Perlmutter (NERSC)

The Perlmutter cluster is located at NERSC.

Introduction

If you are new to this system, please see the following resources:

NERSC user guide
Batch system: Slurm
Jupyter service (documentation)
Filesystems:
- $HOME: per-user directory, use only for inputs, source and scripts; backed up (40GB)
- ${CFS}/m3239/: community file system for users in the project m3239 (or equivalent); moderate performance (20TB default)
- $PSCRATCH: per-user production directory; very fast for parallel jobs; purged every 8 weeks (20TB default)

Preparation

Use the following commands to download the WarpX source code:

git clone https://github.com/ECP-WarpX/WarpX.git $HOME/src/warpx

On Perlmutter, you can run either on GPU nodes with fast A100 GPUs (recommended) or CPU nodes.

A100 GPUs

We use system software modules, add environment hints and further dependencies via the file $HOME/perlmutter_gpu_warpx.profile. Create it now:

cp $HOME/src/warpx/Tools/machines/perlmutter-nersc/perlmutter_gpu_warpx.profile.example $HOME/perlmutter_gpu_warpx.profile

Script Details

# please set your project account
export proj=""  # change me! GPU projects must end in "..._g"

# remembers the location of this script
export MY_PROFILE=$(cd $(dirname $BASH_SOURCE) && pwd)"/"$(basename $BASH_SOURCE)
if [ -z ${proj-} ]; then echo "WARNING: The 'proj' variable is not yet set in your $MY_PROFILE file! Please edit its line 2 to continue!"; return; fi

# required dependencies
module load gpu
module load PrgEnv-gnu
module load craype
module load craype-x86-milan
module load craype-accel-nvidia80
module load cudatoolkit
module load cmake/3.24.3

# optional: for QED support with detailed tables
export BOOST_ROOT=/global/common/software/spackecp/perlmutter/e4s-23.05/default/spack/opt/spack/linux-sles15-zen3/gcc-11.2.0/boost-1.82.0-ow5r5qrgslcwu33grygouajmuluzuzv3

# optional: for openPMD and PSATD+RZ support
module load cray-hdf5-parallel/1.12.2.9
export CMAKE_PREFIX_PATH=${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/c-blosc-1.21.1:$CMAKE_PREFIX_PATH
export CMAKE_PREFIX_PATH=${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/adios2-2.8.3:$CMAKE_PREFIX_PATH
export CMAKE_PREFIX_PATH=${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/blaspp-2024.05.31:$CMAKE_PREFIX_PATH
export CMAKE_PREFIX_PATH=${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/lapackpp-2024.05.31:$CMAKE_PREFIX_PATH
export CMAKE_PREFIX_PATH=${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/heffte-2.4.0:$CMAKE_PREFIX_PATH

export LD_LIBRARY_PATH=${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/c-blosc-1.21.1/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/adios2-2.8.3/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/blaspp-2024.05.31/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/lapackpp-2024.05.31/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/heffte-2.4.0/lib64:$LD_LIBRARY_PATH

export PATH=${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/adios2-2.8.3/bin:${PATH}

# optional: CCache
export PATH=/global/common/software/spackecp/perlmutter/e4s-23.08/default/spack/opt/spack/linux-sles15-zen3/gcc-11.2.0/ccache-4.8.2-cvooxdw5wgvv2g3vjxjkrpv6dopginv6/bin:$PATH

# optional: for Python bindings or libEnsemble
module load cray-python/3.11.5

if [ -d "${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/venvs/warpx-gpu" ]
then
  source ${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/venvs/warpx-gpu/bin/activate
fi

# an alias to request an interactive batch node for one hour
#   for parallel execution, start on the batch node: srun <command>
alias getNode="salloc -N 1 --ntasks-per-node=4 -t 1:00:00 -q interactive -C gpu --gpu-bind=single:1 -c 32 -G 4 -A $proj"
# an alias to run a command on a batch node for up to 30min
#   usage: runNode <command>
alias runNode="srun -N 1 --ntasks-per-node=4 -t 0:30:00 -q interactive -C gpu --gpu-bind=single:1 -c 32 -G 4 -A $proj"

# necessary to use CUDA-Aware MPI and run a job
export CRAY_ACCEL_TARGET=nvidia80

# optimize CUDA compilation for A100
export AMREX_CUDA_ARCH=8.0

# optimize CPU microarchitecture for AMD EPYC 3rd Gen (Milan/Zen3)
# note: the cc/CC/ftn wrappers below add those
export CXXFLAGS="-march=znver3"
export CFLAGS="-march=znver3"

# compiler environment hints
export CC=cc
export CXX=CC
export FC=ftn
export CUDACXX=$(which nvcc)
export CUDAHOSTCXX=CC

Edit the 2nd line of this script, which sets the export proj="" variable. Perlmutter GPU projects must end in ..._g. For example, if you are member of the project m3239, then run nano $HOME/perlmutter_gpu_warpx.profile and edit line 2 to read:

export proj="m3239_g"

Exit the nano editor with Ctrl + O (save) and then Ctrl + X (exit).

