[Pw_forum] Help with Building and Installing Quantum Espresso

[Jean Martel]

On Sat, Dec 29, 2012 at 11:02 PM, Filippo Spiga <spiga.filippo at gmail.com>wrote:

> Dear Jean,
>
> please refer to http://qe-forge.org/gf/project/q-e-gpu/
> in order to configure and install QE-GPU on your machine.  The procedure
> is easy and lot of details are reported in the file GPU/READEME.GPU
>

Thanks that is helpful.


>
>
> On Dec 29, 2012, at 11:48 AM, Jean Martel <john.martel9 at gmail.com> wrote:
>
> I need some help with building and installing Quantum Espresso on my
> desktop running under Ubuntu 12.04. I am interested in running
> GPU accelerated TD-DFT calculations to compute excited-state properties,
> with occasional B3LYP geometry optimizations.
>
>
> TDDFT is not entirely supported. Only part of it can exploit GPU
> acceleration thanks to some libraries and shared modules.
>

>
> One of the main issues I am having is the with the two different QE-GPU
> versions, there are version 5.01 and 5.02. However 5.02 contains much fewer
> and totally different files, and I am not sure which one to use.
>
>
> I do not thing you are really interested to understand too much details
> about the implementation. If you have run calculations using QE 5.0.1 and
> you need to restart/resume from that version then pick up the QE-GPU-5.0.1.
> If you have to run new calculations use the QE-GPU-5.0.2. Latest version
> always means better code, less bug, sometimes better/improved performance,
> less "problems".
>

In the download page there are two files 'espresso-5.0.1-GPU-build2.tar.gz'
and 'QE-GPU-5.0.2-build1.tar.gz' the first one is 18 MB and the second is 3
MB, are you saying that 'QE-GPU-5.0.2-build1.tar.gz' supplements
'espresso-5.0.1-GPU-build2.tar.gz' ?


>
>
> Any guidance or pointers on how to build and install QE  on my system for
> the types of calculations i am looking for would be great.
>
> My System Specifications:
> Ubuntu12.04
> gcc 4.6.3 with gfortran
> Intel MKL version 11 update 1
> Cuda 5.0
> Hexacore i7-3930K
> Dual Gtx 690
> 64 GB RAM
>
>
>
> What it is not clear is if you have a single GTX690 card (that has 2 GPU
> die on the same board) or two different cards. If I understand correctly,
> the GTX 690 comes with 2 integrate GPU in the same board and 4 GByte of
> RAM. QE-GPU will detect two distinct GPUs and we will manage them safely.
> Since your system has so much RAM I do suggest to run QE-GPU in MPI+OpenMP
> mode in order to use both GPUs together as better as possible. So
>

I actually have 2 different GTX 690 cards (so total of 4 GPUs and 8 GB of
RAM).


>
> $ cd GPU
> $ ./configure --enable-parallel --enable-openmp --enable-cuda
> --with-gpu-arch=30 --with-cuda-dir=<where your CUDA is installed>
> --enable-magma --enable-phigemm
> $ cd ..
> $ make -f Makefile.gpu all-gpu
>
> Then you should run using 2 MPI and 3 OpenMP per thread (or 6 OpenMP if
> hyper-threading is enabled). Please install Open MPI on your system (latest
> version is fine). I have no idea what kind of binding settings suggest
> because I have not found any detail about the NUMA topology of the
> processor. Please read the RREADME.GPU file.
>

Thanks will do

>
> The only TDDFT executable will be turbo_lanczos-gpu.x. I never tested it
> so.. good luck. Actually I will be happy to receive a test case and some
> feedback about it.
>

Great, i will be happy to report the results

>
> Few words about some performance expectation: the GTX 690 uses a GK104 GPU
> chip. This chip has been designed for gaming mainly. So its double
> precision peak is quite poor compared to another GPU generation chip
> (GK1110). So please do not expect speedups like 2 or 3 times. GPU will help
> to have a less time-to-solution but not a big improvement in performance.
> Moreover the GPU implementation targets compute intensive calculation. If
> you have small systems composed by less than ~50 atoms then there is not
> guarantee about any performance benefit.
>

Yeah, I know that the double precision peak for GK104 chips is not too high
on paper, however users have reported only an average speed increase of 2
between the K10(GK104) and the K20(GK110). I have systems with about 75
heavy atoms, so I am hoping to get some speed up gains.


>
> Happy New Year and Happy Computing
>
> (PLEASE subscribe the QE-GPU mailing list, I will not reply to any further
> email on this one)
>
> Cheers,
> Filippo
>
> --
> Mr. Filippo SPIGA, M.Sc.
> http://filippospiga.me ~ skype: filippo.spiga
>
> «Nobody will drive us out of Cantor's paradise.» ~ David Hilbert
>
>
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