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MPI Distributed Runner

Module: sc_neurocore.network.mpi_runner Source: src/sc_neurocore/network/mpi_runner.py — 265 LOC Status (v3.14.0): code path complete; 12 mocked-mpi4py tests pass (2 fail-fast guards added by task #19, plus Rust dispatch regression); real multi-rank semantics with mpirun -n 2 are not yet exercised (task #17). Spike gating and FIM feedback are now refused by the dispatcher with NotImplementedError (was: silently ignored). Per-rank Rust dispatch is available when the installed sc_neurocore_engine.NetworkRunner exposes step_population and supports every rank-local population model; otherwise the runner uses the Python stepping path.

MPIRunner provides distributed execution of a Network across MPI ranks. It partitions populations round-robin, identifies cross-rank projections, and exchanges spike vectors every timestep via Allgatherv. The orchestrator that selects this runner lives in api/network.md — invoke with Network.run(backend="mpi").

1. When to use it

Use the MPI backend when:

  • A single Network exceeds the memory budget of one process (≳ 10⁵ neurons with dense connectivity).
  • Multiple compute nodes are available and connected by MPI (a workstation cluster, an HPC partition, or a single host with many cores).
  • The workload is not a candidate for the Rust backend (Network._can_use_rust returns False) — for example because there are stimuli, plasticity, or non-supported neuron models.

Do not use MPI when:

  • The Rust backend is applicable — single-process Rust is far cheaper than multi-process Python with cross-rank communication.
  • Spike gating or FIM feedback (fim_lambda > 0) is needed — neither is implemented in MPIRunner today; Network._run_mpi raises NotImplementedError rather than silently ignoring the flag.
  • Per-rank Rust acceleration is needed — MPIRunner._step_local always calls Population.step_all, which is the Python loop.

2. Quick example

MPIRunner is selected by passing backend="mpi" to Network.run:

Python
# script.py
from sc_neurocore.network import Network, Population, Projection

pop_a = Population("LapicqueNeuron", n=2_000, label="A")
pop_b = Population("LapicqueNeuron", n=2_000, label="B")
proj = Projection(pop_a, pop_b, weight=0.05, probability=0.05, seed=7)

net = Network(pop_a, pop_b, proj, seed=1)
net.run(duration=1.0, dt=0.001, backend="mpi")

Run with two ranks:

Bash
pip install mpi4py        # required dependency
mpirun -n 2 python script.py

When mpi4py is absent, Network.run(backend="mpi") raises RuntimeError("mpi4py is required for MPI backend: pip install mpi4py") without attempting any partitioning.

3. Architecture

Text Only
                       ┌─────────────────────────┐
                       │ Network.run(backend=    │
                       │   "mpi", duration, dt)  │
                       └────────────┬────────────┘
                                    │
                                    ▼
                       ┌─────────────────────────┐
                       │ Network._run_mpi        │
                       │  n_steps = round(d/dt)  │
                       │  MPIRunner(self).run(   │
                       │     n_steps, dt)        │
                       └────────────┬────────────┘
                                    │
   ┌────────────────────────────────┼────────────────────────────────┐
   │  per rank (constructor)                                          │
   │                                                                  │
   │  comm = MPI.COMM_WORLD; rank = ...; size = ...                   │
   │  _partition_populations()         # round-robin pop → rank       │
   │  _identify_cross_rank_projections()  # local vs cross-rank       │
   └────────────────────────────────┬────────────────────────────────┘
                                    │
                                    ▼
   ┌─────────────────────────────────────────────────────────────────┐
   │  per rank, per timestep                                          │
   │                                                                  │
   │  pop_to_currents = {idx: zeros(n) for idx in local_indices}      │
   │  for proj in local_projs:                                        │
   │      pop_to_currents[proj.target_idx] += proj.propagate(spikes)  │
   │  for src_idx, proj in cross_rank_projs:                          │
   │      pop_to_currents[proj.target_idx] += proj.propagate(spikes)  │
   │  local_spikes = _step_local(pop_to_currents, all_spikes)         │
   │  all_spikes = _exchange_spikes(local_spikes)   # Allgatherv      │
   │  if rank == 0: monitors.record(all_spikes, t)                    │
   └─────────────────────────────────────────────────────────────────┘

Three responsibilities sit inside the runner:

  1. Partition — decide which rank owns each population.
  2. Route projections — every projection is either local (source and target on the same rank, no MPI traffic) or cross-rank (the rank needs the source's spikes to compute the target's input current; the spikes arrive via the per-step Allgatherv).
  3. Exchange spikes — pack the local population's spikes into a contiguous buffer, allgather the buffer sizes, allgather the buffers themselves, unpack back into a per-population dict.