Important

Now, and as the first step on future logins to Perlmutter, activate these environment settings:

source $HOME/perlmutter_gpu_warpx.profile

Finally, since Perlmutter does not yet provide software modules for some of our dependencies, install them once:

bash $HOME/src/warpx/Tools/machines/perlmutter-nersc/install_gpu_dependencies.sh
source ${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu/venvs/warpx-gpu/bin/activate

Script Details

#!/bin/bash
#
# Copyright 2023 The WarpX Community
#
# This file is part of WarpX.
#
# Author: Axel Huebl
# License: BSD-3-Clause-LBNL

# Exit on first error encountered #############################################
#
set -eu -o pipefail


# Check: ######################################################################
#
#   Was perlmutter_gpu_warpx.profile sourced and configured correctly?
if [ -z ${proj-} ]; then echo "WARNING: The 'proj' variable is not yet set in your perlmutter_gpu_warpx.profile file! Please edit its line 2 to continue!"; exit 1; fi


# Check $proj variable is correct and has a corresponding CFS directory #######
#
if [ ! -d "${CFS}/${proj%_g}/" ]
then
    echo "WARNING: The directory ${CFS}/${proj%_g}/ does not exist!"
    echo "Is the \$proj environment variable of value \"$proj\" correctly set? "
    echo "Please edit line 2 of your perlmutter_gpu_warpx.profile file to continue!"
    exit
fi


# Remove old dependencies #####################################################
#
SW_DIR="${CFS}/${proj%_g}/${USER}/sw/perlmutter/gpu"
rm -rf ${SW_DIR}
mkdir -p ${SW_DIR}

# remove common user mistakes in python, located in .local instead of a venv
python3 -m pip uninstall -qq -y pywarpx
python3 -m pip uninstall -qq -y warpx
python3 -m pip uninstall -qqq -y mpi4py 2>/dev/null || true


# General extra dependencies ##################################################
#

# tmpfs build directory: avoids issues often seen with $HOME and is faster
build_dir=$(mktemp -d)

# c-blosc (I/O compression)
if [ -d $HOME/src/c-blosc ]
then
  cd $HOME/src/c-blosc
  git fetch --prune
  git checkout v1.21.1
  cd -
else
  git clone -b v1.21.1 https://github.com/Blosc/c-blosc.git $HOME/src/c-blosc
fi
rm -rf $HOME/src/c-blosc-pm-gpu-build
cmake -S $HOME/src/c-blosc -B ${build_dir}/c-blosc-pm-gpu-build -DBUILD_TESTS=OFF -DBUILD_BENCHMARKS=OFF -DDEACTIVATE_AVX2=OFF -DCMAKE_INSTALL_PREFIX=${SW_DIR}/c-blosc-1.21.1
cmake --build ${build_dir}/c-blosc-pm-gpu-build --target install --parallel 16
rm -rf ${build_dir}/c-blosc-pm-gpu-build

# ADIOS2
if [ -d $HOME/src/adios2 ]
then
  cd $HOME/src/adios2
  git fetch --prune
  git checkout v2.8.3
  cd -
else
  git clone -b v2.8.3 https://github.com/ornladios/ADIOS2.git $HOME/src/adios2
fi
rm -rf $HOME/src/adios2-pm-gpu-build
cmake -S $HOME/src/adios2 -B ${build_dir}/adios2-pm-gpu-build -DADIOS2_USE_Blosc=ON -DADIOS2_USE_Fortran=OFF -DADIOS2_USE_Python=OFF -DADIOS2_USE_ZeroMQ=OFF -DCMAKE_INSTALL_PREFIX=${SW_DIR}/adios2-2.8.3
cmake --build ${build_dir}/adios2-pm-gpu-build --target install -j 16
rm -rf ${build_dir}/adios2-pm-gpu-build

