Compiling VASP-GPU on MS Azure GPU VM

After a long wait, the GPU VM has become generally available on MS Azure service. I will use it to test the capability of VASP-GPU version before deciding to invest in the pricey GPU servers at our center. As a beginning, I reference by setting on the previous work, which setup a previewed NC6 unit for deep learning application [Cortana Intelligence and ML Blog Team].

For our test, I used the cheapest model of  NC6 VM at South Central US equipped with 6 CPU cores + 1 K80 GPU pricing at $1.08/hr. To minimize the cost, I put it on a HDD storage. At first, I tried using OpenSuSe 13.2 but the NC6 become unavailable so I used Ubuntu 16.04 LTS instead.
First, I followed the cuda installation instruction by Cortana team. Here are the steps:

  1. The first step is to install the dependencies:

    sudo apt-get install -y  make unzip git gcc g++ libcurl4-openssl-dev libssl-dev

    followed by update to alternatives for cc:

    sudo update-alternatives –install /usr/bin/cc cc /usr/bin/gcc 50

  2. Install cuda driver & toolkit

    chmod 755 cuda_8.0.44_linux.run
    sudo ./cuda_8.0.44_linux.run –override

  3. Enabling the CUDA persistence mode.

    sudo nvidia-smi -pm 1
    nvidia-smi

    To make things easier, I put it in a bash script for ease of use next time I restart the machine.

Then the cuda part is ready to go. Next, I set up the compilers & MPI environment. To make it short, I tried several solutions on Ubuntu that does not work:

  • OpenMPI 2.0.0 + gfortran
  • OpenMPI 1.10.5 + Intel Compiler
  • OpenMPI 2.0.0 + Intel Compiler
  • OpenMPI 2.0.1 + Intel Compiler

It seems that the working solution is MPICH2-3.2 + Intel Compiler. Now, the compilation without VTST code. Next, I’ll try compiling MPICH2-3.2 + Intel Compiler with VTST code applied.

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