KaMinPar

KaMinPar is a shared-memory parallel tool to heuristically solve the graph partitioning problem: divide a graph into k disjoint blocks of roughly equal weight while
minimizing the number of edges between blocks.
Competing algorithms are mostly evaluated for small values of k. If k is large, they often compute highly imbalance solutions, solutions of low quality or suffer excessive running time.
KaMinPar substantially mitigates these problems.
It computes partitions of comparable quality to other high-quality graph partitioning tools while guaranteeing the balance constraint for unweighted input graphs.
Moreover, for large values of k, it is an order of magnitude faster than competing algorithms.

Installation Notes

Requirements

• Compiler: GCC or Clang with C++20 support
• CPU: x86 or ARM
• Operating System: Linux or macOS
• Tools: CMake
• Libraries: Intel TBB, MPI (optional, for the distributed partitioner)

Building KaMinPar

Build KaMinPar following the standard CMake steps:

cmake -B build -DCMAKE_BUILD_TYPE=Release --preset=<default|memory|distributed>
cmake --build build --parallel

Using the Binaries

To partition a graph in METIS format using (d)KaMinPar, run

# KaMinPar: shared-memory partitioning
./build/apps/KaMinPar [-P default|strong|memory|largek] -G <graph filename> -k <number of blocks> -t <nproc> [--epsilon=0.03] [--seed=0]

# dKaMinPar: distributed partitioning
mpirun -n <nproc> ./build/apps/dKaMinPar [-P default|strong] -G <graph filename> -k <number of blocks> [--epsilon=0.03] [--seed=0]

Use the --help flag to see a list of all command line options.
To setup algorithmic tuning parameters, (d)KaMinPar offers configuration presets that can be loaded using the -P <preset> option (view --help for a list of all presets).
Presets can be viewed by using the --dump-config flag; to use a custom preset, load a configuration file using the -C <filename> option, e.g.,

# Write the default preset to a file
./KaMinPar -P default --dump-config > my_preset.ini

# ... modify the configuration by editing my_preset.ini ...

# Use your modified preset
./KaMinPar -C my_preset.ini -G <...> -k <...> -t <...>

For a description of the graph format, please refer to the KaHiP manual.

Using the Libraries

If you are using CMake, you can use the partitioners as libraries by adding this repository as a Git submodule to your project and including it in your CMake configuration:

add_subdirectory(external/KaMinPar)

target_link_libraries(<your-target> PUBLIC KaMinPar::KaMinPar)  # Shared-memory partitioning
target_link_libraries(<your-target> PUBLIC KaMinPar::dKaMinPar) # Distributed partitioning

Alternatively, you can use FetchContent:

include(FetchContent)
FetchContent_Declare(KaMinPar
  GIT_REPOSITORY https://github.com/KaHIP/KaMinPar.git
  GIT_TAG main)
FetchContent_MakeAvailable(KaMinPar)
set_property(DIRECTORY "${KaMinPar_SOURCE_DIR}" PROPERTY EXCLUDE_FROM_ALL YES) # optional

target_link_libraries(<your-target> PUBLIC KaMinPar::KaMinPar)  # Shared-memory partitioning
target_link_libraries(<your-target> PUBLIC KaMinPar::dKaMinPar) # Distributed partitioning

Then, call the libraries as follows:

#include <kaminpar-shm/kaminpar.h>
#include <kaminpar-dist/dkaminpar.h>

using namespace kaminpar;

// Call the shared-memory partitioner:
KaMinPar shm(int num_threads, shm::create_default_context());
// KaMinPar::reseed(int seed);
shm.borrow_and_mutate_graph(NodeID n, EdgeID *xadj, NodeID *adjncy, NodeWeight *vwgt = nullptr, EdgeWeight *adjwgt = nullptr);
// alternatively: shm.copy_graph(n, xadj, adjncy, vwgt, adjwgt); will work on a copy of the graph
shm.compute_partition(BlockID number_of_blocks, BlockID *out_partition);

// Call the distributed partitioner:
dKaMinPar dist(MPI_Comm comm, int num_threads, dist::create_default_context());
// dKaMinPar::reseed(int seed); 
dist.import_graph(GlobalNodeID *vtxdist, GlobalEdgeID *xadj, GlobalNodeID *adjncy, GlobalNodeWeight *vwvgt = nullptr, GlobalEdgeWeight *adjwgt = nullptr);
dist.compute_partition(BlockID number_of_blocks, BlockID *out_partition);

Please take a look at apps/KaMinPar.cc and apps/dKaMinPar.cc for a full example on how to call the libraries.

Licensing

KaMinPar is free software provided under the MIT License.

If you publish results using our shared-memory partitioner, please acknowledge our work by citing the following paper (preprint, resources):

@InProceedings{DeepMultilevelGraphPartitioning,
  author    = {Lars Gottesb{\"{u}}ren and
               Tobias Heuer and
               Peter Sanders and
               Christian Schulz and
               Daniel Seemaier},
  title     = {Deep Multilevel Graph Partitioning},
  booktitle = {29th Annual European Symposium on Algorithms, {ESA} 2021},
  series    = {LIPIcs},
  volume    = {204},
  pages     = {48:1--48:17},
  publisher = {Schloss Dagstuhl - Leibniz-Zentrum f{\"{u}}r Informatik},
  year      = {2021},
  url       = {https://doi.org/10.4230/LIPIcs.ESA.2021.48},
  doi       = {10.4230/LIPIcs.ESA.2021.48}
}

If you publish results using out distributed-memory partitioner, please cite the following paper (preprint, resources):

@InProceedings{DistributedDeepMultilevelGraphPartitioning,
  author    = {Sanders, Peter and Seemaier, Daniel},
  title     = {Distributed Deep Multilevel Graph Partitioning},
  booktitle = {Euro-Par 2023: Parallel Processing},
  year      = {2023},
  publisher = {Springer Nature Switzerland},
  pages     = {443--457},
  isbn      = {978-3-031-39698-4}
}