# BLAS++ (for PSATD+RZ)
if [ -d $HOME/src/blaspp ]
then
  cd $HOME/src/blaspp
  git fetch --prune
  git checkout v2024.05.31
  cd -
else
  git clone -b v2024.05.31 https://github.com/icl-utk-edu/blaspp.git $HOME/src/blaspp
fi
rm -rf $HOME/src/blaspp-pm-gpu-build
CXX=$(which CC) cmake -S $HOME/src/blaspp -B ${build_dir}/blaspp-pm-gpu-build -Duse_openmp=OFF -Dgpu_backend=cuda -DCMAKE_CXX_STANDARD=17 -DCMAKE_INSTALL_PREFIX=${SW_DIR}/blaspp-2024.05.31
cmake --build ${build_dir}/blaspp-pm-gpu-build --target install --parallel 16
rm -rf ${build_dir}/blaspp-pm-gpu-build

# LAPACK++ (for PSATD+RZ)
if [ -d $HOME/src/lapackpp ]
then
  cd $HOME/src/lapackpp
  git fetch --prune
  git checkout v2024.05.31
  cd -
else
  git clone -b v2024.05.31 https://github.com/icl-utk-edu/lapackpp.git $HOME/src/lapackpp
fi
rm -rf $HOME/src/lapackpp-pm-gpu-build
CXX=$(which CC) CXXFLAGS="-DLAPACK_FORTRAN_ADD_" cmake -S $HOME/src/lapackpp -B ${build_dir}/lapackpp-pm-gpu-build -DCMAKE_CXX_STANDARD=17 -Dbuild_tests=OFF -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=ON -DCMAKE_INSTALL_PREFIX=${SW_DIR}/lapackpp-2024.05.31
cmake --build ${build_dir}/lapackpp-pm-gpu-build --target install --parallel 16
rm -rf ${build_dir}/lapackpp-pm-gpu-build

# heFFTe
if [ -d $HOME/src/heffte ]
then
  cd $HOME/src/heffte
  git fetch --prune
  git checkout v2.4.0
  cd -
else
  git clone -b v2.4.0 https://github.com/icl-utk-edu/heffte.git ${HOME}/src/heffte
fi
rm -rf ${HOME}/src/heffte-pm-gpu-build
cmake \
    -S ${HOME}/src/heffte               \
    -B ${build_dir}/heffte-pm-gpu-build \
    -DBUILD_SHARED_LIBS=ON              \
    -DCMAKE_BUILD_TYPE=Release          \
    -DCMAKE_CXX_STANDARD=17             \
    -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=ON  \
    -DCMAKE_INSTALL_PREFIX=${SW_DIR}/heffte-2.4.0  \
    -DHeffte_DISABLE_GPU_AWARE_MPI=OFF  \
    -DHeffte_ENABLE_AVX=OFF             \
    -DHeffte_ENABLE_AVX512=OFF          \
    -DHeffte_ENABLE_FFTW=OFF            \
    -DHeffte_ENABLE_CUDA=ON             \
    -DHeffte_ENABLE_ROCM=OFF            \
    -DHeffte_ENABLE_ONEAPI=OFF          \
    -DHeffte_ENABLE_MKL=OFF             \
    -DHeffte_ENABLE_DOXYGEN=OFF         \
    -DHeffte_SEQUENTIAL_TESTING=OFF     \
    -DHeffte_ENABLE_TESTING=OFF         \
    -DHeffte_ENABLE_TRACING=OFF         \
    -DHeffte_ENABLE_PYTHON=OFF          \
    -DHeffte_ENABLE_FORTRAN=OFF         \
    -DHeffte_ENABLE_SWIG=OFF            \
    -DHeffte_ENABLE_MAGMA=OFF
cmake --build ${build_dir}/heffte-pm-gpu-build --target install --parallel 16
rm -rf ${build_dir}/heffte-pm-gpu-build


# Python ######################################################################
#
python3 -m pip install --upgrade pip
python3 -m pip install --upgrade virtualenv
python3 -m pip cache purge
rm -rf ${SW_DIR}/venvs/warpx-gpu
python3 -m venv ${SW_DIR}/venvs/warpx-gpu
source ${SW_DIR}/venvs/warpx-gpu/bin/activate
python3 -m pip install --upgrade pip
python3 -m pip install --upgrade build
python3 -m pip install --upgrade packaging
python3 -m pip install --upgrade wheel
python3 -m pip install --upgrade setuptools
python3 -m pip install --upgrade cython
python3 -m pip install --upgrade numpy
python3 -m pip install --upgrade pandas
python3 -m pip install --upgrade scipy
MPICC="cc -target-accel=nvidia80 -shared" python3 -m pip install --upgrade mpi4py --no-cache-dir --no-build-isolation --no-binary mpi4py
python3 -m pip install --upgrade openpmd-api
python3 -m pip install --upgrade matplotlib
python3 -m pip install --upgrade yt
# install or update WarpX dependencies
python3 -m pip install --upgrade -r $HOME/src/warpx/requirements.txt
python3 -m pip install --upgrade cupy-cuda12x  # CUDA 12 compatible wheel
# optimas (based on libEnsemble & ax->botorch->gpytorch->pytorch)
python3 -m pip install --upgrade torch  # CUDA 12 compatible wheel
python3 -m pip install --upgrade optimas[all]


