[petsc-users] Performance of the Telescope Multigrid Preconditioner
Dave May
dave.mayhem23 at gmail.com
Fri Oct 7 02:22:27 CDT 2016
On 7 October 2016 at 02:05, Matthew Knepley <knepley at gmail.com> wrote:
> On Thu, Oct 6, 2016 at 7:33 PM, frank <hengjiew at uci.edu> wrote:
>
>> Dear Dave,
>> Follow your advice, I solve the identical equation twice and time two
>> steps separately. The result is below:
>>
>> Test: 1024^3 grid points
>> Cores# reduction factor MG levels# time of 1st solve 2nd time
>> 4096 64 6 + 3
>> 3.85 1.75
>> 8192 128 5 + 3
>> 5.52 0.91
>> 16384 256 5 + 3 5.37
>> 0.52
>> 32768 512 5 + 4 3.03
>> 0.36
>> 32768 64 | 8 4 | 3 | 3 2.80
>> 0.43
>> 65536 1024 5 + 4 3.38
>> 0.59
>> 65536 32 | 32 4 | 4 | 3 2.14
>> 0.22
>>
>> I also attached the log_view info from all the run. The file is names by
>> the cores# + reduction factor.
>> The ksp_view and petsc_options for the 1st run are also included. Others
>> are similar. The only differences are the reduction factor and mg levels.
>>
>> ** The time for the 1st solve is generally much larger. Is this because
>> the ksp solver on the sub-communicator is set up during the 1st solve?
>>
>
Yes, but it's not just the setup for the KSP on the sub-comm.
There is additional setup required,
[1] creating the sub-comm <one time>
[2] creating the DM on the sub-comm <one time only>
[3] creating the scatter objects and nullspaces <one time only>
[3] repartitioning the matrix <every time the operator is changed>
>
> All setup is done in the first solve.
>
>
>> ** The time for 1st solve does not scale.
>> In practice, I am solving a variable coefficient Poisson equation. I
>> need to build the matrix every time step. Therefore, each step is similar
>> to the 1st solve which does not scale. Is there a way I can improve the
>> performance?
>>
>
> You could use rediscretization instead of Galerkin to produce the coarse
> operators.
>
Yes I can think of one option for improved performance, but I cannot tell
whether it will be beneficial because the logging isn't sufficiently fine
grained (and there is no easy way to get the info out of petsc).
I use PtAP to repartition the matrix, this could be consuming most of the
setup time in Telescope with your run. Such a repartitioning could be avoid
if you provided a method to create the operator on the coarse levels (what
Matt is suggesting). However, this requires you to be able to define your
coefficients on the coarse grid. This will most likely reduce setup time,
but your coarse grid operators (now re-discretized) are likely to be less
effective than those generated via Galerkin coarsening.
>
>
>> ** The 2nd solve scales but not quite well for more than 16384 cores.
>>
>
> How well were you looking for? This is strong scaling, which is has an
> Amdahl's Law limit.
>
Is 1024^3 points your target (production run) resolution?
If it is not, then start doing the tests with your target resolution.
Setup time cf the solve time will always smaller and impact the scaling
less when you consider higher resolution problems.
>
>
>> It seems to me that the performance depends on the tuning of MG
>> levels on the sub-communicator(s).
>>
>
Yes - absolutely.
> Is there some general strategies regarding how to distribute the
>> levels? or when to use multiple sub-communicators ?
>>
>
Yes, but there is nothing definite.
We don't have a performance model to guide these choices.
The optimal choice is dependent on the characteristics of your compute
nodes, the network, the form of the discrete operator, and the mesh
refinement factor used when creating the MG hierarchy.
It's a bit complicated.
I have found when using meshes with a refinement factor of 2, using a
reduction factor of 64 within telescope is effective.
I would suggest experimenting with the refinement factor. If your
coefficients are smooth, you can probably refine your mesh for MG by a
factor of 4 (rather than the default of 2). Galerkin will still provide
meaningful coarse grid operators.
Always coarsen the problem until you have ~1 DOF per core before
reparititon the operator via Telescope. Don't use a reduction factor which
will only allow 1 new additional MG level to be defined on the sub-comm.
e.g. if you use meshes refined by 2x, on the coarse level, use a reduction
factor of 64.
Without a performance model, the optimal level to invoke repartitioning and
how aggressively the communicator size is reduced by cannot be determined
apriori. Experimentation is the only way.
>
>
> Also, you use CG/MG when FMG by itself would probably be faster. Your
> smoother is likely not strong enough, and you
> should use something like V(2,2). There is a lot of tuning that is
> possible, but difficult to automate.
>
Matt's completely correct.
