Dear Lev,
thank you very much for the valuable answers and your support! I will
follow your advices!
Your contribution and contribution of other collegues here were so helpful!
Many thanks for you all!
Best regards,
Evgeny
Am 25.02.2014 18:58, schrieb Lev Givon:
Received from Evgeny Lazutkin on Tue, Feb 25, 2014 at 11:58:10AM EST:
Many thanks for your support, Lev!
It works and looks good!
I have already mentioned about parallelization: your answer was that
it happens automatically.
To be more detailed:
0. Could you please explain this mechanism?
Since the library is proprietary, I don't know what CULA is doing
internally. You may wish to inquire further on the CULA forums [1], although I
suspect that the developers will not wish to discuss the specifics of how they
implement the algorithms.
1. How many blocks/threads has been used in my program?
2. How to obtain this numbers in program? How to manipulate?
I don't believe those parameters can be modified.
3. Could you give any literature, where I can read about it?
If you want to get more insight into how LAPACK-type algorithms may be
implemented using GPUs, you may wish to look into the MAGMA library [2]; it
contains a number of similar functions, but since it is open-source you can see
what it is doing internally. The authors have also published a number of
articles discussing their algorithms.
I have such a question because I am new one in such theme. When
using the SourceModul - I give the number of blocks and threads. So
it is not clear for me - how it works automatically? Is that depends
from the Matrix size an so on?
Although pycuda does do automated block and thread number selection for some of
the kernels that it generates to support features such as elementwise
computations, reductions, etc., you will have to figure out how to select the
appropriate number of blocks and threads when you launch your own kernel via the
SourceModule class depending on your specific problem.
I think such information will be useful for all new users! I hope,
you can help me (or someone else:-) ) to understand!
There are a range of online GPU programming courses you may wish to consider for
further information:
https://developer.nvidia.com/cuda-training
Best regards,
[1] http://www.culatools.com/forums/
[2] http://icl.cs.utk.edu/magma/
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