[petsc-users] petsc example with known scalability

[Mohammad Mirzadeh]

Yes, I'm looking at weak scalability right now. I'm using BiCGSTAB with
BoomerAMG (all default options except for rtol = 1e-12). I've not looked
into MF/s yet but I'll surely do to see if I'm having any problem there. So
far, just timing the KSPSolve, I get [0.231, 0.238, 0.296, 0.451, 0.599]
seconds/KSP iteration for p=[1, 4, 16, 64, 256] with almost 93K nodes
(matrix-row) per proc. Which is not bad I guess but still increased by a
factor of 3 for 256 proc. Problem is, I don't know how good/bad this is. In
fact I'm not even sure that is a valid question to ask since it may be very
problem dependent.

Something I just though about, how crucial is the matrix structure for KSP
solvers? The nodes have bad numbering and I do partitioning to get a better
one here.



On Fri, May 18, 2012 at 4:47 PM, Matthew Knepley <knepley at gmail.com> wrote:

> On Fri, May 18, 2012 at 7:43 PM, Mohammad Mirzadeh <mirzadeh at gmail.com>wrote:
>
>> I see; well that's a fair point. So i have my timing results obtained via
>> -log_summary; what should I be looking into for MatMult? Should I be
>> looking at wall timings? Or do I need to look into MFlops/s? I'm sorry but
>> I'm not sure what measure I should be looking into to determine scalability.
>>
>
> Time is only meaningful in isolation if I know how big your matrix is, but
> you obviously take the ratio to look how it is scaling. I am
> assuming you are looking at weak scalability so it should remain constant.
> MF/s will let you know how the routine is performing
> independent of size, and thus is an easy way to see what is happening. It
> should scale like P, and when that drops off you have
> insufficient bandwidth. VecMDot is a good way to look at the latency of
> reductions (assuming you use GMRES). There is indeed no
> good guide to this. Barry should write one.
>
>     Matt
>
>
>>  Also, is there any general meaningful advice one could give? in terms of
>> using the resources, compiler flags (beyond -O3), etc?
>>
>> Thanks,
>> Mohammad
>>
>> On Fri, May 18, 2012 at 4:18 PM, Matthew Knepley <knepley at gmail.com>wrote:
>>
>>> On Fri, May 18, 2012 at 7:06 PM, Mohammad Mirzadeh <mirzadeh at gmail.com>wrote:
>>>
>>>> Hi guys,
>>>>
>>>> I'm trying to generate scalability plots for my code and do profiling
>>>> and fine tuning. In doing so I have noticed that some of the factors
>>>> affecting my results are sort of subtle. For example, I figured, the other
>>>> day, that using all of the cores on a single node is somewhat (50-60%)
>>>> slower when compared to using only half of the cores which I suspect is due
>>>> to memory bandwidth and/or other hardware-related issues.
>>>>
>>>> So I thought to ask and see if there is any example in petsc that has
>>>> been tested for scalability and has been documented? Basically I want to
>>>> use this test example as a benchmark to compare my results with. My own
>>>> test code is currently a linear Poisson solver on an adaptive quadtree grid
>>>> and involves non-trivial geometry (well basically a circle for the boundary
>>>> but still not a simple box).
>>>>
>>>
>>> Unfortunately, I do not even know what that means. We can't guarantee a
>>> certain level of performance because it not
>>> only depends on the hardware, but how you use it (as evident in your
>>> case). In a perfect world, we would have an abstract
>>> model of the computation (available for MatMult) and your machine (not
>>> available anywhere) and we would automatically
>>> work out the consequences and tell you what to expect. Instead today, we
>>> tell you to look at a few key indicators like the
>>> MatMult event, to see what is going on. When MatMult stops scaling, you
>>> have run out of bandwidth.
>>>
>>>     Matt
>>>
>>>
>>>> Thanks,
>>>> Mohammad
>>>>
>>>
>>>
>>>
>>> --
>>> 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
>>>
>>
>>
>
>
> --
> 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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