HOWTO use AmpTools on the JLab farm GPUs
Contents
Access through SLURM
JLab currently provides NVidia Titan RTX or T4 cards on the sciml19 an sciml21 nodes. The nodes can be accessed through SLURM, where N is the number of requested cards (1-4):
>salloc --gres gpu:TitanRTX:N --partition gpu --nodes 1
or
>salloc --gres gpu:T4:N --partition gpu --nodes 1
An interactive shell (e.g. bash) on the node with requested allocation can be opened with srun:
>srun --pty bash
Information about the cards, cuda version and usage is displayed with this command:
>nvidia-smi +-----------------------------------------------------------------------------+ | NVIDIA-SMI 418.87.01 Driver Version: 418.87.01 CUDA Version: 10.1 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 TITAN RTX Off | 00000000:3E:00.0 Off | N/A | | 41% 27C P8 2W / 280W | 0MiB / 24190MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+
AmpTools Compilation with CUDA
This example was done in csh for the Titan RTX cards available on sciml1902.
The compilation does not have to be performed on this machine, we chose ifarm1901 here.
1) Download latest AmpTools release
git clone git@github.com:mashephe/AmpTools.git
2) Set AMPTOOLS directory
setenv AMPTOOLS_HOME $PWD/AmpTools/ setenv AMPTOOLS $AMPTOOLS_HOME/AmpTools/
3) Load cuda environment module (source /etc/profile.d/modules.csh
before if you can't find the module
command)
module add cuda setenv CUDA_INSTALL_PATH /apps/cuda/11.4.2/
4) Set AMPTOOLS directory
setenv AMPTOOLS $PWD/AmpTools
5) Put root-config in your path
setenv PATH $ROOTSYS/bin:$PATH
6) Edit the AmpTools Makefile (or Makefile.settings) to pass the appropriate GPU architecture to the cuda complier (info e.g. here)
CUDA_FLAGS := -m64 -arch=sm_75
7) Build main AmpTools library with GPU support
cd $AMPTOOLS_HOME make gpu
halld_sim Compilation with GPU
The GPU dependent part of halld_sim is libraries/AMPTOOLS_AMPS/ where the GPU kernels are located. With the environment setup above the full halld_sim should be compiled, which will recognize the AMPTOOLS GPU flag and build the necessary libraries and executables to be run on the GPU
cd $HALLD_SIM_HOME/src/ scons -u install -j8
Performing Fits Interactively
With the environment setup above, the fit executable is run the same as on a CPU
fit -c YOURCONFIG.cfg
where YOURCONFIG.cfg is your usual config file. Note: additional command line parameters can be used as well, as needed.
Combining GPU and MPI
To utilize multiple GPUs in the same fit you'll need both the AmpTools and halld_sim libraries to be compiled with GPU and MPI support. To complete the steps below you'll need to be logged into one of the sciml nodes with GPU support (as described above).
AmpTools
Build the main AmpTools library with GPU and MPI support (note "mpigpu" option)
cd $AMPTOOLS_HOME make mpigpu
halld_sim
With the environment setup above the fitMPI executable is the only thing that needs to be recompiled, which will recognize the AmpTools GPU and MPI flag and build the necessary libraries and executables to be run on the GPU with MPI
cd $HALLD_SIM_HOME/src/programs/AmplitudeAnalysis/fitMPI/ scons -u install
Performing Fits Interactively
The fitMPI executable is run with mpirun the same as on a CPU
mpirun fitMPI -c YOURCONFIG.cfg
If you're using Slurm it will recognize how many GPUs you've reserved and assign the number of parallel processes to make use of those GPUs.