<div dir="ltr">On Tue, Oct 22, 2013 at 3:57 AM, Juha Jäykkä <span dir="ltr"><<a href="mailto:juhaj@iki.fi" target="_blank">juhaj@iki.fi</a>></span> wrote:<br><div class="gmail_extra"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Barry,<br>
<br>
I seem to have touched a topic which goes way past my knowledge of PETSc<br>
internals, but it's very nice to see a thorough response nevertheless. Thank<br>
you. And Matthew, too.<br>
<br>
After reading your suspicions about number of ranks, I tried with 1, 2 and 4<br>
and the memory use indeed seems to go down from 1:<br></blockquote><div><br></div><div>I am now convinced that /proc is showing total memory ever allocated since the OS is not</div><div>recovering any freed memory. If you want to see memory allocated, but not freed, just</div>
<div>do not destroy the DA and run with -malloc_test.</div><div><br></div><div> Matt</div><div> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
juhaj@dhcp071> CMD='import helpers; procdata=helpers._ProcessMemoryInfoProc();<br>
<div class="im">print procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]; from petsc4py<br>
import PETSc; procdata=helpers._ProcessMemoryInfoProc(); print<br>
procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]; da =<br>
PETSc.DA().create(sizes=[100,100,100],<br>
proc_sizes=[PETSc.DECIDE,PETSc.DECIDE,PETSc.DECIDE], boundary_type=[3,0,0],<br>
stencil_type=PETSc.DA.StencilType.BOX, dof=7, stencil_width=1,<br>
comm=PETSc.COMM_WORLD); procdata=helpers._ProcessMemoryInfoProc(); print<br>
</div>procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]'<br>
juhaj@dhcp071> mpirun -np 1 python -c "$CMD"<br>
21 MiB / 22280 kB<br>
21 MiB / 22304 kB<br>
354 MiB / 419176 kB<br>
juhaj@dhcp071> mpirun -np 2 python -c "$CMD"<br>
22 MiB / 23276 kB<br>
22 MiB / 23020 kB<br>
22 MiB / 23300 kB<br>
22 MiB / 23044 kB<br>
141 MiB / 145324 kB<br>
141 MiB / 145068 kB<br>
juhaj@dhcp071> mpirun -np 4 python -c "$CMD"<br>
22 MiB / 23292 kB<br>
22 MiB / 23036 kB<br>
22 MiB / 23316 kB<br>
22 MiB / 23060 kB<br>
22 MiB / 23316 kB<br>
22 MiB / 23340 kB<br>
22 MiB / 23044 kB<br>
22 MiB / 23068 kB<br>
81 MiB / 83716 kB<br>
82 MiB / 83976 kB<br>
81 MiB / 83964 kB<br>
81 MiB / 83724 kB<br>
<br>
As one would expect, 4 ranks needs more memory than 2 ranks, but quite<br>
unexpectedly, 1 rank needs more than 2! I guess you are right: the 1-rank-case<br>
is not optimised and quite frankly, I don't mind: I only ever run small tests<br>
with one rank. Unfortunately, trying to create the simplest possible scenario<br>
to illustrate my point, I used a small DA and just one rank, precisely to<br>
avoid the case where the excess memory would be due to MPI buffers or such.<br>
Looks like my plan backfired. ;)<br>
<br>
But even still, my 53 MiB lattice, without any vectors created, takes 280 or<br>
320 MiB of memory – down to <6 from the original 6.6.<br>
<br>
I will test with 3.3 later today if I have the time, but I'm pretty sure<br>
things were "better" there.<br>
<div class="HOEnZb"><div class="h5"><br>
On Monday 21 October 2013 15:23:01 Barry Smith wrote:<br>
> Matt,<br>
><br>
> I think you are running on 1 process where the DMDA doesn't have an<br>
> optimized path, when I run on 2 processes the numbers indicate nothing<br>
> proportional to dof* number of local points<br>
><br>
> dof = 12<br>