If we could automate this in a meaningful manner, we would have done so.
Thanks,
Dave
>
> Thanks,
>
> Matt
>
>
>> Thank you.
>>
>> Regards,
>> Frank
>>
>>
>>
>>
>>
>> On 10/04/2016 12:56 PM, Dave May wrote:
>>
>>
>>
>> On Tuesday, 4 October 2016, frank <hengjiew at uci.edu> wrote:
>>
>>> Hi,
>>> This question is follow-up of the thread "Question about memory usage in
>>> Multigrid preconditioner".
>>> I used to have the "Out of Memory(OOM)" problem when using the
>>> CG+Telescope MG solver with 32768 cores. Adding the "-matrap 0;
>>> -matptap_scalable" option did solve that problem.
>>>
>>> Then I test the scalability by solving a 3d poisson eqn for 1 step. I
>>> used one sub-communicator in all the tests. The difference between the
>>> petsc options in those tests are: 1 the pc_telescope_reduction_factor; 2
>>> the number of multigrid levels in the up/down solver. The function
>>> "ksp_solve" is timed. It is kind of slow and doesn't scale at all.
>>>
>>> Test1: 512^3 grid points
>>> Core# telescope_reduction_factor MG levels# for up/down
>>> solver Time for KSPSolve (s)
>>> 512 8 4 /
>>> 3 6.2466
>>> 4096 64 5 /
>>> 3 0.9361
>>> 32768 64 4 /
>>> 3 4.8914
>>>
>>> Test2: 1024^3 grid points
>>> Core# telescope_reduction_factor MG levels# for up/down
>>> solver Time for KSPSolve (s)
>>> 4096 64 5 / 4
>>> 3.4139
>>> 8192 128 5 /
>>> 4 2.4196
>>> 16384 32 5 / 3
>>> 5.4150
>>> 32768 64 5 /
>>> 3 5.6067
>>> 65536 128 5 /
>>> 3 6.5219
>>>
>>
>> You have to be very careful how you interpret these numbers. Your solver
>> contains nested calls to KSPSolve, and unfortunately as a result the
>> numbers you report include setup time. This will remain true even if you
>> call KSPSetUp on the outermost KSP.
>>
>> Your email concerns scalability of the silver application, so let's focus
>> on that issue.
>>
>> The only way to clearly separate setup from solve time is to perform two
>> identical solves. The second solve will not require any setup. You should
>> monitor the second solve via a new PetscStage.
>>
>> This was what I did in the telescope paper. It was the only way to
>> understand the setup cost (and scaling) cf the solve time (and scaling).
>>
>> Thanks
>> Dave
>>
>>
>>
>>> I guess I didn't set the MG levels properly. What would be the efficient
>>> way to arrange the MG levels?
>>> Also which preconditionr at the coarse mesh of the 2nd communicator
>>> should I use to improve the performance?
>>>
>>> I attached the test code and the petsc options file for the 1024^3 cube
>>> with 32768 cores.
>>>
>>> Thank you.
>>>
>>> Regards,
>>> Frank
>>>
>>>
>>>
>>>
>>>
>>>
>>> On 09/15/2016 03:35 AM, Dave May wrote:
>>>
>>> HI all,
>>>
>>> I the only unexpected memory usage I can see is associated with the call
>>> to MatPtAP().
>>> Here is something you can try immediately.
>>> Run your code with the additional options
>>> -matrap 0 -matptap_scalable
>>>
>>> I didn't realize this before, but the default behaviour of MatPtAP in
>>> parallel is actually to to explicitly form the transpose of P (e.g.
>>> assemble R = P^T) and then compute R.A.P.
>>> You don't want to do this. The option -matrap 0 resolves this issue.
>>>
>>> The implementation of P^T.A.P has two variants.
>>> The scalable implementation (with respect to memory usage) is selected
>>> via the second option -matptap_scalable.
>>>
>>> Try it out - I see a significant memory reduction using these options
>>> for particular mesh sizes / partitions.
>>>
>>> I've attached a cleaned up version of the code you sent me.
>>> There were a number of memory leaks and other issues.
>>> The main points being
>>> * You should call DMDAVecGetArrayF90() before VecAssembly{Begin,End}
>>> * You should call PetscFinalize(), otherwise the option -log_summary
>>> (-log_view) will not display anything once the program has completed.
>>>
>>>
>>> Thanks,
>>> Dave
>>>
>>>
>>> On 15 September 2016 at 08:03, Hengjie Wang <hengjiew at uci.edu> wrote:
>>>
>>>> Hi Dave,
>>>>
>>>> Sorry, I should have put more comment to explain the code.
>>>> The number of process in each dimension is the same: Px = Py=Pz=P. So
>>>> is the domain size.