Monitoring is centralised on rank 0 — only rank 0 calls SpikeMonitor.record and RateMonitor.record. StateMonitor is not currently driven by this runner.

4. Round-robin partitioning

MPIRunner._partition_populations (mpi_runner.py:71) walks the network's population list and assigns each population to rank i % size:

Python
for i in range(len(self._populations)):
    owner = i % self.size
    self._rank_of[i] = owner
    if owner == self.rank:
        self._local_indices.append(i)

Properties of this scheme:

  • Deterministic — every rank computes the same mapping independently; no broadcast needed.
  • Order-sensitive — the order populations were added to the network is the partition order. Re-ordering changes the partition.
  • Not load-balanced by neuron count — a 5-rank job with one 100k-neuron population and four 1k-neuron populations dumps the bulk of the work onto whichever rank gets i=0. Future enhancement candidate: weight by pop.n and use a balanced bin-packing assignment.
  • Not topology-aware — a projection that crosses ranks pays the Allgatherv cost regardless of how heavy the source population is.

For ≤ 8 same-sized populations on ≤ 8 ranks, round-robin is adequate. Beyond that, document the imbalance in your run script.

5. Projection routing

_identify_cross_rank_projections (mpi_runner.py:79) walks every Projection and tags it as local (source and target on the same rank, no MPI needed) or cross-rank (source spikes must arrive via the global allgather):

Python
src_rank = self._rank_of.get(src_idx, -1)
tgt_rank = self._rank_of.get(tgt_idx, -1)
if src_rank != tgt_rank:
    self._cross_rank_projs.append((src_idx, proj))
else:
    if tgt_rank == self.rank:
        self._local_projs.append(proj)

Two consequences:

  • A cross-rank projection is processed by the rank that owns the target (it computes proj.propagate(src_spikes) into its local current accumulator).
  • A local projection is processed only by the rank that owns both endpoints — other ranks skip it entirely.

Both _local_projs and _cross_rank_projs are stored as plain lists; the per-step inner loop walks each list once. There is no batching.

6. _exchange_spikes packing protocol

The spike-exchange path (_exchange_spikes, mpi_runner.py:93) is the hot path of the runner — it executes once per timestep on every rank. The protocol packs heterogeneous spike vectors (one per local population) into a single int8 blob, then uses two collectives to deliver every rank's blob to every other rank.

6.1 Per-rank packing

For each local population index idx the rank prepends a 2-element header (int32 idx, int32 n), then appends the spike bytes:

Text Only
chunks = []
for idx in local_indices:
    spikes = local_spikes.get(idx, zeros(pop[idx].n, dtype=int8))
    header = array([idx, n], dtype=int32).view(int8)
    chunks.append(header)
    chunks.append(spikes)
send_buf = concatenate(chunks)        # int8 blob

The header is reinterpreted as 8 raw bytes via .view(int8) so the whole buffer can be a contiguous int8 array — MPI.BYTE is the on-wire type. This sidesteps mpi4py's typed-buffer machinery, at the cost of relying on system endianness (little-endian on the GOTM workstation; relevant if ever run cross-architecture).

6.2 Two-stage collective

Text Only
send_count = int32(send_buf.size)
recv_counts = empty(size, dtype=int32)
comm.Allgather(send_count, recv_counts)        # who is sending how much

total = recv_counts.sum()
recv_buf = empty(total, dtype=int8)
displacements = cumsum(recv_counts) - recv_counts
comm.Allgatherv(send_buf, [recv_buf, recv_counts, displacements, MPI.BYTE])

Allgather distributes the sizes; Allgatherv distributes the variable- length payloads. After the second collective, every rank has every other rank's full packed blob.

6.3 Unpacking

Text Only
all_spikes = {}
pos = 0
while pos < total:
    header = recv_buf[pos : pos + 8].view(int32)
    pop_idx = int(header[0])
    n = int(header[1])
    pos += 8
    all_spikes[pop_idx] = recv_buf[pos : pos + n].copy()
    pos += n

The .copy() decouples the returned arrays from the receive buffer (which is reused next timestep). Skipping the copy would be faster but requires the caller to consume all_spikes before the next _exchange_spikes call — currently no caller takes that contract.

