Dear Janardhan,

I understand your point but I also agree with my colleague Ivan. You can 
compile QE-GPU, the configure still accepts "sm_13" as valid compute capability 
(and maybe this should change). As the README.GPU reports, it is highly 
recommended a GPU with (at least) a Fermi chip and with ECC support. 


On Sep 25, 2014, at 2:44 PM, Ivan Girotto <igirotto at ictp.it> wrote:
On 25/09/2014 06:10, janardhan H.L. wrote:
> 
>> This GPU (Tesla C1060 / M1060) may be old and computation may be poor 
>> compared to todays GPUs but still the combined speed (we have 4 nos) could 
>> be more than multi core cpus. Even though the current QE-GPU is not 
>> supported             on this GPU
>>  it successfully compiled but while invoking pw-gpu.x following errors is  
>> obtained 
>> 
>> "  ***WARNING: unbalanced configuration (1 MPI per node, 5 GPUs per node)
>> Error in (first zero) memory allocation , program will be terminated!!! 
>> Bye... "

You have 5 GPU per node? Which type of processor do you have? Which NVIDIA 
driver are you using? Which CUDA SDK?

The reason behind the warning is that QE-GPU prefers even number of GPUs if 
there are more than 1 GPU per node. It is harmless warning, ignore it. The 
error is related to a memory allocation of 0 bytes, a trick used to let the GPU 
runtime allocate immediately some internal memory (mainly space for stack 
information) so the application can probe using another CUDA API the real 
amount of memory available for the calculation. The reason this is not working 
in your case I do not know but I do not have anymore x1060 GPU since a long 
time so it is almost two years I am not doing any test of such old type of GPU. 
I have no easy fix, I am not sure it is worth (from my point of view) to 
investigate this problem that has high chances to be "GPU architecture 
dependent" 

Unfortunately I cannot help much.

F

--
Mr. Filippo SPIGA, M.Sc.
http://filippospiga.info ~ skype: filippo.spiga

?Nobody will drive us out of Cantor's paradise.? ~ David Hilbert

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