>>>> So if the you want to run the code for a 512^3 grid points on 16^3
>>>> cores, you need to set "-N 512 -P 16" in the command line.
>>>> I add more comments and also fix an error in the attached code. ( The
>>>> error only effects the accuracy of solution but not the memory usage. )
>>>>
>>>> Thank you.
>>>> Frank
>>>>
>>>>
>>>> On 9/14/2016 9:05 PM, Dave May wrote:
>>>>
>>>>
>>>>
>>>> On Thursday, 15 September 2016, Dave May <dave.mayhem23 at gmail.com>
>>>> wrote:
>>>>
>>>>>
>>>>>
>>>>> On Thursday, 15 September 2016, frank <hengjiew at uci.edu> wrote:
>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> I write a simple code to re-produce the error. I hope this can help
>>>>>> to diagnose the problem.
>>>>>> The code just solves a 3d poisson equation.
>>>>>>
>>>>>
>>>>> Why is the stencil width a runtime parameter?? And why is the default
>>>>> value 2? For 7-pnt FD Laplace, you only need a stencil width of 1.
>>>>>
>>>>> Was this choice made to mimic something in the real application code?
>>>>>
>>>>
>>>> Please ignore - I misunderstood your usage of the param set by -P
>>>>
>>>>
>>>>>
>>>>>
>>>>>>
>>>>>> I run the code on a 1024^3 mesh. The process partition is 32 * 32 *
>>>>>> 32. That's when I re-produce the OOM error. Each core has about 2G memory.
>>>>>> I also run the code on a 512^3 mesh with 16 * 16 * 16 processes. The
>>>>>> ksp solver works fine.
>>>>>> I attached the code, ksp_view_pre's output and my petsc option file.
>>>>>>
>>>>>> Thank you.
>>>>>> Frank
>>>>>>
>>>>>> On 09/09/2016 06:38 PM, Hengjie Wang wrote:
>>>>>>
>>>>>> Hi Barry,
>>>>>>
>>>>>> I checked. On the supercomputer, I had the option "-ksp_view_pre" but
>>>>>> it is not in file I sent you. I am sorry for the confusion.
>>>>>>
>>>>>> Regards,
>>>>>> Frank
>>>>>>
>>>>>> On Friday, September 9, 2016, Barry Smith <bsmith at mcs.anl.gov> wrote:
>>>>>>
>>>>>>>
>>>>>>> > On Sep 9, 2016, at 3:11 PM, frank <hengjiew at uci.edu> wrote:
>>>>>>> >
>>>>>>> > Hi Barry,
>>>>>>> >
>>>>>>> > I think the first KSP view output is from -ksp_view_pre. Before I
>>>>>>> submitted the test, I was not sure whether there would be OOM error or not.
>>>>>>> So I added both -ksp_view_pre and -ksp_view.
>>>>>>>
>>>>>>> But the options file you sent specifically does NOT list the
>>>>>>> -ksp_view_pre so how could it be from that?
>>>>>>>
>>>>>>> Sorry to be pedantic but I've spent too much time in the past
>>>>>>> trying to debug from incorrect information and want to make sure that the
>>>>>>> information I have is correct before thinking. Please recheck exactly what
>>>>>>> happened. Rerun with the exact input file you emailed if that is needed.
>>>>>>>
>>>>>>> Barry
>>>>>>>
>>>>>>> >
>>>>>>> > Frank
>>>>>>> >
>>>>>>> >
>>>>>>> > On 09/09/2016 12:38 PM, Barry Smith wrote:
>>>>>>> >> Why does ksp_view2.txt have two KSP views in it while
>>>>>>> ksp_view1.txt has only one KSPView in it? Did you run two different solves
>>>>>>> in the 2 case but not the one?
>>>>>>> >>
>>>>>>> >> Barry
>>>>>>> >>
>>>>>>> >>
>>>>>>> >>
>>>>>>> >>> On Sep 9, 2016, at 10:56 AM, frank <hengjiew at uci.edu> wrote:
>>>>>>> >>>
>>>>>>> >>> Hi,
>>>>>>> >>>
>>>>>>> >>> I want to continue digging into the memory problem here.
>>>>>>> >>> I did find a work around in the past, which is to use less cores
>>>>>>> per node so that each core has 8G memory. However this is deficient and
>>>>>>> expensive. I hope to locate the place that uses the most memory.
