 AM Steffen Wilksen | Universitaet Bremen <swilksen@itp.uni-bremen.de> wrote:

Hi Junchao,

I tried it out, but unfortunately, this does not seem to give any imporvements, the code is still much slower when starting more processes.


----- Message from Junchao Zhang <junchao.zhang@gmail.com> ---------
   Date: Fri, 12 Jan 2024 09:41:39 -0600
   From: Junchao Zhang <junchao.zhang@gmail.com>
Subject: Re: [petsc-users] Parallel processes run significantly slower
     To: Steffen Wilksen | Universitaet Bremen <swilksen@itp.uni-bremen.de>
     Cc: Barry Smith <bsmith@petsc.dev>, PETSc users list <petsc-users@mcs.anl.gov>

Hi,  Steffen,
  Would it be an MPI process binding issue?  Could you try running with 
mpiexec --bind-to core -n N python parallel_example.py

--Junchao Zhang

On Fri, Jan 12, 2024 at 8:52 AM Steffen Wilksen | Universitaet Bremen <swilksen@itp.uni-bremen.de> wrote:

Thank you for your feedback.
@Stefano: the use of my communicator was intentional, since I later intend to distribute M independent calculations to N processes, each process then only needing to do M/N calculations. Of course I don't expect speed up in my example since the number of calculations is constant and not dependent on N, but I would hope that the time each process takes does not increase too drastically with N.
@Barry: I tried to do the STREAMS benchmark, these are my results:
1  23467.9961   Rate (MB/s) 1
2  26852.0536   Rate (MB/s) 1.1442
3  29715.4762   Rate (MB/s) 1.26621
4  34132.2490   Rate (MB/s) 1.45442
5  34924.3020   Rate (MB/s) 1.48817
6  34315.5290   Rate (MB/s) 1.46223
7  33134.9545   Rate (MB/s) 1.41192
8  33234.9141   Rate (MB/s) 1.41618
9  32584.3349   Rate (MB/s) 1.38846
10  32582.3962   Rate (MB/s) 1.38838
11  32098.2903   Rate (MB/s) 1.36775
12  32064.8779   Rate (MB/s) 1.36632
13  31692.0541   Rate (MB/s) 1.35044
14  31274.2421   Rate (MB/s) 1.33263
15  31574.0196   Rate (MB/s) 1.34541
16  30906.7773   Rate (MB/s) 1.31698

I also attached the resulting plot. As it seems, I get very bad MPI speedup (red curve, right?), even decreasing if I use too many threads. I don't fully understand the reasons given in the discussion you linked since this is all very new to me, but I take that this is a problem with my computer which I can't easily fix, right?


----- Message from Barry Smith <bsmith@petsc.dev> ---------
   Date: Thu, 11 Jan 2024 11:56:24 -0500
   From: Barry Smith <bsmith@petsc.dev>
Subject: Re: [petsc-users] Parallel processes run significantly slower
     To: Steffen Wilksen | Universitaet Bremen <swilksen@itp.uni-bremen.de>
     Cc: PETSc users list <petsc-users@mcs.anl.gov>

 
   Take a look at the discussion in https://petsc.gitlab.io/-/petsc/-/jobs/5814862879/artifacts/public/html/manual/streams.html and I suggest you run the streams benchmark from the branch barry/2023-09-15/fix-log-pcmpi on your machine to get a baseline for what kind of speedup you can expect.  
 
    Then let us know your thoughts.
 
   Barry



On Jan 11, 2024, at 11:37 AM, Stefano Zampini <stefano.zampini@gmail.com> wrote:

You are creating the matrix on the wrong communicator if you want it parallel. You are using PETSc.COMM_SELF

On Thu, Jan 11, 2024, 19:28 Steffen Wilksen | Universitaet Bremen <swilksen@itp.uni-bremen.de> wrote:

Hi all,

I'm trying to do repeated matrix-vector-multiplication of large sparse matrices in python using petsc4py. Even the most simple method of parallelization, dividing up the calculation to run on multiple processes indenpendtly, does not seem to give a singnificant speed up for large matrices. I constructed a minimal working example, which I run using

mpiexec -n N python parallel_example.py,

where N is the number of processes. Instead of taking approximately the same time irrespective of the number of processes used, the calculation is much slower when starting more MPI processes. This translates to little to no speed up when splitting up a fixed number of calculations over N processes. As an example, running with N=1 takes 9s, while running with N=4 takes 34s. When running with smaller matrices, the problem is not as severe (only slower by a factor of 1.5 when setting MATSIZE=1e+5 instead of MATSIZE=1e+6). I get the same problems when just starting the script four times manually without using MPI.
I attached both the script and the log file for running the script with N=4. Any help would be greatly appreciated. Calculations are done on my laptop, arch linux version 6.6.8 and PETSc version 3.20.2.

Kind Regards
Steffen




----- End message from Barry Smith <bsmith@petsc.dev> -----

 




----- End message from Junchao Zhang <junchao.zhang@gmail.com> -----