Lawrencium (LBNL)

The Lawrencium cluster is located at LBNL.


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


Use the following commands to download the WarpX source code and switch to the correct branch:

git clone $HOME/src/warpx

We use the following modules and environments on the system ($HOME/lawrencium_warpx.profile).

Listing 11 You can copy this file from Tools/machines/lawrencium-lbnl/lawrencium_warpx.profile.example.
# please set your project account
#export proj="<yourProject>"  # change me, e.g., ac_blast

# required dependencies
module load cmake/3.24.1
module load cuda/11.4
module load gcc/7.4.0
module load openmpi/4.0.1-gcc

# optional: for QED support with detailed tables
module load boost/1.70.0-gcc

# optional: for openPMD and PSATD+RZ support
module load hdf5/1.10.5-gcc-p
module load lapack/3.8.0-gcc
# CPU only:
#module load fftw/3.3.8-gcc

export CMAKE_PREFIX_PATH=$HOME/sw/v100/c-blosc-1.21.1:$CMAKE_PREFIX_PATH
export CMAKE_PREFIX_PATH=$HOME/sw/v100/adios2-2.8.3:$CMAKE_PREFIX_PATH
export CMAKE_PREFIX_PATH=$HOME/sw/v100/blaspp-2024.05.31:$CMAKE_PREFIX_PATH
export CMAKE_PREFIX_PATH=$HOME/sw/v100/lapackpp-2024.05.31:$CMAKE_PREFIX_PATH

export PATH=$HOME/sw/v100/adios2-2.8.3/bin:$PATH

# optional: CCache
#module load ccache  # missing

# optional: for Python bindings or libEnsemble
module load python/3.8.8

if [ -d "$HOME/sw/v100/venvs/warpx" ]
  source $HOME/sw/v100/venvs/warpx/bin/activate

# 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 -t 1:00:00 --qos=es_debug --partition=es1 --constraint=es1_v100 --gres=gpu:1 --cpus-per-task=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 -t 1:00:00 --qos=es_debug --partition=es1 --constraint=es1_v100 --gres=gpu:1 --cpus-per-task=4 -A $proj"

# optimize CUDA compilation for 1080 Ti (deprecated)
#export AMREX_CUDA_ARCH=6.1
# optimize CUDA compilation for V100
export AMREX_CUDA_ARCH=7.0
# optimize CUDA compilation for 2080 Ti
#export AMREX_CUDA_ARCH=7.5

# compiler environment hints
export CXX=$(which g++)
export CC=$(which gcc)
export FC=$(which gfortran)
export CUDACXX=$(which nvcc)

We recommend to store the above lines in a file, such as $HOME/lawrencium_warpx.profile, and load it into your shell after a login:

source $HOME/lawrencium_warpx.profile

And since Lawrencium does not yet provide a module for them, install ADIOS2, BLAS++ and LAPACK++:

# c-blosc (I/O compression)
git clone -b v1.21.1 src/c-blosc
rm -rf src/c-blosc-v100-build
cmake -S src/c-blosc -B src/c-blosc-v100-build -DBUILD_TESTS=OFF -DBUILD_BENCHMARKS=OFF -DDEACTIVATE_AVX2=OFF -DCMAKE_INSTALL_PREFIX=$HOME/sw/v100/c-blosc-1.21.1
cmake --build src/c-blosc-v100-build --target install --parallel 12

git clone -b v2.8.3 src/adios2
rm -rf src/adios2-v100-build
cmake -S src/adios2 -B src/adios2-v100-build -DADIOS2_USE_Blosc=ON -DADIOS2_USE_Fortran=OFF -DADIOS2_USE_Python=OFF -DADIOS2_USE_ZeroMQ=OFF -DCMAKE_INSTALL_PREFIX=$HOME/sw/v100/adios2-2.8.3
cmake --build src/adios2-v100-build --target install -j 12