>>>>>>> >>>
>>>>>>> >>> Here is a brief summary of the tests I did in past:
>>>>>>> >>>> Test1: Mesh 1536*128*384 | Process Mesh 48*4*12
>>>>>>> >>> Maximum (over computational time) process memory:
>>>>>>> total 7.0727e+08
>>>>>>> >>> Current process memory:
>>>>>>> total 7.0727e+08
>>>>>>> >>> Maximum (over computational time) space PetscMalloc()ed: total
>>>>>>> 6.3908e+11
>>>>>>> >>> Current space PetscMalloc()ed:
>>>>>>> total 1.8275e+09
>>>>>>> >>>
>>>>>>> >>>> Test2: Mesh 1536*128*384 | Process Mesh 96*8*24
>>>>>>> >>> Maximum (over computational time) process memory:
>>>>>>> total 5.9431e+09
>>>>>>> >>> Current process memory:
>>>>>>> total 5.9431e+09
>>>>>>> >>> Maximum (over computational time) space PetscMalloc()ed: total
>>>>>>> 5.3202e+12
>>>>>>> >>> Current space PetscMalloc()ed:
>>>>>>> total 5.4844e+09
>>>>>>> >>>
>>>>>>> >>>> Test3: Mesh 3072*256*768 | Process Mesh 96*8*24
>>>>>>> >>> OOM( Out Of Memory ) killer of the supercomputer terminated
>>>>>>> the job during "KSPSolve".
>>>>>>> >>>
>>>>>>> >>> I attached the output of ksp_view( the third test's output is
>>>>>>> from ksp_view_pre ), memory_view and also the petsc options.
>>>>>>> >>>
>>>>>>> >>> In all the tests, each core can access about 2G memory. In
>>>>>>> test3, there are 4223139840 non-zeros in the matrix. This will consume
>>>>>>> about 1.74M, using double precision. Considering some extra memory used to
>>>>>>> store integer index, 2G memory should still be way enough.
>>>>>>> >>>
>>>>>>> >>> Is there a way to find out which part of KSPSolve uses the most
>>>>>>> memory?
>>>>>>> >>> Thank you so much.
>>>>>>> >>>
>>>>>>> >>> BTW, there are 4 options remains unused and I don't understand
>>>>>>> why they are omitted:
>>>>>>> >>> -mg_coarse_telescope_mg_coarse_ksp_type value: preonly
>>>>>>> >>> -mg_coarse_telescope_mg_coarse_pc_type value: bjacobi
>>>>>>> >>> -mg_coarse_telescope_mg_levels_ksp_max_it value: 1
>>>>>>> >>> -mg_coarse_telescope_mg_levels_ksp_type value: richardson
>>>>>>> >>>
>>>>>>> >>>
>>>>>>> >>> Regards,
>>>>>>> >>> Frank
>>>>>>> >>>
>>>>>>> >>> On 07/13/2016 05:47 PM, Dave May wrote:
>>>>>>> >>>>
>>>>>>> >>>> On 14 July 2016 at 01:07, frank <hengjiew at uci.edu> wrote:
>>>>>>> >>>> Hi Dave,
>>>>>>> >>>>
>>>>>>> >>>> Sorry for the late reply.
>>>>>>> >>>> Thank you so much for your detailed reply.
>>>>>>> >>>>
>>>>>>> >>>> I have a question about the estimation of the memory usage.
>>>>>>> There are 4223139840 allocated non-zeros and 18432 MPI processes. Double
>>>>>>> precision is used. So the memory per process is:
>>>>>>> >>>> 4223139840 * 8bytes / 18432 / 1024 / 1024 = 1.74M ?
>>>>>>> >>>> Did I do sth wrong here? Because this seems too small.
>>>>>>> >>>>
>>>>>>> >>>> No - I totally f***ed it up. You are correct. That'll teach me
>>>>>>> for fumbling around with my iphone calculator and not using my brain. (Note
>>>>>>> that to convert to MB just divide by 1e6, not 1024^2 - although I
>>>>>>> apparently cannot convert between units correctly....)
>>>>>>> >>>>
>>>>>>> >>>> From the PETSc objects associated with the solver, It looks
>>>>>>> like it _should_ run with 2GB per MPI rank. Sorry for my mistake.
>>>>>>> Possibilities are: somewhere in your usage of PETSc you've introduced a
>>>>>>> memory leak; PETSc is doing a huge over allocation (e.g. as per our
>>>>>>> discussion of MatPtAP); or in your application code there are other objects
>>>>>>> you have forgotten to log the memory for.
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> I am running this job on Bluewater
>>>>>>> >>>> I am using the 7 points FD stencil in 3D.
>>>>>>> >>>>
>>>>>>> >>>> I thought so on both counts.
>>>>>>> >>>>
>>>>>>> >>>> I apologize that I made a stupid mistake in computing the
>>>>>>> memory per core. My settings render each core can access only 2G memory on
>>>>>>> average instead of 8G which I mentioned in previous email. I re-run the job
>>>>>>> with 8G memory per core on average and there is no "Out Of Memory" error. I
>>>>>>> would do more test to see if there is still some memory issue.
