[petsc-users] Scaling with number of cores

Barry Smith bsmith at mcs.anl.gov
Sun Nov 1 19:49:36 CST 2015 

  If you are doing many time steps with the same linear solver then you MUST do your weak scaling studies with MANY time steps since the setup time of AMG only takes place in the first stimestep. So run both 48 and 96 processes with the same large number of time steps.

  Barry



> On Nov 1, 2015, at 7:35 PM, TAY wee-beng <zonexo at gmail.com> wrote:
> 
> Hi,
> 
> Sorry I forgot and use the old a.out. I have attached the new log for 48cores (log48), together with the 96cores log (log96).
> 
> Why does the number of processes increase so much? Is there something wrong with my coding?
> 
> Only the Poisson eqn 's RHS changes, the LHS doesn't. So if I want to reuse the preconditioner, what must I do? Or what must I not do?
> 
> Lastly, I only simulated 2 time steps previously. Now I run for 10 timesteps (log48_10). Is it building the preconditioner at every timestep?
> 
> Also, what about momentum eqn? Is it working well?
> 
> I will try the gamg later too.
> 
> Thank you
> 
> Yours sincerely,
> 
> TAY wee-beng
> 
> On 2/11/2015 12:30 AM, Barry Smith wrote:
>>   You used gmres with 48 processes but richardson with 96. You need to be careful and make sure you don't change the solvers when you change the number of processors since you can get very different inconsistent results
>> 
>>    Anyways all the time is being spent in the BoomerAMG algebraic multigrid setup and it is is scaling badly. When you double the problem size and number of processes it went from 3.2445e+01 to 4.3599e+02 seconds.
>> 
>> PCSetUp                3 1.0 3.2445e+01 1.0 9.58e+06 2.0 0.0e+00 0.0e+00 4.0e+00 62  8  0  0  4  62  8  0  0  5    11
>> 
>> PCSetUp                3 1.0 4.3599e+02 1.0 9.58e+06 2.0 0.0e+00 0.0e+00 4.0e+00 85 18  0  0  6  85 18  0  0  6     2
>> 
>>   Now is the Poisson problem changing at each timestep or can you use the same preconditioner built with BoomerAMG for all the time steps? Algebraic multigrid has a large set up time that you often doesn't matter if you have many time steps but if you have to rebuild it each timestep it is too large?
>> 
>>   You might also try -pc_type gamg and see how PETSc's algebraic multigrid scales for your problem/machine.
>> 
>>   Barry
>> 
>> 
>> 
>>> On Nov 1, 2015, at 7:30 AM, TAY wee-beng <zonexo at gmail.com> wrote:
>>> 
>>> 
>>> On 1/11/2015 10:00 AM, Barry Smith wrote:
>>>>> On Oct 31, 2015, at 8:43 PM, TAY wee-beng <zonexo at gmail.com> wrote:
>>>>> 
>>>>> 
>>>>> On 1/11/2015 12:47 AM, Matthew Knepley wrote:
>>>>>> On Sat, Oct 31, 2015 at 11:34 AM, TAY wee-beng <zonexo at gmail.com> wrote:
>>>>>> Hi,
>>>>>> 
>>>>>> I understand that as mentioned in the faq, due to the limitations in memory, the scaling is not linear. So, I am trying to write a proposal to use a supercomputer.
>>>>>> Its specs are:
>>>>>> Compute nodes: 82,944 nodes (SPARC64 VIIIfx; 16GB of memory per node)
>>>>>> 
>>>>>> 8 cores / processor
>>>>>> Interconnect: Tofu (6-dimensional mesh/torus) Interconnect
>>>>>> Each cabinet contains 96 computing nodes,
>>>>>> One of the requirement is to give the performance of my current code with my current set of data, and there is a formula to calculate the estimated parallel efficiency when using the new large set of data
>>>>>> There are 2 ways to give performance:
>>>>>> 1. Strong scaling, which is defined as how the elapsed time varies with the number of processors for a fixed
>>>>>> problem.
>>>>>> 2. Weak scaling, which is defined as how the elapsed time varies with the number of processors for a
>>>>>> fixed problem size per processor.
>>>>>> I ran my cases with 48 and 96 cores with my current cluster, giving 140 and 90 mins respectively. This is classified as strong scaling.
>>>>>> Cluster specs:
>>>>>> CPU: AMD 6234 2.4GHz
>>>>>> 8 cores / processor (CPU)
>>>>>> 6 CPU / node
>>>>>> So 48 Cores / CPU
>>>>>> Not sure abt the memory / node
>>>>>> 
>>>>>> The parallel efficiency ‘En’ for a given degree of parallelism ‘n’ indicates how much the program is
>>>>>> efficiently accelerated by parallel processing. ‘En’ is given by the following formulae. Although their
>>>>>> derivation processes are different depending on strong and weak scaling, derived formulae are the
>>>>>> same.
>>>>>> From the estimated time, my parallel efficiency using  Amdahl's law on the current old cluster was 52.7%.
>>>>>> So is my results acceptable?
>>>>>> For the large data set, if using 2205 nodes (2205X8cores), my expected parallel efficiency is only 0.5%. The proposal recommends value of > 50%.
>>>>>> The problem with this analysis is that the estimated serial fraction from Amdahl's Law  changes as a function
>>>>>> of problem size, so you cannot take the strong scaling from one problem and apply it to another without a
>>>>>> model of this dependence.
>>>>>> 
>>>>>> Weak scaling does model changes with problem size, so I would measure weak scaling on your current
>>>>>> cluster, and extrapolate to the big machine. I realize that this does not make sense for many scientific
>>>>>> applications, but neither does requiring a certain parallel efficiency.
>>>>> Ok I check the results for my weak scaling it is even worse for the expected parallel efficiency. From the formula used, it's obvious it's doing some sort of exponential extrapolation decrease. So unless I can achieve a near > 90% speed up when I double the cores and problem size for my current 48/96 cores setup,     extrapolating from about 96 nodes to 10,000 nodes will give a much lower expected parallel efficiency for the new case.
>>>>> 
>>>>> However, it's mentioned in the FAQ that due to memory requirement, it's impossible to get >90% speed when I double the cores and problem size (ie linear increase in performance), which means that I can't get >90% speed up when I double the cores and problem size for my current 48/96 cores setup. Is that so?
>>>>   What is the output of -ksp_view -log_summary on the problem and then on the problem doubled in size and number of processors?
>>>> 
>>>>   Barry
>>> Hi,
>>> 
>>> I have attached the output
>>> 
>>> 48 cores: log48
>>> 96 cores: log96
>>> 
>>> There are 2 solvers - The momentum linear eqn uses bcgs, while the Poisson eqn uses hypre BoomerAMG.
>>> 
>>> Problem size doubled from 158x266x150 to 158x266x300.
>>>>> So is it fair to say that the main problem does not lie in my programming skills, but rather the way the linear equations are solved?
>>>>> 
>>>>> Thanks.
>>>>>>   Thanks,
>>>>>> 
>>>>>>      Matt
>>>>>> Is it possible for this type of scaling in PETSc (>50%), when using 17640 (2205X8) cores?
>>>>>> Btw, I do not have access to the system.
>>>>>> 
>>>>>> 
>>>>>> 
>>>>>> Sent using CloudMagic Email
>>>>>> 
>>>>>> 
>>>>>> 
>>>>>> -- 
>>>>>> 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
>>> <log48.txt><log96.txt>
> 
> <log48_10.txt><log48.txt><log96.txt>

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