# remove build temporary directory
rm -rf ${build_dir}

CPU Nodes

We use system software modules, add environment hints and further dependencies via the file $HOME/perlmutter_cpu_warpx.profile. Create it now:

cp $HOME/src/warpx/Tools/machines/perlmutter-nersc/perlmutter_cpu_warpx.profile.example $HOME/perlmutter_cpu_warpx.profile

Script Details

# please set your project account
export proj=""  # change me!

# remembers the location of this script
export MY_PROFILE=$(cd $(dirname $BASH_SOURCE) && pwd)"/"$(basename $BASH_SOURCE)
if [ -z ${proj-} ]; then echo "WARNING: The 'proj' variable is not yet set in your $MY_PROFILE file! Please edit its line 2 to continue!"; return; fi

# required dependencies
module load cpu
module load cmake/3.24.3
module load cray-fftw/3.3.10.6

# optional: for QED support with detailed tables
export BOOST_ROOT=/global/common/software/spackecp/perlmutter/e4s-23.05/default/spack/opt/spack/linux-sles15-zen3/gcc-11.2.0/boost-1.82.0-ow5r5qrgslcwu33grygouajmuluzuzv3

# optional: for openPMD and PSATD+RZ support
module load cray-hdf5-parallel/1.12.2.9
export CMAKE_PREFIX_PATH=${CFS}/${proj}/${USER}/sw/perlmutter/cpu/c-blosc-1.21.1:$CMAKE_PREFIX_PATH
export CMAKE_PREFIX_PATH=${CFS}/${proj}/${USER}/sw/perlmutter/cpu/adios2-2.8.3:$CMAKE_PREFIX_PATH
export CMAKE_PREFIX_PATH=${CFS}/${proj}/${USER}/sw/perlmutter/cpu/blaspp-2024.05.31:$CMAKE_PREFIX_PATH
export CMAKE_PREFIX_PATH=${CFS}/${proj}/${USER}/sw/perlmutter/cpu/lapackpp-2024.05.31:$CMAKE_PREFIX_PATH
export CMAKE_PREFIX_PATH=${CFS}/${proj}/${USER}/sw/perlmutter/cpu/heffte-2.4.0:$CMAKE_PREFIX_PATH

export LD_LIBRARY_PATH=${CFS}/${proj}/${USER}/sw/perlmutter/cpu/c-blosc-1.21.1/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=${CFS}/${proj}/${USER}/sw/perlmutter/cpu/adios2-2.8.3/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=${CFS}/${proj}/${USER}/sw/perlmutter/cpu/blaspp-2024.05.31/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=${CFS}/${proj}/${USER}/sw/perlmutter/cpu/lapackpp-2024.05.31/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=${CFS}/${proj}/${USER}/sw/perlmutter/cpu/heffte-2.4.0/lib64:$LD_LIBRARY_PATH

export PATH=${CFS}/${proj}/${USER}/sw/perlmutter/cpu/adios2-2.8.3/bin:${PATH}

# optional: CCache
export PATH=/global/common/software/spackecp/perlmutter/e4s-23.08/default/spack/opt/spack/linux-sles15-zen3/gcc-11.2.0/ccache-4.8.2-cvooxdw5wgvv2g3vjxjkrpv6dopginv6/bin:$PATH

# optional: for Python bindings or libEnsemble
module load cray-python/3.11.5

if [ -d "${CFS}/${proj}/${USER}/sw/perlmutter/cpu/venvs/warpx-cpu" ]
then
  source ${CFS}/${proj}/${USER}/sw/perlmutter/cpu/venvs/warpx-cpu/bin/activate
fi

# an alias to request an interactive batch node for one hour
#   for parallel execution, start on the batch node: srun <command>
alias getNode="salloc --nodes 1 --qos interactive --time 01:00:00 --constraint cpu --account=$proj"
# an alias to run a command on a batch node for up to 30min
#   usage: runNode <command>
alias runNode="srun --nodes 1 --qos interactive --time 01:00:00 --constraint cpu $proj"

# optimize CPU microarchitecture for AMD EPYC 3rd Gen (Milan/Zen3)
# note: the cc/CC/ftn wrappers below add those
export CXXFLAGS="-march=znver3"
export CFLAGS="-march=znver3"

# compiler environment hints
export CC=cc
export CXX=CC
export FC=ftn

Edit the 2nd line of this script, which sets the export proj="" variable. For example, if you are member of the project m3239, then run nano $HOME/perlmutter_cpu_warpx.profile and edit line 2 to read:

export proj="m3239"

Exit the nano editor with Ctrl + O (save) and then Ctrl + X (exit).

