Independent Parallel Particle Layer (IPPL)

Independent Parallel Particle Layer (IPPL) is a performance portable C++ library for Particle-Mesh methods. IPPL makes use of Kokkos (https://github.com/kokkos/kokkos), HeFFTe (https://github.com/icl-utk-edu/heffte), and MPI (Message Passing Interface) to deliver a portable, massively parallel toolkit for particle-mesh methods. IPPL supports simulations in one to six dimensions, mixed precision, and asynchronous execution in different execution spaces (e.g. CPUs and GPUs).

All IPPL releases (< 3.2.0) are available under the BSD 3-clause license. Since version 3.2.0, this repository includes a modified version of the variant header by GNU, created to support compilation under CUDA 12.2 with GCC 12.3.0. This header file is available under the same terms as the GNU Standard Library; note the GNU runtime library exception. As long as this file is not removed, IPPL is available under GNU GPL version 3.

Installing IPPL and its dependencies

All the new developments of IPPL are merged into the master branch which can make it potentially unstable from time to time. So if you want a stable and more tested version please checkout the tagged branch correspodning to the latest release (e.g. git checkout tags/IPPL-x.x.x). Otherwise if you want the latest developments go with the master with the above caveat in mind.

Requirements

• CMake
• A C++ compilation toolchain (GPU-capable for GPU builds, e.g. nvcc, clang or rocmcc)
• MPI (GPU-aware if building for GPUs)

Optional requirements

• FFTW
• CuFFT

Compilation

IPPL is a CMake Project and can be configured by passing options in CMake syntax:

cmake <src_dir> -D<option>=<value>

None of the options have to be set explicitly, all have a default.

The relevant options of IPPL are

• IPPL_PLATFORMS, can be one of SERIAL, OPENMP, CUDA, "OPENMP;CUDA", default SERIAL
• Kokkos_VERSION, default 4.1.00
• Heffte_VERSION, default MASTER
  • If set to MASTER, an additional flag Heffte_COMMIT_HASH can be set, default 9eab7c0eb18e86acaccc2b5699b30e85a9e7bdda
  • Currently, this is the only compatible commit of Heffte
• ENABLE_SOLVERS, default OFF
• ENABLE_FFT, default OFF
  • If ENABLE_FFT is set, Heffte_ENABLE_CUDA will default to ON if IPPL_PLATFORMS contains cuda
  • Otherwise, Heffte_ENABLE_AVX2 is enabled. FFTW has to be enabled explicitly.
• Heffte_ENABLE_FFTW, default OFF
• ENABLE_TESTS, default OFF
• ENABLE_UNIT_TESTS, default OFF
• ENABLE_ALPINE, default OFF
• USE_ALTERNATIVE_VARIANT, default OFF. Can turned on for GPU builds where the use of the system-provided variant doesn't work.

Furthermore, be aware of CMAKE_BUILD_TYPE, which can be either

• Release for optimized builds
• RelWithDebInfo for optimized builds with debug info (default)
• Debug for debug builds (with Sanitizers enabled)

Examples

Download and setup a build directory:

https://github.com/IPPL-framework/ippl
cd ippl
mkdir build
cd build

Serial debug build with tests and newest Kokkos

cmake .. -DCMAKE_BUILD_TYPE=Debug -DCMAKE_CXX_STANDARD=20 -DENABLE_TESTS=True -DKokkos_VERSION=4.2.00

OpenMP release build with alpine and FFTW

cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_STANDARD=20 -DENABLE_FFT=ON -DENABLE_SOLVERS=ON -DENABLE_ALPINE=True -DENABLE_TESTS=ON -DIPPL_PLATFORMS=openmp -DHeffte_ENABLE_FFTW=True

Cuda alpine release build

cmake .. -DCMAKE_BUILD_TYPE=Release -DKokkos_ARCH_[architecture]=ON -DCMAKE_CXX_STANDARD=20 -DENABLE_FFT=ON -DENABLE_TESTS=ON -DUSE_ALTERNATIVE_VARIANT=ON -DENABLE_SOLVERS=ON -DENABLE_ALPINE=True -DIPPL_PLATFORMS=cuda

[architecture] should be the target architecture, e.g.