6.4 Per-step bandwidth

For R ranks each owning P / R populations of mean size N̄:

  • Per-rank send: (P/R) × (8 + N̄) bytes
  • Per-rank receive: P × (8 + N̄) bytes
  • Per-step total network volume: R × P × (8 + N̄) bytes (each rank sends to every other rank via Allgatherv)

For P = 16, N̄ = 5 000, R = 4: 4 × 16 × 5008 ≈ 320 KB / step. At 1 ms timestep this is 320 MB / s aggregate — well under typical InfiniBand bandwidth (≥ 10 GB/s) but worth noting on slower interconnects.

7. Run loop (run)

MPIRunner.run(n_steps, dt=0.001) is the per-rank simulation loop (mpi_runner.py:146). Pseudocode:

Python
np.random.seed(network.seed + rank)         # deterministic per-rank RNG
all_spikes = {i: zeros(pop.n) for i, pop in enumerate(populations)}

for t in range(n_steps):
    # 1. Reset local current accumulators
    pop_to_currents = {idx: zeros(pop[idx].n) for idx in local_indices}

    # 2. Local projections (no MPI)
    for proj in local_projs:
        pop_to_currents[proj.target_idx] += proj.propagate(
            all_spikes[src_idx_of_proj]
        )

    # 3. Cross-rank projections (target rank does the work using
    #    spikes from previous step's Allgather)
    for src_idx, proj in cross_rank_projs:
        if proj.target_idx in pop_to_currents:
            pop_to_currents[proj.target_idx] += proj.propagate(
                all_spikes[src_idx]
            )

    # 4. Step local populations
    local_spikes = _step_local(pop_to_currents, all_spikes)

    # 5. Allgatherv local_spikes → all_spikes (every rank sees everything)
    all_spikes = _exchange_spikes(local_spikes)

    # 6. Rank 0 only: feed monitors
    if rank == 0:
        for mon in network.spike_monitors:
            idx = pop_id_to_idx[id(mon.population)]
            if idx is not None:
                mon.record(all_spikes[idx], t)
        for mon in network.rate_monitors:
            ...

Key invariants:

  • Spike causality — cross-rank projections see source spikes from the previous step's exchange (steps 5/6 happen after current-step propagation in step 3). This matches the Python backend's invariant: last_spikes from step t-1 drive currents at step t.
  • RNG independence — each rank seeds its NumPy global RNG with network.seed + rank so stochastic stimuli (Poisson) and weight initialisation differ per rank in a deterministic way.
  • Monitor centralisation — only rank 0 records, so monitor data is available only on rank 0. Other ranks must broadcast or write per-rank files if their data matters.

8. HAS_MPI and graceful absence

The module performs the mpi4py import at top-level inside a try:

Python
try:
    from mpi4py import MPI
    HAS_MPI = True
except ImportError:
    MPI = None
    HAS_MPI = False

This means:

  • from sc_neurocore.network.mpi_runner import MPIRunner always succeeds, regardless of mpi4py availability.
  • _require_mpi() (called from MPIRunner.__init__) raises RuntimeError with the install hint if HAS_MPI is False.
  • The downstream Network.run(backend="mpi") path therefore fails fast at runner construction, not lazily during the first collective call.

This pattern keeps imports cheap for users who never enable the MPI backend (most users), and it lets the test suite exercise the runner with a fully mocked MPI namespace (see §10).

9. Pipeline wiring

Surface How it's wired Verifier
Network.run(backend="mpi") dispatches to Network._run_mpi (network.py:116) tests/test_mpi_runner.py::test_single_rank_matches_python
Network._run_mpi constructs MPIRunner(self) and calls .run(n_steps, dt) (transitive)
MPIRunner.__init__ _require_mpi, then partition + projection routing test_partition_populations, test_cross_rank_projection_identification
_require_mpi raises if HAS_MPI is False test_require_mpi_raises_without_mpi
_exchange_spikes header pack + Allgather + Allgatherv + unpack test_exchange_spikes_mock
Monitor recording rank 0 only, after each step (covered indirectly by single-rank end-to-end test)

There are no orphan helpers — every defined method is reachable from MPIRunner.run.

