[petsc-users] memory use of a DMDA

Tue Oct 22 03:57:15 CDT 2013

Barry,

I seem to have touched a topic which goes way past my knowledge of PETSc 
internals, but it's very nice to see a thorough response nevertheless. Thank 
you. And Matthew, too.

After reading your suspicions about number of ranks, I tried with 1, 2 and 4 
and the memory use indeed seems to go down from 1:

juhaj at dhcp071> CMD='import helpers; procdata=helpers._ProcessMemoryInfoProc(); 
print procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]; from petsc4py 
import PETSc; procdata=helpers._ProcessMemoryInfoProc(); print 
procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]; da = 
PETSc.DA().create(sizes=[100,100,100], 
proc_sizes=[PETSc.DECIDE,PETSc.DECIDE,PETSc.DECIDE], boundary_type=[3,0,0], 
stencil_type=PETSc.DA.StencilType.BOX, dof=7, stencil_width=1, 
comm=PETSc.COMM_WORLD); procdata=helpers._ProcessMemoryInfoProc(); print 
procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]'
juhaj at dhcp071> mpirun -np 1 python -c "$CMD"
21 MiB / 22280 kB
21 MiB / 22304 kB
354 MiB / 419176 kB
juhaj at dhcp071> mpirun -np 2 python -c "$CMD"
22 MiB / 23276 kB
22 MiB / 23020 kB
22 MiB / 23300 kB
22 MiB / 23044 kB
141 MiB / 145324 kB
141 MiB / 145068 kB
juhaj at dhcp071> mpirun -np 4 python -c "$CMD"
22 MiB / 23292 kB
22 MiB / 23036 kB
22 MiB / 23316 kB
22 MiB / 23060 kB
22 MiB / 23316 kB
22 MiB / 23340 kB
22 MiB / 23044 kB
22 MiB / 23068 kB
81 MiB / 83716 kB
82 MiB / 83976 kB
81 MiB / 83964 kB
81 MiB / 83724 kB

As one would expect, 4 ranks needs more memory than 2 ranks, but quite 
unexpectedly, 1 rank needs more than 2! I guess you are right: the 1-rank-case 
is not optimised and quite frankly, I don't mind: I only ever run small tests 
with one rank. Unfortunately, trying to create the simplest possible scenario 
to illustrate my point, I used a small DA and just one rank, precisely to 
avoid the case where the excess memory would be due to MPI buffers or such. 
Looks like my plan backfired. ;)

But even still, my 53 MiB lattice, without any vectors created, takes 280 or 
320 MiB of memory – down to <6 from the original 6.6.

I will test with 3.3 later today if I have the time, but I'm pretty sure 
things were "better" there.