# BLAS++ (for PSATD+RZ)
git clone src/blaspp
rm -rf src/blaspp-v100-build
cmake -S src/blaspp -B src/blaspp-v100-build -Duse_openmp=OFF -Dgpu_backend=cuda -DCMAKE_CXX_STANDARD=17 -DCMAKE_INSTALL_PREFIX=$HOME/sw/v100/blaspp-master
cmake --build src/blaspp-v100-build --target install --parallel 12

git clone src/lapackpp
rm -rf src/lapackpp-v100-build
cmake -S src/lapackpp -B src/lapackpp-v100-build -DCMAKE_CXX_STANDARD=17 -Dgpu_backend=cuda -Dbuild_tests=OFF -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=ON -DCMAKE_INSTALL_PREFIX=$HOME/sw/v100/lapackpp-master -Duse_cmake_find_lapack=ON -DBLAS_LIBRARIES=${LAPACK_DIR}/lib/libblas.a -DLAPACK_LIBRARIES=${LAPACK_DIR}/lib/liblapack.a
cmake --build src/lapackpp-v100-build --target install --parallel 12

Optionally, download and install Python packages for PICMI or dynamic ensemble optimizations (libEnsemble):

python3 -m pip install --user --upgrade pip
python3 -m pip install --user virtualenv
python3 -m pip cache purge
rm -rf $HOME/sw/v100/venvs/warpx
python3 -m venv $HOME/sw/v100/venvs/warpx
source $HOME/sw/v100/venvs/warpx/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
python3 -m pip install --upgrade mpi4py --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
# optional: for libEnsemble
python3 -m pip install -r $HOME/src/warpx/Tools/LibEnsemble/requirements.txt

Then, cd into the directory $HOME/src/warpx and use the following commands to compile the application executable:

cd $HOME/src/warpx
rm -rf build

cmake -S . -B build -DWarpX_DIMS="1;2;RZ;3" -DWarpX_COMPUTE=CUDA -DWarpX_FFT=ON -DWarpX_QED_TABLE_GEN=ON
cmake --build build -j 12

The general cmake compile-time options apply as usual.

That’s it! A 3D WarpX executable is now in build/bin/ and can be run with a 3D example inputs file. Most people execute the binary directly or copy it out to a location in /global/scratch/users/$USER/.

For a full PICMI install, follow the instructions for Python (PICMI) bindings:

# PICMI build
cd $HOME/src/warpx

# install or update dependencies
python3 -m pip install -r requirements.txt

# compile parallel PICMI interfaces in 3D, 2D, 1D and RZ
WARPX_MPI=ON WARPX_COMPUTE=CUDA WARPX_FFT=ON BUILD_PARALLEL=12 python3 -m pip install --force-reinstall --no-deps -v .

Or, if you are developing, do a quick PICMI install of a single geometry (see: WarpX_DIMS) using:

# find dependencies & configure

# build and then call "python3 -m pip install ..."
cmake --build build --target pip_install -j 12


V100 GPUs (16 GB)

12 nodes with each two NVIDIA V100 GPUs.

Listing 12 You can copy this file from Tools/machines/lawrencium-lbnl/lawrencium_v100.sbatch.
#!/bin/bash -l

# Copyright 2023 The WarpX Community
# Author: Axel Huebl
# License: BSD-3-Clause-LBNL

#SBATCH -t 00:10:00
#SBATCH --job-name=WarpX
#SBATCH --account=<proj>
#SBATCH --qos=es_normal
# 2xV100 nodes
#SBATCH --partition=es1
#SBATCH --constraint=es1_v100
#SBATCH --gres=gpu:1
#SBATCH --cpus-per-task=4
#SBATCH -o WarpX.o%j
#SBATCH -e WarpX.e%j
#S BATCH --mail-type=all

# executable & inputs file or python interpreter & PICMI script here

srun ${EXE} ${INPUTS} \
  > output_${SLURM_JOB_ID}.txt

To run a simulation, copy the lines above to a file v100.sbatch and run

sbatch lawrencium_v100.sbatch

2080 Ti GPUs (10 GB)

18 nodes with each four NVIDIA 2080 TI GPUs. These are most interesting if you run in single precision.

Use --constraint=es1_2080ti --cpus-per-task=2 in the above template to run on those nodes.