>>>>>>> >>>>
>>>>>>> >>>> Ok. I'd still like to know where the memory was being used
>>>>>>> since my estimates were off.
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> Thanks,
>>>>>>> >>>> Dave
>>>>>>> >>>>
>>>>>>> >>>> Regards,
>>>>>>> >>>> Frank
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>>
>>>>>>> >>>> On 07/11/2016 01:18 PM, Dave May wrote:
>>>>>>> >>>>> Hi Frank,
>>>>>>> >>>>>
>>>>>>> >>>>>
>>>>>>> >>>>> On 11 July 2016 at 19:14, frank <hengjiew at uci.edu> wrote:
>>>>>>> >>>>> Hi Dave,
>>>>>>> >>>>>
>>>>>>> >>>>> I re-run the test using bjacobi as the preconditioner on the
>>>>>>> coarse mesh of telescope. The Grid is 3072*256*768 and process mesh is
>>>>>>> 96*8*24. The petsc option file is attached.
>>>>>>> >>>>> I still got the "Out Of Memory" error. The error occurred
>>>>>>> before the linear solver finished one step. So I don't have the full info
>>>>>>> from ksp_view. The info from ksp_view_pre is attached.
>>>>>>> >>>>>
>>>>>>> >>>>> Okay - that is essentially useless (sorry)
>>>>>>> >>>>>
>>>>>>> >>>>> It seems to me that the error occurred when the decomposition
>>>>>>> was going to be changed.
>>>>>>> >>>>>
>>>>>>> >>>>> Based on what information?
>>>>>>> >>>>> Running with -info would give us more clues, but will create a
>>>>>>> ton of output.
>>>>>>> >>>>> Please try running the case which failed with -info
>>>>>>> >>>>> I had another test with a grid of 1536*128*384 and the same
>>>>>>> process mesh as above. There was no error. The ksp_view info is attached
>>>>>>> for comparison.
>>>>>>> >>>>> Thank you.
>>>>>>> >>>>>
>>>>>>> >>>>>
>>>>>>> >>>>> [3] Here is my crude estimate of your memory usage.
>>>>>>> >>>>> I'll target the biggest memory hogs only to get an order of
>>>>>>> magnitude estimate
>>>>>>> >>>>>
>>>>>>> >>>>> * The Fine grid operator contains 4223139840 non-zeros --> 1.8
>>>>>>> GB per MPI rank assuming double precision.
>>>>>>> >>>>> The indices for the AIJ could amount to another 0.3 GB
>>>>>>> (assuming 32 bit integers)
>>>>>>> >>>>>
>>>>>>> >>>>> * You use 5 levels of coarsening, so the other operators
>>>>>>> should represent (collectively)
>>>>>>> >>>>> 2.1 / 8 + 2.1/8^2 + 2.1/8^3 + 2.1/8^4 ~ 300 MB per MPI rank
>>>>>>> on the communicator with 18432 ranks.
>>>>>>> >>>>> The coarse grid should consume ~ 0.5 MB per MPI rank on the
>>>>>>> communicator with 18432 ranks.
>>>>>>> >>>>>
>>>>>>> >>>>> * You use a reduction factor of 64, making the new
>>>>>>> communicator with 288 MPI ranks.
>>>>>>> >>>>> PCTelescope will first gather a temporary matrix associated
>>>>>>> with your coarse level operator assuming a comm size of 288 living on the
>>>>>>> comm with size 18432.
>>>>>>> >>>>> This matrix will require approximately 0.5 * 64 = 32 MB per
>>>>>>> core on the 288 ranks.
>>>>>>> >>>>> This matrix is then used to form a new MPIAIJ matrix on the
>>>>>>> subcomm, thus require another 32 MB per rank.
>>>>>>> >>>>> The temporary matrix is now destroyed.
>>>>>>> >>>>>
>>>>>>> >>>>> * Because a DMDA is detected, a permutation matrix is
>>>>>>> assembled.
>>>>>>> >>>>> This requires 2 doubles per point in the DMDA.
>>>>>>> >>>>> Your coarse DMDA contains 92 x 16 x 48 points.
>>>>>>> >>>>> Thus the permutation matrix will require < 1 MB per MPI rank
>>>>>>> on the sub-comm.
>>>>>>> >>>>>
>>>>>>> >>>>> * Lastly, the matrix is permuted. This uses MatPtAP(), but the
>>>>>>> resulting operator will have the same memory footprint as the unpermuted
>>>>>>> matrix (32 MB). At any stage in PCTelescope, only 2 operators of size 32 MB
>>>>>>> are held in memory when the DMDA is provided.