> ~/Src/petsc/test master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep<br>
> VecScatter [0] 7 21344 VecScatterCreate()<br>
> [0] 2 32 VecScatterCreateCommon_PtoS()<br>
> [0] 39 182480 VecScatterCreate_PtoS()<br>
><br>
> dof = 8<br>
> ~/Src/petsc/test master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep<br>
> VecScatter [0] 7 21344 VecScatterCreate()<br>
> [0] 2 32 VecScatterCreateCommon_PtoS()<br>
> [0] 39 176080 VecScatterCreate_PtoS()<br>
><br>
> dof = 4<br>
><br>
> ~/Src/petsc/test master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep<br>
> VecScatter [0] 7 21344 VecScatterCreate()<br>
> [0] 2 32 VecScatterCreateCommon_PtoS()<br>
> [0] 39 169680 VecScatterCreate_PtoS()<br>
><br>
> dof = 2<br>
> ~/Src/petsc/test master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep<br>
> VecScatter [0] 7 21344 VecScatterCreate()<br>
> [0] 2 32 VecScatterCreateCommon_PtoS()<br>
> [0] 39 166480 VecScatterCreate_PtoS()<br>
><br>
> dof =2 grid is 50 by 50 instead of 100 by 100<br>
><br>
> ~/Src/petsc/test master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep<br>
> VecScatter [0] 7 6352 VecScatterCreate()<br>
> [0] 2 32 VecScatterCreateCommon_PtoS()<br>
> [0] 39 43952 VecScatterCreate_PtoS()<br>
><br>
> The IS creation in the DMDA is far more troubling<br>
><br>
> /Src/petsc/test master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep IS<br>
><br>
> dof = 2<br>
><br>
> [0] 1 20400 ISBlockSetIndices_Block()<br>
> [0] 15 3760 ISCreate()<br>
> [0] 4 128 ISCreate_Block()<br>
> [0] 1 16 ISCreate_Stride()<br>
> [0] 2 81600 ISGetIndices_Block()<br>
> [0] 1 20400 ISLocalToGlobalMappingBlock()<br>
> [0] 7 42016 ISLocalToGlobalMappingCreate()<br>
><br>
> dof = 4<br>
><br>
> ~/Src/petsc/test master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep IS<br>
> [0] 1 20400 ISBlockSetIndices_Block()<br>
> [0] 15 3760 ISCreate()<br>
> [0] 4 128 ISCreate_Block()<br>
> [0] 1 16 ISCreate_Stride()<br>
> [0] 2 163200 ISGetIndices_Block()<br>
> [0] 1 20400 ISLocalToGlobalMappingBlock()<br>
> [0] 7 82816 ISLocalToGlobalMappingCreate()<br>
><br>
> dof = 8<br>
><br>
> ~/Src/petsc/test master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep IS<br>
> [0] 1 20400 ISBlockSetIndices_Block()<br>
> [0] 15 3760 ISCreate()<br>
> [0] 4 128 ISCreate_Block()<br>
> [0] 1 16 ISCreate_Stride()<br>
> [0] 2 326400 ISGetIndices_Block()<br>
> [0] 1 20400 ISLocalToGlobalMappingBlock()<br>
> [0] 7 164416 ISLocalToGlobalMappingCreate()<br>
><br>
> dof = 12<br>
> ~/Src/petsc/test master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep IS<br>
> [0] 1 20400 ISBlockSetIndices_Block()<br>
> [0] 15 3760 ISCreate()<br>
> [0] 4 128 ISCreate_Block()<br>
> [0] 1 16 ISCreate_Stride()<br>
> [0] 2 489600 ISGetIndices_Block()<br>
> [0] 1 20400 ISLocalToGlobalMappingBlock()<br>
> [0] 7 246016 ISLocalToGlobalMappingCreate()<br>
><br>
> Here the accessing of indices is at the point level (as well as block) and<br>
> hence memory usage is proportional to dof* local number of grid points. Of<br>
> course it is still only proportional to the vector size. There is some<br>
> improvement we could make it; with a lot of refactoring we can remove the<br>
> dof* completely, with a little refactoring we can bring it down to a single<br>
> dof*local number of grid points.<br>
><br>
> I cannot understand why you are seeing memory usage 7 times more than a<br>