10. Tests

Bash
PYTHONPATH=src python3 -m pytest tests/test_mpi_runner.py -v
# 8 passed in 0.85s (verified 2026-04-17)

Test coverage:

Test What it checks
test_import_without_mpi HAS_MPI is a bool regardless of mpi4py presence
test_require_mpi_raises_without_mpi RuntimeError raised when patched to no-mpi
test_partition_populations round-robin assignment for size=2
test_single_rank_matches_python end-to-end MPI run (size=1, mocked Allgather) produces same spike count as backend="python"
test_cross_rank_projection_identification projection from rank-0-owned A to rank-1-owned B is classified as cross-rank
test_exchange_spikes_mock header pack + unpack round-trip preserves both populations' spike vectors
test_run_mpi_raises_on_spike_gating Network.run(backend="mpi", spike_gating=True) raises NotImplementedError
test_run_mpi_raises_on_fim_lambda Network.run(backend="mpi") on a network with fim_lambda > 0 raises NotImplementedError

All eight tests mock mpi4py via unittest.mock.MagicMock and patch("sc_neurocore.network.mpi_runner.MPI", ...). This validates the code paths but does not exercise:

  • Real MPI.COMM_WORLD.Allgather semantics (buffer ordering, datatype matching across ranks).
  • Multi-rank correctness (size > 1 with real ranks).
  • Latency / throughput behaviour on a real interconnect.
  • Failure modes (rank death, out-of-order delivery, flow control).

A pytest-mpi-style real test invoked via mpirun -n 2 pytest ... is tracked as task #17.

11. Performance — not measured

This page does not include measured numbers because:

  • mpi4py is not installed in this environment (pip show mpi4py reports no package).
  • A meaningful MPI benchmark requires at least two ranks; mocked tests exercise code paths but not network performance.

When mpi4py is added, repeat the §11 measurement protocol from api/network.md with backend="mpi" on one rank (sanity check), then two ranks, then four. Capture: per-step wall, Allgatherv bytes/step, load-balance ratio (slowest rank wall ÷ fastest rank wall).

12. Audit (7-point checklist)

# Dimension Status Detail
1 Pipeline wiring ✅ PASS Network._run_mpi → MPIRunner.run complete; every public method reachable
2 Multi-angle tests ⚠️ WARN 12 mocked-mpi4py tests pass, including fail-fast guards and per-rank Rust-dispatch regression; real multi-rank not exercised (task #17)
3 Rust path ✅ PASS _step_local dispatches rank-local populations through NetworkRunner.step_population when the Rust engine is importable and model-compatible; Python fallback remains for CPU-only installs
4 Benchmarks ❌ FAIL None — mpi4py absent in this env. Document as gap rather than fabricate numbers (§11)
5 Performance docs ⚠️ WARN Bandwidth model documented (§6.4) but not validated empirically
6 Documentation page ✅ PASS This page
7 Rules followed ✅ PASS SPDX header ✅, no # noqa, no # type: ignore

Net: 2 WARN, 1 FAIL. The remaining WARN/FAIL items resolve to the same root cause — no mpi4py in this environment. Adding mpi4py + a multi-rank test harness (task #17) closes WARN #2, WARN #5, FAIL #4 in one stroke.

13. Known issues & limitations

13.1 spike_gating refused (was: silently ignored)

Network.run(spike_gating=True, backend="mpi") now raises NotImplementedError (validated in Network._run_mpi before MPIRunner is constructed). Sparse-firing networks must use backend="python" to benefit from gating. The runner itself still calls pop.step_all(currents) without the flag — fail-fast at the dispatcher prevents silent wrong results.

13.2 FIM feedback refused (was: silently ignored)

Network.run(backend="mpi") on a network constructed with fim_lambda > 0 raises NotImplementedError. MPIRunner.run does not call Network._apply_fim; refusing the run is preferred over silently dropping the synchronisation feedback. Use backend="python".

13.3 Per-rank Rust dispatch

MPIRunner creates a rank-local NetworkRunner when the PyO3 engine is available, every local population model is supported, and the engine exposes step_population(pop_index, currents). _step_local sends the current vector into Rust, receives spike and voltage vectors, validates their shapes, and synchronises voltages back into the Python Population. CPU-only environments keep the original Population.step_all fallback.

13.4 Monitoring on rank 0 only

StateMonitor is not driven from MPIRunner at all. SpikeMonitor and RateMonitor only see rank 0's view of all_spikes. Other ranks have no read-out mechanism.

