Sorry for the confusion. My fault...
I saw my previous post and found that I missed something. In fact, I couldn't 
run "-pme gpu".

So, once again, I ran all the commands and uploaded the log files


gmx mdrun -nobackup -nb cpu -pme cpu -deffnm md_0_1
https://pastebin.com/RNT4XJy8


gmx mdrun -nobackup -nb cpu -pme gpu -deffnm md_0_1
https://pastebin.com/7BQn8R7g
This run shows an error on the screen which is not shown in the log file. So 
please also see https://pastebin.com/KHg6FkBz



gmx mdrun -nobackup -nb gpu -pme cpu -deffnm md_0_1
https://pastebin.com/YXYj23tB



gmx mdrun -nobackup -nb gpu -pme gpu -deffnm md_0_1
https://pastebin.com/P3X4mE5y





From the results, it seems that running the pme on the cpu is better than gpu. 
The fastest command here is -nb gpu -pme cpu


Still I have the question that while GPU is utilized, the CPU is also busy. So, 
I was thinking that the source code uses cudaDeviceSynchronize() where the CPU 
enters a busy loop.



Regards,
Mahmood






On Friday, March 2, 2018, 3:24:41 PM GMT+3:30, Szilárd Páll 
<pall.szil...@gmail.com> wrote: 





Once again, full log files, please, not partial cut-and-paste, please.

Also, you misread something because your previous logs show:
-nb cpu -pme gpu: 56.4 ns/day
-nb cpu -pme gpu -pmefft cpu 64.6 ns/day
-nb cpu -pme cpu 67.5 ns/day

So both mixed mode PME and PME on CPU are faster, the latter slightly faster 
than the former.

This is about as much as you can do, I think. Your GPU is just too slow to get 
more performance out of it and the runs are GPU-bound. You might be able to get 
a bit more performance with some tweaks (compile mdrun with AVX2_256, use a 
newer fftw, use a newer gcc), but expect marginal gains.

Cheers,

--
Szilárd


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