Important

Now, and as the first step on future logins to Perlmutter, activate these environment settings:

source $HOME/perlmutter_cpu_warpx.profile

Finally, since Perlmutter does not yet provide software modules for some of our dependencies, install them once:

bash $HOME/src/warpx/Tools/machines/perlmutter-nersc/install_cpu_dependencies.sh
source ${CFS}/${proj}/${USER}/sw/perlmutter/cpu/venvs/warpx-cpu/bin/activate

Script Details

#!/bin/bash
#
# Copyright 2023 The WarpX Community
#
# This file is part of WarpX.
#
# Author: Axel Huebl
# License: BSD-3-Clause-LBNL

# Exit on first error encountered #############################################
#
set -eu -o pipefail


# Check: ######################################################################
#
#   Was perlmutter_cpu_warpx.profile sourced and configured correctly?
if [ -z ${proj-} ]; then echo "WARNING: The 'proj' variable is not yet set in your perlmutter_cpu_warpx.profile file! Please edit its line 2 to continue!"; exit 1; fi


# Check $proj variable is correct and has a corresponding CFS directory #######
#
if [ ! -d "${CFS}/${proj}/" ]
then
    echo "WARNING: The directory ${CFS}/${proj}/ does not exist!"
    echo "Is the \$proj environment variable of value \"$proj\" correctly set? "
    echo "Please edit line 2 of your perlmutter_cpu_warpx.profile file to continue!"
    exit
fi


# Remove old dependencies #####################################################
#
SW_DIR="${CFS}/${proj}/${USER}/sw/perlmutter/cpu"
rm -rf ${SW_DIR}
mkdir -p ${SW_DIR}

# remove common user mistakes in python, located in .local instead of a venv
python3 -m pip uninstall -qq -y pywarpx
python3 -m pip uninstall -qq -y warpx
python3 -m pip uninstall -qqq -y mpi4py 2>/dev/null || true


# General extra dependencies ##################################################
#

# tmpfs build directory: avoids issues often seen with $HOME and is faster
build_dir=$(mktemp -d)

# c-blosc (I/O compression)
if [ -d $HOME/src/c-blosc ]
then
  cd $HOME/src/c-blosc
  git fetch --prune
  git checkout v1.21.1
  cd -
else
  git clone -b v1.21.1 https://github.com/Blosc/c-blosc.git $HOME/src/c-blosc
fi
rm -rf $HOME/src/c-blosc-pm-cpu-build
cmake -S $HOME/src/c-blosc -B ${build_dir}/c-blosc-pm-cpu-build -DBUILD_TESTS=OFF -DBUILD_BENCHMARKS=OFF -DDEACTIVATE_AVX2=OFF -DCMAKE_INSTALL_PREFIX=${SW_DIR}/c-blosc-1.21.1
cmake --build ${build_dir}/c-blosc-pm-cpu-build --target install --parallel 16
rm -rf ${build_dir}/c-blosc-pm-cpu-build

# ADIOS2
if [ -d $HOME/src/adios2 ]
then
  cd $HOME/src/adios2
  git fetch --prune
  git checkout v2.8.3
  cd -
else
  git clone -b v2.8.3 https://github.com/ornladios/ADIOS2.git $HOME/src/adios2
fi
rm -rf $HOME/src/adios2-pm-cpu-build
cmake -S $HOME/src/adios2 -B ${build_dir}/adios2-pm-cpu-build -DADIOS2_USE_Blosc=ON -DADIOS2_USE_CUDA=OFF -DADIOS2_USE_Fortran=OFF -DADIOS2_USE_Python=OFF -DADIOS2_USE_ZeroMQ=OFF -DCMAKE_INSTALL_PREFIX=${SW_DIR}/adios2-2.8.3
cmake --build ${build_dir}/adios2-pm-cpu-build --target install -j 16
rm -rf ${build_dir}/adios2-pm-cpu-build

# BLAS++ (for PSATD+RZ)
if [ -d $HOME/src/blaspp ]
then
  cd $HOME/src/blaspp
  git fetch --prune
  git checkout v2024.05.31
  cd -
else
  git clone -b v2024.05.31 https://github.com/icl-utk-edu/blaspp.git $HOME/src/blaspp
fi
rm -rf $HOME/src/blaspp-pm-cpu-build
CXX=$(which CC) cmake -S $HOME/src/blaspp -B ${build_dir}/blaspp-pm-cpu-build -Duse_openmp=ON -Dgpu_backend=OFF -DCMAKE_CXX_STANDARD=17 -DCMAKE_INSTALL_PREFIX=${SW_DIR}/blaspp-2024.05.31
cmake --build ${build_dir}/blaspp-pm-cpu-build --target install --parallel 16
rm -rf ${build_dir}/blaspp-pm-cpu-build

