Scheduling GPU resources in the Grid

Full documentation at this page.

Underlying tale of installations, applied configuration and tweaks

NVIDIA worker

1. Blacklist nouveau.

   • To avoid compilation errors (aka ERROR: Unable to load the kernel module 'nvidia.ko'...) when installing NVIDIA driver, is often not enough to include blacklist nouveau in /etc/modprobe.d/blacklist.conf. It is also required to remove it from the initrd image like so:

         # echo -e "blacklist nouveau\noptions nouveau modeset=0" > /etc/modprobe.d/disable-nouveau.conf
         # mkinitrd -f -v /boot/initrd-$(uname -r).img $(uname -r)  # or `dracut -f`

     and reboot.

2. Install ./NVIDIA-Linux-x86_64-325.08.run or whatever other version.

3. Install ./cudatoolkit_4.0.17_linux_64_rhel6.0.run or whatever other version.

4. Check nvidia-smi command output.

   • If not supported or NA information is found:

         (..)
         +-----------------------------------------------------------------------------+
         | Compute processes:                                               GPU Memory |
         |  GPU       PID  Process name                                     Usage      |
         |=============================================================================|
         |    0            Not Supported                                               |
         |    1            Not Supported                                               |
         |    2            Not Supported                                               |
         |    3            Not Supported                                               |
         +-----------------------------------------------------------------------------+

     we need to patch libnvidia-ml.so.1 library:

     1. Get patch from Github's nvml_fix repository.

     2. Compile it with TARGET=<your-nvidia-driver-version> (must be supported by the fix).

        • HACK: in Scientific Linux 6 it must be compiled with pthread and dl libraries:

          •            # cat Makefile
                       (..)
                       CFLAGS        = -lpthread -ldl
                       (..)

     3. Remove the link /usr/lib64/libnvidia-ml.so.1 and substitute it with the just created $PWD/libnvidia-ml.so.1 file.

        • Note that we use lib64 (not the default Makefile's libdir location -> lib).

        • Do not use make install PREFIX=/usr, copy it by hand.

        • Do not create a link, since ldconfig will overwrite it.

     Now nvidia-smi output should look like:

         (..)
         +-----------------------------------------------------------------------------+
         | Compute processes:                                               GPU Memory |
         |  GPU       PID  Process name                                     Usage      |
         |=============================================================================|
         |  No running compute processes found                                         |
         +-----------------------------------------------------------------------------+

CREAM CE

1. Added to BLAHP script /usr/libexec/sge_local_submit_attributes.sh:

   •     (..)
         if [ -n $gpu ]; then
             echo "#$ -l gpu=${gpu}"
         fi
         (..)

Scheduler

1. [qmaster] Define complex value 'gpu':

   •     #name               shortcut     type        relop requestable consumable default  urgency  
         #-------------------------------------------------------------------------------------------
         (..)
         gpu                 gpu          INT         <=    YES         YES        0        0
         (..)

2. [qmaster] Host(s) complexes:

   •     hostname              tesla.ifca.es
         load_scaling          NONE
         complex_values        gpu=4,mem_free=24G,virtual_free=24G
         user_lists            NONE
         xuser_lists           NONE
         projects              NONE
         xprojects             NONE
         usage_scaling         NONE
         report_variables      NONE

3. Load sensor:

   • hostname=`uname -n`
     
     while [ 1 ]; do
       read input
       result=$?
       if [ $result != 0 ]; then
         exit 1
       fi
       if [ "$input" == "quit" ]; then
         exit 0
       fi
     
     
       smitool=`which nvidia-smi`
       result=$?
       if [ $result != 0 ]; then
         gpusav=0
         gpus=0
       else
         gpustotal=`nvidia-smi -L|wc -l`
         gpusused=`nvidia-smi |grep "Process name" -A 6|grep -v +-|grep -v \|=|grep -v Usage|grep -v "No running"|wc -l`
         gpusavail=`echo $gpustotal-$gpusused|bc`
       fi
     
       echo begin
       echo "$hostname:gpu:$gpusavail"
       echo end
     done
     
     exit 0

4. [qmaster] Per-host load sensor:

   •     # qconf -sconf tesla
         #tesla.ifca.es:
         load_sensor                  /nfs4/opt/gridengine/util/resources/loadsensors/gpu.sh

   • Must be available in the execution node (e.g. shared via NFS)
5. [execd] Restart execd process to load the new sensor:

   • # ps auxf
     (..)
     root     24786  0.0  0.0 163252  2268 ?        Sl   16:51   0:00 /nfs4/opt/gridengine/bin/lx-amd64/sge_execd
     root     24798  0.0  0.0 106104  1260 ?        S    16:51   0:00  \_ /bin/sh /nfs4/opt/gridengine/util/resources/loadsensors/gpu.sh
     root     24801  0.0  0.0 106104   544 ?        S    16:51   0:00      \_ /bin/sh /nfs4/opt/gridengine/util/resources/loadsensors/gpu.sh
     root     24802 71.0  0.0  11140   988 ?        R    16:51   0:00          \_ nvidia-smi -L
     root     24803  0.0  0.0 100924   632 ?        S    16:51   0:00          \_ wc -l
     (..)

   • soft-stop the service if there are jobs running.

6. [qmaster] Query the GPU-host gpu resource:

   •    # qhost -h tesla -F gpu
        HOSTNAME                ARCH         NCPU NSOC NCOR NTHR  LOAD  MEMTOT  MEMUSE  SWAPTO  SWAPUS
        ----------------------------------------------------------------------------------------------
        global                  -               -    -    -    -     -       -       -       -       -
        tesla                   lx-amd64        4    1    4    4  0.19   23.5G    1.7G   11.8G     0.0
            Host Resource(s):      hl:gpu=4.000000

References

1. GridEngine

   • http://serverfault.com/questions/322073/howto-set-up-sge-for-cuda-devices

   • http://gridengine.org/pipermail/users/2012-April/003338.html

2. NVIDIA CUDA

   • https://github.com/CFSworks/nvml_fix