On Monday 21 October 2013 15:23:01 Barry Smith wrote:
>    Matt,
> 
>      I think you are running on 1 process where the DMDA doesn't have an
> optimized path, when I run on 2 processes the numbers indicate nothing
> proportional to dof* number of local points
> 
> dof = 12
> ~/Src/petsc/test  master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep
> VecScatter [0] 7 21344 VecScatterCreate()
> [0] 2 32 VecScatterCreateCommon_PtoS()
> [0] 39 182480 VecScatterCreate_PtoS()
> 
> dof = 8
> ~/Src/petsc/test  master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep
> VecScatter [0] 7 21344 VecScatterCreate()
> [0] 2 32 VecScatterCreateCommon_PtoS()
> [0] 39 176080 VecScatterCreate_PtoS()
> 
> dof = 4
> 
> ~/Src/petsc/test  master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep
> VecScatter [0] 7 21344 VecScatterCreate()
> [0] 2 32 VecScatterCreateCommon_PtoS()
> [0] 39 169680 VecScatterCreate_PtoS()
> 
> dof = 2
> ~/Src/petsc/test  master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep
> VecScatter [0] 7 21344 VecScatterCreate()
> [0] 2 32 VecScatterCreateCommon_PtoS()
> [0] 39 166480 VecScatterCreate_PtoS()
> 
> dof =2 grid is 50 by 50 instead of 100 by 100
> 
> ~/Src/petsc/test  master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep
> VecScatter [0] 7 6352 VecScatterCreate()
> [0] 2 32 VecScatterCreateCommon_PtoS()
> [0] 39 43952 VecScatterCreate_PtoS()
> 
> The IS creation in the DMDA is far more troubling
> 
> /Src/petsc/test  master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep IS
> 
> dof = 2
> 
> [0] 1 20400 ISBlockSetIndices_Block()
> [0] 15 3760 ISCreate()
> [0] 4 128 ISCreate_Block()
> [0] 1 16 ISCreate_Stride()
> [0] 2 81600 ISGetIndices_Block()
> [0] 1 20400 ISLocalToGlobalMappingBlock()
> [0] 7 42016 ISLocalToGlobalMappingCreate()
> 
> dof = 4
> 
> ~/Src/petsc/test  master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep IS
> [0] 1 20400 ISBlockSetIndices_Block()
> [0] 15 3760 ISCreate()
> [0] 4 128 ISCreate_Block()
> [0] 1 16 ISCreate_Stride()
> [0] 2 163200 ISGetIndices_Block()
> [0] 1 20400 ISLocalToGlobalMappingBlock()
> [0] 7 82816 ISLocalToGlobalMappingCreate()
> 
> dof = 8
> 
> ~/Src/petsc/test  master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep IS
> [0] 1 20400 ISBlockSetIndices_Block()
> [0] 15 3760 ISCreate()
> [0] 4 128 ISCreate_Block()
> [0] 1 16 ISCreate_Stride()
> [0] 2 326400 ISGetIndices_Block()
> [0] 1 20400 ISLocalToGlobalMappingBlock()
> [0] 7 164416 ISLocalToGlobalMappingCreate()
> 
> dof = 12
> ~/Src/petsc/test  master $ petscmpiexec -n 2 ./ex1 -malloc_log | grep IS
> [0] 1 20400 ISBlockSetIndices_Block()
> [0] 15 3760 ISCreate()
> [0] 4 128 ISCreate_Block()
> [0] 1 16 ISCreate_Stride()
> [0] 2 489600 ISGetIndices_Block()
> [0] 1 20400 ISLocalToGlobalMappingBlock()
> [0] 7 246016 ISLocalToGlobalMappingCreate()
> 
> Here the accessing of indices is at the point level (as well as block) and
> hence memory usage is proportional to dof* local number of grid points. Of
> course it is still only proportional to the vector size. There is some
> improvement we could make it; with a lot of refactoring we can remove the
> dof* completely, with a little refactoring we can bring it down to a single
> dof*local number of grid points.
> 
>    I cannot understand why you are seeing memory usage 7 times more than a
> vector. That seems like a lot.
> 
>    Barry
> 
> On Oct 21, 2013, at 11:32 AM, Barry Smith <bsmith at mcs.anl.gov> wrote:
> >   The PETSc DMDA object greedily allocates several arrays of data used to
> >   set up the communication and other things like local to global mappings
> >   even before you create any vectors. This is why you see this big bump
> >   in memory usage.
> >   
> >   BUT I don't think it should be any worse in 3.4 than in 3.3 or earlier;
> >   at least we did not intend to make it worse. Are you sure it is using
> >   more memory than in 3.3
> >   
> >   In order for use to decrease the memory usage of the DMDA setup it would
> >   be helpful if we knew which objects created within it used the most
> >   memory.  There is some sloppiness in that routine of not reusing memory
> >   as well as could be, not sure how much difference that would make.
> >   
> >   
> >   Barry
> > 
> > On Oct 21, 2013, at 7:02 AM, Juha Jäykkä <juhaj at iki.fi> wrote:
> >> Dear list members,
> >> 
> >> I have noticed strange memory consumption after upgrading to 3.4 series.
> >> I
> >> never had time to properly investigate, but here is what happens [yes,
> >> this
> >> might be a petsc4py issue, but I doubt it] is
> >> 
> >> # helpers contains _ProcessMemoryInfoProc routine which just digs the
> >> memory # usage data from /proc
> >> import helpers
> >> procdata=helpers._ProcessMemoryInfoProc()
> >> print procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]
> >> from petsc4py import PETSc
> >> procdata=helpers._ProcessMemoryInfoProc()
> >> print procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]
> >> da = PETSc.DA().create(sizes=[100,100,100],
> >> 
> >>                      proc_sizes=[PETSc.DECIDE,PETSc.DECIDE,PETSc.DECIDE],
> >>                      boundary_type=[3,0,0],
> >>                      stencil_type=PETSc.DA.StencilType.BOX,
> >>                      dof=7, stencil_width=1, comm=PETSc.COMM_WORLD)
> >> 
> >> procdata=helpers._ProcessMemoryInfoProc()
> >> print procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]
> >> vec=da.createGlobalVec()
> >> procdata=helpers._ProcessMemoryInfoProc()
> >> print procdata.rss/2**20, "MiB /", procdata.os_specific[3][1]
> >> 
> >> outputs
> >> 
> >> 48 MiB / 49348 kB
> >> 48 MiB / 49360 kB
> >> 381 MiB / 446228 kB
> >> 435 MiB / 446228 kB
> >> 
> >> Which is odd: size of the actual data to be stored in the da is just
> >> about 56 megabytes, so why does creating the da consume 7 times that?
> >> And why does the DA reserve the memory in the first place? I thought
> >> memory only gets allocated once an associated vector is created and it
> >> indeed looks like the
> >> createGlobalVec call does indeed allocate the right amount of data. But
> >> what is that 330 MiB that DA().create() consumes? [It's actually the
> >> .setUp() method that does the consuming, but that's not of much use as
> >> it needs to be called before a vector can be created.]
> >> 
> >> Cheers,
> >> Juha