# LAPACK++ (for PSATD+RZ)
if [ -d $HOME/src/lapackpp ]
then
  cd $HOME/src/lapackpp
  git fetch --prune
  git checkout v2024.05.31
  cd -
else
  git clone -b v2024.05.31 https://github.com/icl-utk-edu/lapackpp.git $HOME/src/lapackpp
fi
rm -rf $HOME/src/lapackpp-pm-cpu-build
CXX=$(which CC) CXXFLAGS="-DLAPACK_FORTRAN_ADD_" cmake -S $HOME/src/lapackpp -B ${build_dir}/lapackpp-pm-cpu-build -DCMAKE_CXX_STANDARD=17 -Dbuild_tests=OFF -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=ON -DCMAKE_INSTALL_PREFIX=${SW_DIR}/lapackpp-2024.05.31
cmake --build ${build_dir}/lapackpp-pm-cpu-build --target install --parallel 16
rm -rf ${build_dir}/lapackpp-pm-cpu-build

# heFFTe
if [ -d $HOME/src/heffte ]
then
  cd $HOME/src/heffte
  git fetch --prune
  git checkout v2.4.0
  cd -
else
  git clone -b v2.4.0 https://github.com/icl-utk-edu/heffte.git ${HOME}/src/heffte
fi
rm -rf ${HOME}/src/heffte-pm-cpu-build
cmake \
    -S ${HOME}/src/heffte               \
    -B ${build_dir}/heffte-pm-cpu-build \
    -DBUILD_SHARED_LIBS=ON              \
    -DCMAKE_BUILD_TYPE=Release          \
    -DCMAKE_CXX_STANDARD=17             \
    -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=ON  \
    -DCMAKE_INSTALL_PREFIX=${SW_DIR}/heffte-2.4.0  \
    -DHeffte_DISABLE_GPU_AWARE_MPI=ON   \
    -DHeffte_ENABLE_AVX=ON              \
    -DHeffte_ENABLE_AVX512=OFF          \
    -DHeffte_ENABLE_FFTW=ON             \
    -DHeffte_ENABLE_CUDA=OFF            \
    -DHeffte_ENABLE_ROCM=OFF            \
    -DHeffte_ENABLE_ONEAPI=OFF          \
    -DHeffte_ENABLE_MKL=OFF             \
    -DHeffte_ENABLE_DOXYGEN=OFF         \
    -DHeffte_SEQUENTIAL_TESTING=OFF     \
    -DHeffte_ENABLE_TESTING=OFF         \
    -DHeffte_ENABLE_TRACING=OFF         \
    -DHeffte_ENABLE_PYTHON=OFF          \
    -DHeffte_ENABLE_FORTRAN=OFF         \
    -DHeffte_ENABLE_SWIG=OFF            \
    -DHeffte_ENABLE_MAGMA=OFF
cmake --build ${build_dir}/heffte-pm-cpu-build --target install --parallel 16
rm -rf ${build_dir}/heffte-pm-cpu-build


# Python ######################################################################
#
python3 -m pip install --upgrade pip
python3 -m pip install --upgrade virtualenv
python3 -m pip cache purge
rm -rf ${SW_DIR}/venvs/warpx-cpu
python3 -m venv ${SW_DIR}/venvs/warpx-cpu
source ${SW_DIR}/venvs/warpx-cpu/bin/activate
python3 -m pip install --upgrade pip
python3 -m pip install --upgrade build
python3 -m pip install --upgrade packaging
python3 -m pip install --upgrade wheel
python3 -m pip install --upgrade setuptools
python3 -m pip install --upgrade cython
python3 -m pip install --upgrade numpy
python3 -m pip install --upgrade pandas
python3 -m pip install --upgrade scipy
MPICC="cc -shared" python3 -m pip install --upgrade mpi4py --no-cache-dir --no-build-isolation --no-binary mpi4py
python3 -m pip install --upgrade openpmd-api
python3 -m pip install --upgrade matplotlib
python3 -m pip install --upgrade yt
# install or update WarpX dependencies
python3 -m pip install --upgrade -r $HOME/src/warpx/requirements.txt
# optimas (based on libEnsemble & ax->botorch->gpytorch->pytorch)
python3 -m pip install --upgrade torch --index-url https://download.pytorch.org/whl/cpu
python3 -m pip install --upgrade optimas[all]