>>>>>>> >>>>>
>>>>>>> >>>>> From my rough estimates, the worst case memory foot print for
>>>>>>> any given core, given your options is approximately
>>>>>>> >>>>> 2100 MB + 300 MB + 32 MB + 32 MB + 1 MB = 2465 MB
>>>>>>> >>>>> This is way below 8 GB.
>>>>>>> >>>>>
>>>>>>> >>>>> Note this estimate completely ignores:
>>>>>>> >>>>> (1) the memory required for the restriction operator,
>>>>>>> >>>>> (2) the potential growth in the number of non-zeros per row
>>>>>>> due to Galerkin coarsening (I wished -ksp_view_pre reported the output from
>>>>>>> MatView so we could see the number of non-zeros required by the coarse
>>>>>>> level operators)
>>>>>>> >>>>> (3) all temporary vectors required by the CG solver, and those
>>>>>>> required by the smoothers.
>>>>>>> >>>>> (4) internal memory allocated by MatPtAP
>>>>>>> >>>>> (5) memory associated with IS's used within PCTelescope
>>>>>>> >>>>>
>>>>>>> >>>>> So either I am completely off in my estimates, or you have not
>>>>>>> carefully estimated the memory usage of your application code. Hopefully
>>>>>>> others might examine/correct my rough estimates
>>>>>>> >>>>>
>>>>>>> >>>>> Since I don't have your code I cannot access the latter.
>>>>>>> >>>>> Since I don't have access to the same machine you are running
>>>>>>> on, I think we need to take a step back.
>>>>>>> >>>>>
>>>>>>> >>>>> [1] What machine are you running on? Send me a URL if its
>>>>>>> available
>>>>>>> >>>>>
>>>>>>> >>>>> [2] What discretization are you using? (I am guessing a scalar
>>>>>>> 7 point FD stencil)
>>>>>>> >>>>> If it's a 7 point FD stencil, we should be able to examine the
>>>>>>> memory usage of your solver configuration using a standard, light weight
>>>>>>> existing PETSc example, run on your machine at the same scale.
>>>>>>> >>>>> This would hopefully enable us to correctly evaluate the
>>>>>>> actual memory usage required by the solver configuration you are using.
>>>>>>> >>>>>
>>>>>>> >>>>> Thanks,
>>>>>>> >>>>> Dave
>>>>>>> >>>>>
>>>>>>> >>>>>
>>>>>>> >>>>> Frank
>>>>>>> >>>>>
>>>>>>> >>>>>
>>>>>>> >>>>>
>>>>>>> >>>>>
>>>>>>> >>>>> On 07/08/2016 10:38 PM, Dave May wrote:
>>>>>>> >>>>>>
>>>>>>> >>>>>> On Saturday, 9 July 2016, frank <hengjiew at uci.edu> wrote:
>>>>>>> >>>>>> Hi Barry and Dave,
>>>>>>> >>>>>>
>>>>>>> >>>>>> Thank both of you for the advice.
>>>>>>> >>>>>>
>>>>>>> >>>>>> @Barry
>>>>>>> >>>>>> I made a mistake in the file names in last email. I attached
>>>>>>> the correct files this time.
>>>>>>> >>>>>> For all the three tests, 'Telescope' is used as the coarse
>>>>>>> preconditioner.
>>>>>>> >>>>>>
>>>>>>> >>>>>> == Test1: Grid: 1536*128*384, Process Mesh: 48*4*12
>>>>>>> >>>>>> Part of the memory usage: Vector 125 124
>>>>>>> 3971904 0.
>>>>>>> >>>>>> Matrix 101
>>>>>>> 101 9462372 0
>>>>>>> >>>>>>
>>>>>>> >>>>>> == Test2: Grid: 1536*128*384, Process Mesh: 96*8*24
>>>>>>> >>>>>> Part of the memory usage: Vector 125 124
>>>>>>> 681672 0.
>>>>>>> >>>>>> Matrix 101
>>>>>>> 101 1462180 0.
>>>>>>> >>>>>>
>>>>>>> >>>>>> In theory, the memory usage in Test1 should be 8 times of
>>>>>>> Test2. In my case, it is about 6 times.
>>>>>>> >>>>>>
>>>>>>> >>>>>> == Test3: Grid: 3072*256*768, Process Mesh: 96*8*24.
>>>>>>> Sub-domain per process: 32*32*32
>>>>>>> >>>>>> Here I get the out of memory error.
