Compadre

The Compadre (Compatible Particle Discretization and Remap) Toolkit provides a performance portable solution for the parallel evaluation of computationally dense kernels. The toolkit specifically targets the Generalized Moving Least Squares (GMLS) approach, which requires the inversion of small dense matrices. The result is a set of weights that provide the information needed for remap or entries that constitute the rows of some globally sparse matrix.

This toolkit focuses on the 'on-node' aspects of meshless PDE solution and remap, namely the parallel construction of small dense matrices and their inversion. What it does not provide is the tools for managing fields, inverting globally sparse matrices, or neighbor search that requires orchestration over many MPI processes. This toolkit is designed to be easily dropped-in to an existing MPI (or serial) based framework for PDE solution or remap, with minimal dependencies ([Kokkos](https://github.com/kokkos/kokkos) and [KokkosKernels](https://github.com/kokkos/kokkos-kernels/wiki), either built in-tree, location specified by the user, or existing in [Trilinos](https://github.com/trilinos/Trilinos)).

Find up-to-date details [HERE](https://github.com/SNLComputation/compadre/wiki)

This release corresponds to version 1.0.34 on pypi.

The Compadre (Compatible Particle Discretization and Remap) Toolkit provides a performance portable solution for the parallel evaluation of computationally dense kernels. The toolkit specifically targets the Generalized Moving Least Squares (GMLS) approach, which requires the inversion of small dense matrices. The result is a set of weights that provide the information needed for remap or entries that constitute the rows of some globally sparse matrix.

This toolkit focuses on the 'on-node' aspects of meshless PDE solution and remap, namely the parallel construction of small dense matrices and their inversion. What it does not provide is the tools for managing fields, inverting globally sparse matrices, or neighbor search that requires orchestration over many MPI processes. This toolkit is designed to be easily dropped-in to an existing MPI (or serial) based framework for PDE solution or remap, with minimal dependencies ([Kokkos](https://github.com/kokkos/kokkos) and either [Cuda Toolkit](https://developer.nvidia.com/cuda-toolkit) or [LAPACK](http://www.netlib.org/lapack/)).

Find up-to-date details [HERE](https://github.com/SNLComputation/compadre/blob/master/README.md)

The Compadre (Compatible Particle Discretization and Remap) Toolkit provides a performance portable solution for the parallel evaluation of computationally dense kernels. The toolkit specifically targets the Generalized Moving Least Squares (GMLS) approach, which requires the inversion of small dense matrices. The result is a set of weights that provide the information needed for remap or entries that constitute the rows of some globally sparse matrix.

This toolkit focuses on the 'on-node' aspects of meshless PDE solution and remap, namely the parallel construction of small dense matrices and their inversion. What it does not provide is the tools for managing fields, inverting globally sparse matrices, or neighbor search that requires orchestration over many MPI processes. This toolkit is designed to be easily dropped-in to an existing MPI (or serial) based framework for PDE solution or remap, with minimal dependencies ([Kokkos](https://github.com/kokkos/kokkos) and either [Cuda Toolkit](https://developer.nvidia.com/cuda-toolkit) or [LAPACK](http://www.netlib.org/lapack/)).

Find up-to-date details [HERE](https://github.com/SNLComputation/compadre/blob/master/README.md)