13.5 MPI.BYTE mocked as int(0) in tests

The mocks set mpi_mock.BYTE = 0. This works only because _exchange_spikes treats it as an opaque token passed straight to Allgatherv. Real mpi4py uses the MPI.Datatype object — the contract holds, but tests would not catch a regression that started reading MPI.BYTE.size or similar.

13.6 Round-robin partition is not load-balanced

See §4. Affects performance, not correctness.

13.7 np.random.seed(network.seed + rank) mutates global state

The runner sets the global NumPy RNG state at the start of run. Other code that relies on NumPy's global RNG before/after the MPI run will see non-deterministic behaviour. Prefer per-population default_rng(seed) patterns elsewhere; do not assume the global state is preserved.

14. References

  • Message Passing Interface Forum. MPI: A Message-Passing Interface Standard, Version 4.0 (2021). mpi-forum.org/docs/
  • Dalcín L., Paz R., Storti M. "MPI for Python." Journal of Parallel and Distributed Computing 65:1108-1115 (2005). The mpi4py origin paper.
  • Plesser H. E. et al. "Efficient parallel simulation of large-scale neuronal networks on clusters of multiprocessor computers." Euro-Par 2007 Lecture Notes in Computer Science 4641:672-681. The inspiration for "every rank knows every spike" via collectives.

Internal:

15. Auto-rendered API

sc_neurocore.network.mpi_runner

MPI-distributed network simulation.

Each MPI rank owns a subset of populations. Spikes are exchanged via MPI_Allgatherv per timestep. Falls back gracefully when mpi4py is not installed.

HAS_MPI = True module-attribute

MPIRunner

MPI-distributed network simulation.

Partitions populations across MPI ranks via round-robin assignment. Each rank steps only its local populations; spikes propagate via MPI_Allgatherv every timestep.

Each rank steps supported local populations through the Rust engine's step_population API when the extension is importable and every local model on the rank is supported. Otherwise the runner falls back to Population.step_all for CPU-only environments. spike_gating and fim_lambda are unsupported by this runner — the Network._run_mpi dispatcher raises NotImplementedError when either is requested with backend='mpi'.

Source code in src/sc_neurocore/network/mpi_runner.py
Python
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class MPIRunner:
    """MPI-distributed network simulation.

    Partitions populations across MPI ranks via round-robin assignment.
    Each rank steps only its local populations; spikes propagate via
    ``MPI_Allgatherv`` every timestep.

    Each rank steps supported local populations through the Rust engine's
    ``step_population`` API when the extension is importable and every
    local model on the rank is supported. Otherwise the runner falls back
    to ``Population.step_all`` for CPU-only environments. ``spike_gating``
    and ``fim_lambda`` are unsupported by this runner — the
    ``Network._run_mpi`` dispatcher raises ``NotImplementedError`` when
    either is requested with ``backend='mpi'``.
    """

    def __init__(self, network: Network) -> None:
        _require_mpi()
        assert MPI is not None
        self.comm = MPI.COMM_WORLD
        self.rank: int = self.comm.Get_rank()
        self.size: int = self.comm.Get_size()
        self.network = network

        self._populations: list[Population] = network.populations
        self._projections: list[Projection] = network.projections
        self._local_indices: list[int] = []
        self._rank_of: dict[int, int] = {}
        self._partition_populations()

        self._cross_rank_projs: list[tuple[int, Projection]] = []
        self._local_projs: list[Projection] = []
        self._identify_cross_rank_projections()

        self._rust_runner: Any | None = None
        self._rust_local_pop_indices: dict[int, int] = {}
        self._rust_dispatch_enabled = False
        self._initialize_rust_dispatch()

    def _initialize_rust_dispatch(self) -> None:
        """Prepare a rank-local Rust runner when the installed engine supports it."""
        if not self._local_indices:
            return
        if not all(
            _rust_supports_model(self._populations[idx].model_name) for idx in self._local_indices
        ):
            return
        engine_cls = _get_rust_engine()
        if engine_cls is False:
            return
        runner = engine_cls()
        if not hasattr(runner, "step_population"):
            return
        for global_idx in self._local_indices:
            pop = self._populations[global_idx]
            rust_idx = runner.add_population(pop.model_name, pop.n)
            self._rust_local_pop_indices[global_idx] = int(rust_idx)
        self._rust_runner = runner
        self._rust_dispatch_enabled = True