# remove build temporary directory
rm -rf ${build_dir}

Compilation

Use the following cmake commands to compile the application executable:

A100 GPUs

cd $HOME/src/warpx
rm -rf build_pm_gpu

cmake -S . -B build_pm_gpu -DWarpX_COMPUTE=CUDA -DWarpX_FFT=ON -DWarpX_HEFFTE=ON -DWarpX_QED_TABLE_GEN=ON -DWarpX_DIMS="1;2;RZ;3"
cmake --build build_pm_gpu -j 16

The WarpX application executables are now in $HOME/src/warpx/build_pm_gpu/bin/. Additionally, the following commands will install WarpX as a Python module:

cd $HOME/src/warpx
rm -rf build_pm_gpu_py

cmake -S . -B build_pm_gpu_py -DWarpX_COMPUTE=CUDA -DWarpX_FFT=ON -DWarpX_HEFFTE=ON -DWarpX_QED_TABLE_GEN=ON -DWarpX_APP=OFF -DWarpX_PYTHON=ON -DWarpX_DIMS="1;2;RZ;3"
cmake --build build_pm_gpu_py -j 16 --target pip_install

CPU Nodes

cd $HOME/src/warpx
rm -rf build_pm_cpu

cmake -S . -B build_pm_cpu -DWarpX_COMPUTE=OMP -DWarpX_FFT=ON -DWarpX_HEFFTE=ON -DWarpX_QED_TABLE_GEN=ON -DWarpX_DIMS="1;2;RZ;3"
cmake --build build_pm_cpu -j 16

The WarpX application executables are now in $HOME/src/warpx/build_pm_cpu/bin/. Additionally, the following commands will install WarpX as a Python module:

rm -rf build_pm_cpu_py

cmake -S . -B build_pm_cpu_py -DWarpX_COMPUTE=OMP -DWarpX_FFT=ON -DWarpX_HEFFTE=ON -DWarpX_QED_TABLE_GEN=ON -DWarpX_APP=OFF -DWarpX_PYTHON=ON -DWarpX_DIMS="1;2;RZ;3"
cmake --build build_pm_cpu_py -j 16 --target pip_install

Now, you can submit Perlmutter compute jobs for WarpX Python (PICMI) scripts (example scripts). Or, you can use the WarpX executables to submit Perlmutter jobs (example inputs). For executables, you can reference their location in your job script or copy them to a location in $PSCRATCH.

Update WarpX & Dependencies

If you already installed WarpX in the past and want to update it, start by getting the latest source code:

cd $HOME/src/warpx

# read the output of this command - does it look ok?
git status

# get the latest WarpX source code
git fetch
git pull

# read the output of these commands - do they look ok?
git status
git log # press q to exit

And, if needed,

update the perlmutter_gpu_warpx.profile or perlmutter_cpu_warpx files,
log out and into the system, activate the now updated environment profile as usual,
execute the dependency install scripts.

As a last step, clean the build directory rm -rf $HOME/src/warpx/build_pm_* and rebuild WarpX.

Running

A100 (40GB) GPUs

The batch script below can be used to run a WarpX simulation on multiple nodes (change -N accordingly) on the supercomputer Perlmutter at NERSC. This partition as up to 1536 nodes.

Replace descriptions between chevrons <> by relevant values, for instance <input file> could be plasma_mirror_inputs. Note that we run one MPI rank per GPU.

Listing 17 You can copy this file from $HOME/src/warpx/Tools/machines/perlmutter-nersc/perlmutter_gpu.sbatch.

#!/bin/bash -l

# Copyright 2021-2023 Axel Huebl, Kevin Gott
#
# This file is part of WarpX.
#
# License: BSD-3-Clause-LBNL

#SBATCH -t 00:10:00
#SBATCH -N 2
#SBATCH -J WarpX
#    note: <proj> must end on _g
#SBATCH -A <proj>
#SBATCH -q regular
# A100 40GB (most nodes)
#SBATCH -C gpu
# A100 80GB (256 nodes)
#S BATCH -C gpu&hbm80g
#SBATCH --exclusive
# ideally single:1, but NERSC cgroups issue
#SBATCH --gpu-bind=none
#SBATCH --ntasks-per-node=4
#SBATCH --gpus-per-node=4
#SBATCH -o WarpX.o%j
#SBATCH -e WarpX.e%j

# executable & inputs file or python interpreter & PICMI script here
EXE=./warpx
INPUTS=inputs

# pin to closest NIC to GPU
export MPICH_OFI_NIC_POLICY=GPU

# threads for OpenMP and threaded compressors per MPI rank
#   note: 16 avoids hyperthreading (32 virtual cores, 16 physical)
export SRUN_CPUS_PER_TASK=16
export OMP_NUM_THREADS=${SRUN_CPUS_PER_TASK}