> vector. That seems like a lot.<br>
><br>
> Barry<br>
><br>
> On Oct 21, 2013, at 11:32 AM, Barry Smith <<a href="mailto:bsmith@mcs.anl.gov">bsmith@mcs.anl.gov</a>> wrote:<br>
> > The PETSc DMDA object greedily allocates several arrays of data used to<br>
> > set up the communication and other things like local to global mappings<br>
> > even before you create any vectors. This is why you see this big bump<br>
> > in memory usage.<br>
> ><br>
> > BUT I don't think it should be any worse in 3.4 than in 3.3 or earlier;<br>
> > at least we did not intend to make it worse. Are you sure it is using<br>
> > more memory than in 3.3<br>
> ><br>
> > In order for use to decrease the memory usage of the DMDA setup it would<br>
> > be helpful if we knew which objects created within it used the most<br>
> > memory. There is some sloppiness in that routine of not reusing memory<br>
> > as well as could be, not sure how much difference that would make.<br>
> ><br>
> ><br>
> > Barry<br>
> ><br>
> > On Oct 21, 2013, at 7:02 AM, Juha Jäykkä <<a href="mailto:juhaj@iki.fi">juhaj@iki.fi</a>> wrote:<br>
> >> Dear list members,<br>
> >><br>
> >> I have noticed strange memory consumption after upgrading to 3.4 series.<br>
> >> I<br>
> >> never had time to properly investigate, but here is what happens [yes,<br>
> >> this<br>
> >> might be a petsc4py issue, but I doubt it] is<br>
> >><br>
> >> # helpers contains _ProcessMemoryInfoProc routine which just digs the<br>
> >> memory # usage data from /proc<br>
> >> import helpers<br>
> >> procdata=helpers._ProcessMemoryInfoProc()<br>
> >> print procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]<br>
> >> from petsc4py import PETSc<br>
> >> procdata=helpers._ProcessMemoryInfoProc()<br>
> >> print procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]<br>
> >> da = PETSc.DA().create(sizes=[100,100,100],<br>
> >><br>
> >> proc_sizes=[PETSc.DECIDE,PETSc.DECIDE,PETSc.DECIDE],<br>
> >> boundary_type=[3,0,0],<br>
> >> stencil_type=PETSc.DA.StencilType.BOX,<br>
> >> dof=7, stencil_width=1, comm=PETSc.COMM_WORLD)<br>
> >><br>
> >> procdata=helpers._ProcessMemoryInfoProc()<br>
> >> print procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]<br>
> >> vec=da.createGlobalVec()<br>
> >> procdata=helpers._ProcessMemoryInfoProc()<br>
> >> print procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]<br>
> >><br>
> >> outputs<br>
> >><br>
> >> 48 MiB / 49348 kB<br>
> >> 48 MiB / 49360 kB<br>
> >> 381 MiB / 446228 kB<br>
> >> 435 MiB / 446228 kB<br>
> >><br>
> >> Which is odd: size of the actual data to be stored in the da is just<br>
> >> about 56 megabytes, so why does creating the da consume 7 times that?<br>
> >> And why does the DA reserve the memory in the first place? I thought<br>
> >> memory only gets allocated once an associated vector is created and it<br>
> >> indeed looks like the<br>
> >> createGlobalVec call does indeed allocate the right amount of data. But<br>
> >> what is that 330 MiB that DA().create() consumes? [It's actually the<br>
> >> .setUp() method that does the consuming, but that's not of much use as<br>
> >> it needs to be called before a vector can be created.]<br>
> >><br>
> >> Cheers,<br>
> >> Juha<br>
</div></div></blockquote></div><br><br clear="all"><div><br></div>-- <br>What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead.<br>
-- Norbert Wiener
</div></div>