core — peclet-halo (+ the peclet-core compatibility shell)

Shared infrastructure for the peclet suite (the suite-wide architecture, conventions, style, interfaces and roadmap live in the umbrella repository).

It provides the pieces every method code (flow, dem, voro, …) should share: a common MPI block domain decomposition, an efficient asynchronous ghost-layer exchange (CPU + portable Kokkos GPU), particle migration, dynamic load balancing, unified SDF geometry (peclet::core::geom), the face-CSR solver layer (peclet::core::solver), the coarse-level multigrid stages that move a level onto fewer ranks, and nanobind Python bindings. Header-only C++20 (the device side, compiled through Kokkos, is also C++20; only the morton dependency pins C++17 — see STYLE.md). The AMR octree and the Navier–Stokes solver on it are the separate peclet-amr package (peclet::amr, peclet.amr) since 2026-09-10 — relocated out of this tree with its history.

Install

The Python surface is the peclet.halo module of the peclet-halo distribution. It links MPI, so it ships as an sdist and builds against the MPI on your machine (an MPI C++ compiler wrapper and a C++20 compiler; Linux):

pip install peclet-halo          # or: pip install "peclet[mpi]", which also brings peclet-geom

peclet.halo never initialises MPI itself: import mpi4py.MPI (built against the same MPI) first. The C++ library is header-only — use it from a CMake project with find_package(peclet-core CONFIG) after cmake --install, or as a sibling checkout in the suite.

What works today

• peclet::core::decomp::BlockDecomposer<Dim> — orthogonal recursive bisection of a global cell grid into rank-owned blocks; ownerOf() tree-walk; x-fastest global/local linear indexing. The weighted ORB (init(numBlocks, globalSize, weights)) balances a per-cell work field; the multigrid-safe forms keep every block a multiple of an alignment so the grid coarsens cleanly — init(…, align), the coarse-first refined(ratio), and the aligned weighted ORB init(…, weights, align), with chooseAlignedWeighted picking the deepest alignment within an imbalance budget (weightImbalance).

• Coarse-level multigrid stages (decomp/stage_target.hpp, stage_comm.hpp, redistribute_topology.hpp, gather_by_global_id.hpp) — where a multigrid level goes when it can no longer coarsen on its own decomposition, and how its fields get there and back. chooseStageTarget is the replicated policy (in place, a sibling merge of ORB subtrees, a repartition onto fewer ranks, or full replication), makeStageComm builds the stage's communicators, RedistributeTopology plans the movement once and replays it every V-cycle (gather/scatter, allgather or planned point-to-point), and gatherByGlobalId is the id-keyed replicated gather. Tested bitwise against flow's Telescope and peclet-amr's replicated tail stage, and against redistributeGridFields, at np 1–8. redistributeGridFields (grid_redistribute.hpp) is the one-shot move of grid fields between two decompositions.

• peclet::core::decomp::BlockIndexer<Dim> — local↔global indexing for a block with a ghost layer.

• peclet::core::halo::NbxEngine — nonblocking-consensus sparse exchange (Issend + Ibarrier), for dynamic patterns.

• peclet::core::halo::GridHaloTopology<Dim> (grid_halo_topology.hpp) — asynchronous ghost-layer exchange with topology separated from exchange and a field-agnostic pack/unpack interface. buildTopology() runs once; two interchangeable exchange engines give identical results:

  • exchangeNbx() / start()+wait() — NBX, supports compute/comm overlap.
  • exchangePersistent() — MPI_Neighbor_alltoallv on a cached distributed-graph communicator; fastest for the static neighbour pattern of a fixed grid.

• peclet::core::halo::GridFieldView<T> — wraps a contiguous local array as an exchangeable field.

• peclet::core::halo::GridHalo<T> (grid_halo.hpp) — portable GPU-resident ghost-layer exchange (Kokkos: CUDA / HIP / OpenMP). Built once from a host GridHaloTopology<Dim>::flatten(); pack / unpack / periodic self-copy run as Kokkos::parallel_for over the device peclet::core::View<T> field, so the full field never crosses the bus — only the compact halo buffers are host-staged for MPI by default, with an opt-in GPU-aware path (env PECLET_CORE_GPU_AWARE_MPI). Bit-for-bit identical to the CPU exchange.

• peclet::core::halo::ParticleMigrator<Dim> — Lagrangian particle migration to owning ranks (NBX), the dynamic counterpart to the Eulerian grid halo.

• peclet::core::halo::ParticleHaloTopology<Dim> (particle_halo_topology.hpp) — persistent Lagrangian ghost halo: forward (owner→ghost), reverse (ghost→owner, accumulate) and forwardPositions (periodic image shift). peclet::core::halo::ParticleHalo<Dim> (particle_halo.hpp) is its GPU-resident Kokkos driver (on-device gather/scatter, host-staged or GPU-aware MPI), consumed by dem's distributed step.

• peclet::core::halo::rebalanceByParticleCount(...) (particle_rebalance.hpp) — dynamic load balancing for the Lagrangian path: re-inits the decomposition in place with the weighted ORB (BlockDecomposer::init(numBlocks, globalSize, weights)) and migrates. The Eulerian/AMR counterpart is peclet::amr::DistributedOctree::rebalance (peclet-amr).

• peclet::core::geom (sdf.hpp, grid_sdf.hpp, vti_io.hpp) — shared SDF solids: analytic primitives + trilinear GridSdf behind one Sdf concept, with VTI (.vti) read/write.