>>>>>>> >>>>>>
>>>>>>> >>>>>> I tried to use -mg_coarse jacobi. In this way, I don't need
>>>>>>> to set -mg_coarse_ksp_type and -mg_coarse_pc_type explicitly, right?
>>>>>>> >>>>>> The linear solver didn't work in this case. Petsc output some
>>>>>>> errors.
>>>>>>> >>>>>>
>>>>>>> >>>>>> @Dave
>>>>>>> >>>>>> In test3, I use only one instance of 'Telescope'. On the
>>>>>>> coarse mesh of 'Telescope', I used LU as the preconditioner instead of SVD.
>>>>>>> >>>>>> If my set the levels correctly, then on the last coarse mesh
>>>>>>> of MG where it calls 'Telescope', the sub-domain per process is 2*2*2.
>>>>>>> >>>>>> On the last coarse mesh of 'Telescope', there is only one
>>>>>>> grid point per process.
>>>>>>> >>>>>> I still got the OOM error. The detailed petsc option file is
>>>>>>> attached.
>>>>>>> >>>>>>
>>>>>>> >>>>>> Do you understand the expected memory usage for the
>>>>>>> particular parallel LU implementation you are using? I don't (seriously).
>>>>>>> Replace LU with bjacobi and re-run this test. My point about solver
>>>>>>> debugging is still valid.
>>>>>>> >>>>>>
>>>>>>> >>>>>> And please send the result of KSPView so we can see what is
>>>>>>> actually used in the computations
>>>>>>> >>>>>>
>>>>>>> >>>>>> Thanks
>>>>>>> >>>>>> Dave
>>>>>>> >>>>>>
>>>>>>> >>>>>>
>>>>>>> >>>>>> Thank you so much.
>>>>>>> >>>>>>
>>>>>>> >>>>>> Frank
>>>>>>> >>>>>>
>>>>>>> >>>>>>
>>>>>>> >>>>>>
>>>>>>> >>>>>> On 07/06/2016 02:51 PM, Barry Smith wrote:
>>>>>>> >>>>>> On Jul 6, 2016, at 4:19 PM, frank <hengjiew at uci.edu> wrote:
>>>>>>> >>>>>>
>>>>>>> >>>>>> Hi Barry,
>>>>>>> >>>>>>
>>>>>>> >>>>>> Thank you for you advice.
>>>>>>> >>>>>> I tried three test. In the 1st test, the grid is 3072*256*768
>>>>>>> and the process mesh is 96*8*24.
>>>>>>> >>>>>> The linear solver is 'cg' the preconditioner is 'mg' and
>>>>>>> 'telescope' is used as the preconditioner at the coarse mesh.
>>>>>>> >>>>>> The system gives me the "Out of Memory" error before the
>>>>>>> linear system is completely solved.
>>>>>>> >>>>>> The info from '-ksp_view_pre' is attached. I seems to me that
>>>>>>> the error occurs when it reaches the coarse mesh.
>>>>>>> >>>>>>
>>>>>>> >>>>>> The 2nd test uses a grid of 1536*128*384 and process mesh is
>>>>>>> 96*8*24. The 3rd test uses the
>>>>>>> same grid but a different process mesh 48*4*12.
>>>>>>> >>>>>> Are you sure this is right? The total matrix and vector
>>>>>>> memory usage goes from 2nd test
>>>>>>> >>>>>> Vector 384 383 8,193,712
>>>>>>> 0.
>>>>>>> >>>>>> Matrix 103 103 11,508,688
>>>>>>> 0.
>>>>>>> >>>>>> to 3rd test
>>>>>>> >>>>>> Vector 384 383 1,590,520
>>>>>>> 0.
>>>>>>> >>>>>> Matrix 103 103 3,508,664
>>>>>>> 0.
>>>>>>> >>>>>> that is the memory usage got smaller but if you have only
>>>>>>> 1/8th the processes and the same grid it should have gotten about 8 times
>>>>>>> bigger. Did you maybe cut the grid by a factor of 8 also? If so that still
>>>>>>> doesn't explain it because the memory usage changed by a factor of 5
>>>>>>> something for the vectors and 3 something for the matrices.
>>>>>>> >>>>>>
>>>>>>> >>>>>>
>>>>>>> >>>>>> The linear solver and petsc options in 2nd and 3rd tests are
>>>>>>> the same in 1st test. The linear solver works fine in both test.
>>>>>>> >>>>>> I attached the memory usage of the 2nd and 3rd tests. The
>>>>>>> memory info is from the option '-log_summary'. I tried to use
>>>>>>> '-momery_info' as you suggested, but in my case petsc treated it as an
>>>>>>> unused option. It output nothing about the memory. Do I need to add sth to
>>>>>>> my code so I can use '-memory_info'?