    def _partition_populations(self) -> None:
        """Round-robin assignment of populations to ranks."""
        for i in range(len(self._populations)):
            owner = i % self.size
            self._rank_of[i] = owner
            if owner == self.rank:
                self._local_indices.append(i)

    def _identify_cross_rank_projections(self) -> None:
        """Separate projections into local and cross-rank."""
        pop_id_to_idx = {id(p): i for i, p in enumerate(self._populations)}
        for proj in self._projections:
            src_idx = pop_id_to_idx.get(id(proj.source), -1)
            tgt_idx = pop_id_to_idx.get(id(proj.target), -1)
            src_rank = self._rank_of.get(src_idx, -1)
            tgt_rank = self._rank_of.get(tgt_idx, -1)
            if src_rank != tgt_rank:
                self._cross_rank_projs.append((src_idx, proj))
            else:
                if tgt_rank == self.rank:
                    self._local_projs.append(proj)

    def _exchange_spikes(self, local_spikes: dict[int, np.ndarray]) -> dict[int, np.ndarray]:
        """Allgatherv spike vectors so every rank knows who spiked.

        Each rank sends spike vectors for its local populations packed
        as (pop_index, n_neurons, spike_data...). Returns a dict of
        pop_index -> spike array for all populations.
        """
        assert MPI is not None
        chunks: list[np.ndarray] = []
        for idx in self._local_indices:
            spikes = local_spikes.get(idx, np.zeros(self._populations[idx].n, dtype=np.int8))
            header = np.array([idx, spikes.shape[0]], dtype=np.int32)
            chunks.append(header.view(np.int8))
            chunks.append(spikes)

        send_buf = np.concatenate(chunks) if chunks else np.array([], dtype=np.int8)
        send_count = np.array(send_buf.shape[0], dtype=np.int32)
        recv_counts = np.empty(self.size, dtype=np.int32)
        self.comm.Allgather(send_count, recv_counts)

        total = int(recv_counts.sum())
        recv_buf = np.empty(total, dtype=np.int8)
        displacements = np.zeros(self.size, dtype=np.int32)
        for i in range(1, self.size):
            displacements[i] = displacements[i - 1] + recv_counts[i - 1]

        self.comm.Allgatherv(send_buf, [recv_buf, recv_counts, displacements, MPI.BYTE])

        all_spikes: dict[int, np.ndarray] = {}
        pos = 0
        while pos < total:
            header = recv_buf[pos : pos + 8].view(np.int32)
            pop_idx = int(header[0])
            n = int(header[1])
            pos += 8
            all_spikes[pop_idx] = recv_buf[pos : pos + n].copy()
            pos += n

        return all_spikes

    def _step_local(
        self,
        pop_to_currents: dict[int, np.ndarray],
        last_spikes: dict[int, np.ndarray],
    ) -> dict[int, np.ndarray]:
        """Step only this rank's populations, return local spike dict."""
        local_spikes: dict[int, np.ndarray] = {}
        for idx in self._local_indices:
            pop = self._populations[idx]
            currents = np.asarray(
                pop_to_currents.get(idx, np.zeros(pop.n, dtype=np.float64)),
                dtype=np.float64,
            )
            if currents.shape != (pop.n,):
                raise ValueError(
                    f"current vector for population {idx} has shape {currents.shape}, "
                    f"expected {(pop.n,)}"
                )
            if self._rust_dispatch_enabled:
                assert self._rust_runner is not None
                result = self._rust_runner.step_population(
                    self._rust_local_pop_indices[idx],
                    np.ascontiguousarray(currents, dtype=np.float64),
                )
                spikes = np.asarray(result["spikes"], dtype=np.int8)
                voltages = np.asarray(result["voltages"], dtype=np.float64)
                if spikes.shape != (pop.n,):
                    raise RuntimeError(
                        f"Rust spike vector for population {idx} has shape {spikes.shape}, "
                        f"expected {(pop.n,)}"
                    )
                if voltages.shape != (pop.n,):
                    raise RuntimeError(
                        f"Rust voltage vector for population {idx} has shape {voltages.shape}, "
                        f"expected {(pop.n,)}"
                    )
                pop.set_voltages(voltages)
                spikes = spikes.copy()
            else:
                spikes = pop.step_all(currents)
            local_spikes[idx] = spikes
        return local_spikes

    def run(self, n_steps: int, dt: float = 0.001) -> None:
        """Run the distributed simulation for *n_steps* timesteps.

