How big are the sub matrices, how many MPI processes are you hoping to use,
how fast/sophisticated is your file system?
All of these things and others will determine whether this approach will
buy you anything or not.
I recommend NOT doing this first, instead just sequentially read in the
matrices and perform the computations and then run profiling to determine where
the time is being spent and whether even trying this kind of optimization makes
sense. I suspect it does not.
Barry
> On May 24, 2017, at 12:37 PM, Michał Dereziński <[email protected]>
> wrote:
>
> Great! Then I have a follow-up question:
>
> My goal is to be able to load the full matrix X from disk, while at the same
> time in parallel, performing computations on the submatrices that have
> already been loaded. Essentially, I want to think of X as a block matrix
> (where the blocks are horizontal, spanning the full width of the matrix),
> where I’m loading one block at a time, and all the blocks that have already
> been loaded are combined using MatCreateNest, so that I can make computations
> on that portion of the matrix.
>
> In this scenario, every process needs to be simultaneously loading the next
> block of X, and perform computations on the previously loaded portion. My
> strategy is for each MPI process to spawn a thread for data loading (so that
> the memory between the process and the thread is shared), while the process
> does computations. My concern is that the data loading thread may be using up
> computational resources of the processor, even though it is mainly doing IO.
> Will this be an issue? What is the best way to minimize the cpu time of this
> parallel data loading scheme?
>
> Thanks,
> Michal.
>
>
>> Wiadomość napisana przez Matthew Knepley <[email protected]> w dniu
>> 24.05.2017, o godz. 04:55:
>>
>> On Wed, May 24, 2017 at 1:09 AM, Michal Derezinski <[email protected]> wrote:
>> Hi,
>>
>> I want to be able to perform matrix operations on several contiguous
>> submatrices of a full matrix, without allocating the memory redundantly for
>> the submatrices (in addition to the memory that is already allocated for the
>> full matrix).
>> I tried using MatGetSubMatrix, but this function appears to allocate the
>> additional memory.
>>
>> The other way I found to do this is to create the smallest submatrices I
>> need first, then use MatCreateNest to combine them into bigger ones
>> (including the full matrix).
>> The documentation of MatCreateNest seems to indicate that it does not
>> allocate additional memory for storing the new matrix.
>> Is this the right approach, or is there a better one?
>>
>> Yes, that is the right approach.
>>
>> Thanks,
>>
>> Matt
>>
>> Thanks,
>> Michal Derezinski.
>>
>>
>>
>> --
>> 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
>>
>> http://www.caam.rice.edu/~mk51/
>