# GPU-aware MPI optimizations
GPU_AWARE_MPI="amrex.use_gpu_aware_mpi=1"

# CUDA visible devices are ordered inverse to local task IDs
#   Reference: nvidia-smi topo -m
srun --cpu-bind=cores bash -c "
    export CUDA_VISIBLE_DEVICES=\$((3-SLURM_LOCALID));
    ${EXE} ${INPUTS} ${GPU_AWARE_MPI}" \
  > output.txt

To run a simulation, copy the lines above to a file perlmutter_gpu.sbatch and run

sbatch perlmutter_gpu.sbatch

to submit the job.

A100 (80GB) GPUs

Perlmutter has 256 nodes that provide 80 GB HBM per A100 GPU. In the A100 (40GB) batch script, replace -C gpu with -C gpu&hbm80g to use these large-memory GPUs.

CPU Nodes

The Perlmutter CPU partition as up to 3072 nodes, each with 2x AMD EPYC 7763 CPUs.

Listing 18 You can copy this file from $HOME/src/warpx/Tools/machines/perlmutter-nersc/perlmutter_cpu.sbatch.

#!/bin/bash -l

# Copyright 2021-2023 WarpX
#
# This file is part of WarpX.
#
# Authors: Axel Huebl
# License: BSD-3-Clause-LBNL

#SBATCH -t 00:10:00
#SBATCH -N 2
#SBATCH -J WarpX
#SBATCH -A <proj>
#SBATCH -q regular
#SBATCH -C cpu
#SBATCH --ntasks-per-node=16
#SBATCH --exclusive
#SBATCH -o WarpX.o%j
#SBATCH -e WarpX.e%j

# executable & inputs file or python interpreter & PICMI script here
EXE=./warpx
INPUTS=inputs_small

# each CPU node on Perlmutter (NERSC) has 64 hardware cores with
# 2x Hyperthreading/SMP
# https://en.wikichip.org/wiki/amd/epyc/7763
# https://www.amd.com/en/products/cpu/amd-epyc-7763
# Each CPU is made up of 8 chiplets, each sharing 32MB L3 cache.
# This will be our MPI rank assignment (2x8 is 16 ranks/node).

# threads for OpenMP and threaded compressors per MPI rank
export SRUN_CPUS_PER_TASK=16  # 8 cores per chiplet, 2x SMP
export OMP_PLACES=threads
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=${SRUN_CPUS_PER_TASK}

srun --cpu-bind=cores \
  ${EXE} ${INPUTS} \
  > output.txt

Post-Processing

For post-processing, most users use Python via NERSC’s Jupyter service (documentation).

As a one-time preparatory setup, log into Perlmutter via SSH and do not source the WarpX profile script above. Create your own Conda environment and Jupyter kernel for post-processing:

module load python

conda config --set auto_activate_base false

# create conda environment
rm -rf $HOME/.conda/envs/warpx-pm-postproc
conda create --yes -n warpx-pm-postproc -c conda-forge mamba conda-libmamba-solver
conda activate warpx-pm-postproc
conda config --set solver libmamba
mamba install --yes -c conda-forge python ipykernel ipympl matplotlib numpy pandas yt openpmd-viewer openpmd-api h5py fast-histogram dask dask-jobqueue pyarrow

# create Jupyter kernel
rm -rf $HOME/.local/share/jupyter/kernels/warpx-pm-postproc/
python -m ipykernel install --user --name warpx-pm-postproc --display-name WarpX-PM-PostProcessing
echo -e '#!/bin/bash\nmodule load python\nsource activate warpx-pm-postproc\nexec "$@"' > $HOME/.local/share/jupyter/kernels/warpx-pm-postproc/kernel-helper.sh
chmod a+rx $HOME/.local/share/jupyter/kernels/warpx-pm-postproc/kernel-helper.sh
KERNEL_STR=$(jq '.argv |= ["{resource_dir}/kernel-helper.sh"] + .' $HOME/.local/share/jupyter/kernels/warpx-pm-postproc/kernel.json | jq '.argv[1] = "python"')
echo ${KERNEL_STR} | jq > $HOME/.local/share/jupyter/kernels/warpx-pm-postproc/kernel.json

exit

When opening a Jupyter notebook on https://jupyter.nersc.gov, just select WarpX-PM-PostProcessing from the list of available kernels on the top right of the notebook.

Additional software can be installed later on, e.g., in a Jupyter cell using !mamba install -y -c conda-forge .... Software that is not available via conda can be installed via !python -m pip install ....