>>>>>>> >>>>>> Sorry, my mistake the option is -memory_view
>>>>>>> >>>>>>
>>>>>>> >>>>>> Can you run the one case with -memory_view and -mg_coarse
>>>>>>> jacobi -ksp_max_it 1 (just so it doesn't iterate forever) to see how much
>>>>>>> memory is used without the telescope? Also run case 2 the same way.
>>>>>>> >>>>>>
>>>>>>> >>>>>> Barry
>>>>>>> >>>>>>
>>>>>>> >>>>>>
>>>>>>> >>>>>>
>>>>>>> >>>>>> In both tests the memory usage is not large.
>>>>>>> >>>>>>
>>>>>>> >>>>>> It seems to me that it might be the 'telescope'
>>>>>>> preconditioner that allocated a lot of memory and caused the error in the
>>>>>>> 1st test.
>>>>>>> >>>>>> Is there is a way to show how much memory it allocated?
>>>>>>> >>>>>>
>>>>>>> >>>>>> Frank
>>>>>>> >>>>>>
>>>>>>> >>>>>> On 07/05/2016 03:37 PM, Barry Smith wrote:
>>>>>>> >>>>>> Frank,
>>>>>>> >>>>>>
>>>>>>> >>>>>> You can run with -ksp_view_pre to have it "view" the KSP
>>>>>>> before the solve so hopefully it gets that far.
>>>>>>> >>>>>>
>>>>>>> >>>>>> Please run the problem that does fit with -memory_info
>>>>>>> when the problem completes it will show the "high water mark" for PETSc
>>>>>>> allocated memory and total memory used. We first want to look at these
>>>>>>> numbers to see if it is using more memory than you expect. You could also
>>>>>>> run with say half the grid spacing to see how the memory usage scaled with
>>>>>>> the increase in grid points. Make the runs also with -log_view and send all
>>>>>>> the output from these options.
>>>>>>> >>>>>>
>>>>>>> >>>>>> Barry
>>>>>>> >>>>>>
>>>>>>> >>>>>> On Jul 5, 2016, at 5:23 PM, frank <hengjiew at uci.edu> wrote:
>>>>>>> >>>>>>
>>>>>>> >>>>>> Hi,
>>>>>>> >>>>>>
>>>>>>> >>>>>> I am using the CG ksp solver and Multigrid preconditioner to
>>>>>>> solve a linear system in parallel.
>>>>>>> >>>>>> I chose to use the 'Telescope' as the preconditioner on the
>>>>>>> coarse mesh for its good performance.
>>>>>>> >>>>>> The petsc options file is attached.
>>>>>>> >>>>>>
>>>>>>> >>>>>> The domain is a 3d box.
>>>>>>> >>>>>> It works well when the grid is 1536*128*384 and the process
>>>>>>> mesh is 96*8*24. When I double the size of grid and
>>>>>>> keep the same process mesh and petsc options, I
>>>>>>> get an "out of memory" error from the super-cluster I am using.
>>>>>>> >>>>>> Each process has access to at least 8G memory, which should
>>>>>>> be more than enough for my application. I am sure that all the other parts
>>>>>>> of my code( except the linear solver ) do not use much memory. So I doubt
>>>>>>> if there is something wrong with the linear solver.
>>>>>>> >>>>>> The error occurs before the linear system is completely
>>>>>>> solved so I don't have the info from ksp view. I am not able to re-produce
>>>>>>> the error with a smaller problem either.
>>>>>>> >>>>>> In addition, I tried to use the block jacobi as the
>>>>>>> preconditioner with the same grid and same decomposition. The linear solver
>>>>>>> runs extremely slow but there is no memory error.
>>>>>>> >>>>>>
>>>>>>> >>>>>> How can I diagnose what exactly cause the error?
>>>>>>> >>>>>> Thank you so much.
>>>>>>> >>>>>>
>>>>>>> >>>>>> Frank
>>>>>>> >>>>>> <petsc_options.txt>
>>>>>>> >>>>>> <ksp_view_pre.txt><memory_test2.txt><memory_test3.txt><petsc
>>>>>>> _options.txt>
>>>>>>> >>>>>>
>>>>>>> >>>>>
>>>>>>> >>>>
>>>>>>> >>> <ksp_view1.txt><ksp_view2.txt><ksp_view3.txt><memory1.txt><m
>>>>>>> emory2.txt><petsc_options1.txt><petsc_options2.txt><petsc_op
>>>>>>> tions3.txt>
>>>>>>> >
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>>
>>
>>
>
>
> --
> What most experimenters take for granted before they begin their
> experiments is infinitely more interesting than any results to which their
> experiments lead.
> -- Norbert Wiener
>
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