GPU for Science Day Mini-App

Hacking Competition Code for NERSC's GPU for Science Day, July 2019

Project Layout

• ^/gpp
  • gpp.cpp
    • main function and the kernel to focus on
  • Makefile
• ^/external
  • commonDefines.h
  • arrayMD/
    • host-device data array
  • ComplexClass/
    • custom complex number class
  • timemory
    • C++ template library for performance reporting
    • PGI compiler
      • v18.10 will fail to compile this library
      • v19.5 (default loaded by modules below) will compile this library but not report anything -- this is a compiler bug
  • cereal
    • Library used by timemory for serialization (not used)

Development Environment

Logging on to Cori

If you are already a NERSC user, your normal account should be enabled for the Cori GPU nodes.

ssh [email protected]
password + OTP

If you are not a NERSC user, we have provided some test accounts.

ssh [email protected]
testaccountpassword

Obtaining code

cd $HOME
git clone https://github.com/NERSC/gpu-for-science-day-july-2019.git
cd gpu-for-science-day-july-2019/gpp

Cori Modules [REQUIRED]

module use /usr/common/software/gpu4sci-jul-2019/modulefiles

Available modules
gpu4science/gcc
gpu4science/intel
gpu4science/cuda
gpu4science/kokkos
gpu4science/openmp
gpu4science/openacc

Info: To switch between available modules:

make clean
# make sure unload the current module
module unload gpu4science/{gcc,intel,cuda,kokkos,openmp,openacc}
# load the new module
module load gpu4science/{gcc,intel,cuda,kokkos,openmp,openacc}

Cori GPU

Get GPU node

module load gpu4science/required
salloc -A gpu4sci -C gpu -N 1 -t 04:00:00 -c 10 --gres=gpu:1

Build CPU (sequential) version

# setup
module load gpu4science/intel

# build
make COMP=intel

CUDA

# setup
module load gpu4science/cuda

# build
make COMP=cuda

OpenACC

# setup
module load gpu4science/openacc

# build
make COMP=openacc

OpenMP

# setup
module load gpu4science/openmp

# build
make COMP=openmp
export OMP_NUM_THREADS=10

Info: For the Cori GPU Skylake CPU, set OMP_NUM_THREADS=10. (This will not affect the GPU.)

Kokkos

# setup
module load gpu4science/kokkos

# build
make COMP=kokkos

Testing

Running test/debugging problem

• Fast, good for debugging

srun ./gpp.ex test

Hint: Do NOT optimize the test problem, it runs so quickly it is not representative

Running benchmark problem

• Slow, this is how we will determine the hackathon winner

srun ./gpp.ex benchmark

Hint: Optimize this problem

Competition Submission

1. Decide on a team name, if you have not done so already
   • In the steps below, replace TEAM_NAME with this name
2. Go to the top-level directory
3. Create a branch for your team
   • git checkout -b TEAM_NAME
4. Check to make sure your directory is clean
   • git status
   • remove any build files or outputs that show up
   • Do not add/commit any files other than code
5. Stage the files you want to commit
   • git add gpp/gpp.cpp and any other relevant text files, e.g. gpp/gpp.cu
6. Commit the code
   • git commit -m "Official submission of TEAM_NAME"
7. Push the code upstream
   • git push
8. Execute the PyCTest script
   • python ./pyctest-runner.py --team=TEAM_NAME --compiler=COMPILER
     • TEAM_NAME should be your team name... Please do not copy/paste and submit as TEAM_NAME
     • COMPILER should be one of openacc, openmp, cuda, or kokkos
   • This script will:
     • Build the code in the gpp folder
     • Execute the benchmark test
     • Submit the build and test logs to cdash.nersc.gov