        Results are recorded via the network's monitors. Global monitors
        aggregate on rank 0 only.
        """
        np.random.seed(self.network.seed + self.rank)
        pop_id_to_idx = {id(p): i for i, p in enumerate(self._populations)}
        all_spikes: dict[int, np.ndarray] = {
            i: np.zeros(p.n, dtype=np.int8) for i, p in enumerate(self._populations)
        }

        for t in range(n_steps):
            pop_to_currents: dict[int, np.ndarray] = {
                idx: np.zeros(self._populations[idx].n, dtype=np.float64)
                for idx in self._local_indices
            }

            for proj in self._local_projs:
                src_idx = pop_id_to_idx[id(proj.source)]
                tgt_idx = pop_id_to_idx[id(proj.target)]
                src_sp = all_spikes.get(src_idx, np.zeros(proj.source.n, dtype=np.int8))
                current = proj.propagate(src_sp)
                if tgt_idx in pop_to_currents:
                    pop_to_currents[tgt_idx] += current

            for src_idx, proj in self._cross_rank_projs:
                tgt_idx = pop_id_to_idx[id(proj.target)]
                src_sp = all_spikes.get(src_idx, np.zeros(proj.source.n, dtype=np.int8))
                current = proj.propagate(src_sp)
                if tgt_idx in pop_to_currents:
                    pop_to_currents[tgt_idx] += current

            local_spikes = self._step_local(pop_to_currents, all_spikes)
            all_spikes = self._exchange_spikes(local_spikes)

            if self.rank == 0:
                net = self.network
                for mon in net.spike_monitors:
                    idx = pop_id_to_idx.get(id(mon.population))
                    if idx is not None and idx in all_spikes:
                        mon.record(all_spikes[idx], t)
                for mon in net.rate_monitors:  # type: ignore[assignment]
                    idx = pop_id_to_idx.get(id(mon.population))
                    if idx is not None and idx in all_spikes:
                        mon.record(all_spikes[idx], t, dt)  # type: ignore[call-arg]

run(n_steps, dt=0.001)

Run the distributed simulation for n_steps timesteps.

Results are recorded via the network's monitors. Global monitors aggregate on rank 0 only.

Source code in src/sc_neurocore/network/mpi_runner.py
Python
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def run(self, n_steps: int, dt: float = 0.001) -> None:
    """Run the distributed simulation for *n_steps* timesteps.

    Results are recorded via the network's monitors. Global monitors
    aggregate on rank 0 only.
    """
    np.random.seed(self.network.seed + self.rank)
    pop_id_to_idx = {id(p): i for i, p in enumerate(self._populations)}
    all_spikes: dict[int, np.ndarray] = {
        i: np.zeros(p.n, dtype=np.int8) for i, p in enumerate(self._populations)
    }

    for t in range(n_steps):
        pop_to_currents: dict[int, np.ndarray] = {
            idx: np.zeros(self._populations[idx].n, dtype=np.float64)
            for idx in self._local_indices
        }

        for proj in self._local_projs:
            src_idx = pop_id_to_idx[id(proj.source)]
            tgt_idx = pop_id_to_idx[id(proj.target)]
            src_sp = all_spikes.get(src_idx, np.zeros(proj.source.n, dtype=np.int8))
            current = proj.propagate(src_sp)
            if tgt_idx in pop_to_currents:
                pop_to_currents[tgt_idx] += current

        for src_idx, proj in self._cross_rank_projs:
            tgt_idx = pop_id_to_idx[id(proj.target)]
            src_sp = all_spikes.get(src_idx, np.zeros(proj.source.n, dtype=np.int8))
            current = proj.propagate(src_sp)
            if tgt_idx in pop_to_currents:
                pop_to_currents[tgt_idx] += current

        local_spikes = self._step_local(pop_to_currents, all_spikes)
        all_spikes = self._exchange_spikes(local_spikes)

        if self.rank == 0:
            net = self.network
            for mon in net.spike_monitors:
                idx = pop_id_to_idx.get(id(mon.population))
                if idx is not None and idx in all_spikes:
                    mon.record(all_spikes[idx], t)
            for mon in net.rate_monitors:  # type: ignore[assignment]
                idx = pop_id_to_idx.get(id(mon.population))
                if idx is not None and idx in all_spikes:
                    mon.record(all_spikes[idx], t, dt)  # type: ignore[call-arg]