• PASCAL60
• PASCAL61
• VOLTA70
• VOLTA72
• TURING75
• AMPERE80 (PSI GWENDOLEN machine)
• AMPERE86

Contributions

We are open and welcome contributions from others. Please open an issue and a corresponding pull request in the main repository if it is a bug fix or a minor change.

For larger projects we recommend to fork the main repository and then submit a pull request from it. More information regarding github workflow for forks can be found in this page and how to submit a pull request from a fork can be found here. Please follow the coding guidelines as mentioned in this page.

You can add an upstream to be able to get all the latest changes from the master. For example, if you are working with a fork of the main repository, you can add the upstream by:

$ git remote add upstream [email protected]:IPPL-framework/ippl.git

You can then easily pull by typing

$ git pull upstream master

All the contributions (except for bug fixes) need to be accompanied with a unit test. For more information on unit tests in IPPL please take a look at this page.

Citing IPPL

@inproceedings{muralikrishnan2024scaling,
  title={Scaling and performance portability of the particle-in-cell scheme for plasma physics applications
         through mini-apps targeting exascale architectures},
  author={Muralikrishnan, Sriramkrishnan and Frey, Matthias and Vinciguerra, Alessandro and Ligotino, Michael
          and Cerfon, Antoine J and Stoyanov, Miroslav and Gayatri, Rahulkumar and Adelmann, Andreas},
  booktitle={Proceedings of the 2024 SIAM Conference on Parallel Processing for Scientific Computing (PP)},
  pages={26--38},
  year={2024},
  organization={SIAM}
}

Job scripts for running on Merlin and Gwendolen (at PSI)

You can use the following example job scripts to run on the local PSI computing cluster, which uses slurm. More documentation on the local cluster can be found here (need to be in the PSI network to access).

Merlin CPU (MPI + OpenMP)

For example, to run a job on 1 MPI node, with 44 OpenMP threads:

#!/bin/bash
#SBATCH --partition=hourly      # Using 'hourly' will grant higher priority
#SBATCH --nodes=1               # No. of nodes
#SBATCH --ntasks-per-node=1     # No. of MPI ranks per node. Merlin CPU nodes have 44 cores
#SBATCH --cpus-per-task=44      # No. of OMP threads
#SBATCH --time=00:05:00         # Define max time job will run (e.g. here 5 mins)
#SBATCH --hint=nomultithread    # Without hyperthreading
##SBATCH --exclusive            # The allocations will be exclusive if turned on (remove extra hashtag to turn on)

#SBATCH --output=<output_file_name>.out  # Name of output file
#SBATCH --error=<error_file_name>.err    # Name of error file

export OMP_NUM_THREADS=44
export OMP_PROC_BIND=spread
export OMP_PLACES=threads

# need to pass the --cpus-per-task option to srun otherwise will not use more than 1 core per task
# (see https://lsm-hpce.gitpages.psi.ch/merlin6/known-problems.html#sbatch-using-one-core-despite-setting--ccpus-per-task)

srun --cpus-per-task=44 ./<your_executable> <args>

Gwendolen GPU

For example, to run a job on 4 GPUs (max on Gwendolen is 8 GPUs, which are all on a single node):

#!/bin/bash
#SBATCH --time=00:05:00         # Define max time job will run (e.g. here 5 mins)
#SBATCH --nodes=1               # No. of nodes (there is only 1 node on Gwendolen)
#SBATCH --ntasks=4              # No. of tasks (max. 8)
#SBATCH --clusters=gmerlin6     # Specify that we are running on the GPU cluster
#SBATCH --partition=gwendolen   # Running on the Gwendolen partition of the GPU cluster
#SBATCH --account=gwendolen
##SBATCH --exclusive            # The allocations will be exclusive if turned on (remove extra hashtag to turn on)
#SBATCH --gpus=4                # No. of GPUs (max. 8)

#SBATCH --output=<output_file_name>.out  # Name of output file
#SBATCH --error=<error_file_name>.err    # Name of error file

srun ./<your_executable> <args> --kokkos-map-device-id-by=mpi_rank