[petsc-users] Performance of Conda Binary vs Self Compiled Version

[Jorge Nin]

Hi Mathew,

Thanks for the response. It actually seems like the matrix is very sparse (0.99% sparsity from what I’m measuring). It’s an FEA solver so it would make sense.
My current guess is the optimization flags are making a large difference for the M1 Mac, but I am also surprised it makes such a huge difference.

It’s why I was asking if there was a resource or another to use my own version of PETSc with Conda.

I believe a 2-3 x speed up is worth the hassle. 


Best,
Jorge



> On Oct 19, 2023, at 4:00 PM, Matthew Knepley <knepley at gmail.com> wrote:
> 
> On Thu, Oct 19, 2023 at 3:54 PM Jorge Nin <jorgenin at mit.edu <mailto:jorgenin at mit.edu>> wrote:
>> Hi,
>> I was playing around with a self compiled version and, and a the Conda binary of Petsc on the same problem, on my M1 Mac.
>> Interestingly I found that the Conda binary solves the problem 2-3 times slower vs the self compiled version. (For context I’m using the petsc4py python interface) 
>> 
>> I’ve attached two log views to show the comparison.
>> 
>> I was mostly curious about the possible cause for this.
> 
> All the time is in the LU numeric factorization. I don't know if your matrix is sparse or dense. I am guessing it is dense and different LAPACK implementations are linked. If it is sparse, then the compiler options are different between builds, but I would be surprised if it made this much difference.
> 
>   Thanks,
> 
>      Matt
>  
>>  I was also curious how I could use my own compiled version of PETSc in my Conda install? 
>> 
>> 
>> Best,
>> Jorge
>> 
> 
> 
> --
> 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
> 
> https://www.cse.buffalo.edu/~knepley/ <http://www.cse.buffalo.edu/~knepley/>

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