• peclet::core::vof (include/peclet/core/vof/) — layer L1 of the VoF stack: container-free KOKKOS_INLINE_FUNCTION kernels (PLIC plane↔volume and normals, the height-function curvature cascade, the cut-cell colour-transport rules, wetting). No Kokkos::View and no grid indexing in any signature, so one copy serves every VoF container; the drivers live in flow.

• peclet::core::solver (include/peclet/core/solver/) — mesh-agnostic linear algebra: the smoothed-aggregation graph AMG (graph_amg.hpp, host setup; graph_amg_device.hpp, device apply) and the assembled face-CSR operator layer lifted out of the AMR tree on 2026-09-10 — face_csr.hpp (host+device row kernels), coloring.hpp (greedy symmetrised graph colouring), csr_operator.hpp (the device operator, Jacobi and multicolour Gauss–Seidel sweeps), csr_bicgstab.hpp (preconditioned BiCGStab / defect correction) and vector_ops.hpp. Consumed by voro's mesh optimiser and by peclet-amr.

• Python bindings (python/halo_bindings.cpp) — a host-only nanobind module (no Kokkos): peclet.halo exposes the Lagrangian halo (ParticleMigrator, ParticleHalo: migration / ghosts / count-weighted rebalance) to an mpi4py driver. The shared zero-copy Kokkos View↔ndarray bridge that flow, pnm, dem, coupling and peclet-amr bind through lives here too (include/peclet/core/python/ndarray_interop.hpp). Type stubs ship beside it (python/packaging/_halo.pyi, generated with python -m nanobind.stubgen). python/state_hash.py is the structural byte gate: fixed-seed runs of every entry path, SHA-256 of the final state.

  The scene-authoring bindings left this repository in peclet 1.2.0 for peclet-geom (peclet.geom, wheels). They are host-only with no MPI in them, and while they shared this distribution they inherited its find_package(MPI REQUIRED) — so a pure-geometry API could not be installed without an MPI toolchain. The C++ headers include/peclet/core/geom/ stay here; peclet-geom vendors them at PECLET_CORE_TAG. The old spellings keep working through the peclet-core compatibility shell until 2.0.0 — peclet.core.geom is now peclet.geom, peclet.core.mpi is now peclet.halo, and each pair is the same object. See docs/CORE_BOUNDARY.md.

Validated end-to-end by distributed explicit heat-diffusion solvers (plain, and around an SDF solid obstacle) matching a serial reference cell-for-cell across ranks, and consumed by the validated flow and dem distributed solvers. 72 ctests in the plain host+MPI build, 87 with Kokkos (np 1–8), plus 6 Python ctests in the python/ build.

Build / test / benchmark

cmake -S . -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j
ctest --test-dir build --output-on-failure -LE bench  # 72 ctests: serial + MPI (np=1,2,4,8); 87 with -DPECLET_CORE_ENABLE_KOKKOS=ON
ctest --test-dir build -L bench                       # the halo benchmark (label `bench`)

# halo microbenchmark: weak scaling, NBX vs persistent
mpirun -np 4 ./build/benchmarks/bench_halo 48 1 300  # cells/rank/axis, ghost, iters

ctest labels: mpi (every mpirun test), np8 (the 8-rank instances — a local gate, excluded in CI whose runners have 4 cores), bench (benchmarks/studies, excluded by default). A test that cannot run in a configuration (no morton sibling: morton_indexer) exits 77 and ctest reports it skipped, never passed. The Python modules and their tests (test_mpi.py np=1,2,4,8, state_hash.py, the ndarray-interop pytest) are a second CMake project: cmake -S python -B build_py -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH=<kokkos prefix> && cmake --build build_py -j && ctest --test-dir build_py. Kokkos is needed only for the ndarray-interop test; the build type matters because the state_hash byte gate compares against a reference recorded with a Release build and reports skipped for any other toolchain string.

Requires MPI (OpenMPI/MPICH) and a C++20 compiler (-DPECLET_CORE_ENABLE_MPI=OFF builds the single-rank no-MPI stub). morton is picked up automatically if checked out as a sibling directory (enables PECLET_CORE_HAVE_MORTON; -DPECLET_CORE_MORTON_DIR=<dir> points elsewhere). CI builds all three configurations (host+MPI gcc/clang Debug/Release, Kokkos-OpenMP + Python, no-MPI) against the pinned morton tag. The CMake project is peclet_core (its version is read from pyproject.toml); it exports the header-only targets peclet::core and peclet::halo (cmake --install + find_package(peclet-core CONFIG)).

Documentation

The AMR reference notes and campaign records moved to peclet-amr with the code. What stays here is docs/archive/ (the GPU-aware-MPI recipe) and the Doxygen API pages (docs/Doxyfile, README + include/), published to GitHub Pages by .github/workflows/docs.yml.

Status

Complete and in production. The block decomposition, the async ghost-layer exchange (CPU + portable Kokkos GPU, host-staged and opt-in GPU-aware), particle migration, dynamic load balancing (weighted ORB + Lagrangian rebalancing), SDF geometry, the face-CSR solver layer, and the nanobind Python bindings are all shipped and tested (counts above; np 1–8). The aligned weighted ORB and the coarse-level multigrid stages are the newest pieces: tested here, bitwise against the stages they generalise, and not yet adopted on the consumers' main branches. flow (distributed cut-cell IBM Navier–Stokes), dem (distributed XPBD and Hertz–Mindlin with load rebalancing), voro and peclet-amr are its consumers. CUDA is retired; Kokkos (CUDA / HIP / OpenMP) is the canonical device path. Remaining work is at-scale multi-GPU tuning.