Python API

The suite is driven from Python — everything lives under the single peclet namespace, installable from PyPI (pip install peclet for the CPU family, or an individual pip install peclet-<name>).

Package	Import	What you get
peclet.flow	import peclet.flow	Solver / SolverColocated — the Eulerian Navier–Stokes solver; peclet.flow.pnm pore extraction
peclet.dem	import peclet.dem	Simulation — Lagrangian DEM/XPBD packing
peclet.voro	import peclet.voro	Tessellation, Simulation — moving-cell Voronoi + dynamics
peclet.core	from peclet.core import mpi, amr	particle halo (MPI) + Kokkos AMR octree
peclet.morton	import peclet.morton	vectorised Morton/Z-order arithmetic

Every Kokkos-backed module exposes execution_space (OpenMP / Cuda / HIP / Serial) so you can confirm which build you imported. The pages here are generated from the modules' own docstrings; the full C++ API is on each code's Doxygen site (linked from the home page).

GPU & multi-rank

The wheels on PyPI are the multicore-CPU (OpenMP) build. For GPU (CUDA/HIP) or multi-rank MPI, build the package from source against a Kokkos prefix, or use a container — see Install & run.

Distributed (MPI) API

Built with the MPI flags on (PECLET_FLOW_MPI / PECLET_DEM_MPI / PECLET_VORO_MPI — all on in the containers), the compute modules gain a multi-rank surface driven from mpi4py. These methods aren't in the auto-generated tables above (that snapshot is the single-rank CPU wheel):

Module	Distributed entry points
peclet.flow	Solver.init_mpi(gnx,gny,gnz), peclet.flow.mpi_block(gnx,gny,gnz) → (origin, size), real Solver.rank()/size(), peclet.flow.has_mpi
peclet.dem	Simulation.init_mpi(...), enable_mpi_step(...), step_mpi(nsteps), rebalance(), rank(), num_ghost()
peclet.voro	VoronoiHalo(origin, size, gsize, periodic) with owned_mask, gather(...) → (pos, gid, weight, n_owned), refresh_positions, rank()/size()
peclet.core	peclet.core.mpi.Migrator / Halo (the shared particle halo the above build on)

A distributed driver import mpi4py (which calls MPI_Init), then decomposes and steps. See the worked example benchmarks/profile_mpi_flow.py and the launch recipes in Containers → Distributed MPI.