src/simulation_loop.py

"""Monte Carlo episodes: MMPP traffic through a packet-level queueing network under six routers.



One episode samples a topology from its design cell, the failure/degradation timeline, F flows

between distinct ordered (source, sink) pairs whose mean rates realise the cell's offered load,

and each flow's Markov-modulated Poisson arrival stream. That identical scenario is then replayed

under every router, so any difference between routers is attributable to the routing policy

alone. The queueing model is deliberately explicit:



* every node owns one drop-tail FIFO buffer of ``buffer_size`` packets shared by all flows;

* every directed link forwards at most ``capacity`` packets per step, taking the oldest packets at

  its tail whose next hop crosses it (virtual output queueing, no head-of-line blocking);

* a packet forwarded at step t over a link of latency ℓ reaches the head node at step t + ℓ, where

  it is delivered (its flow's sink), admitted, or dropped if the buffer is full;

* routing decisions are recomputed every step (potential, potential_split, adaptive_shortest_path)

  or at every topology change (potential_static, shortest_path, ecmp).



Every step is vectorised with NumPy over the packets in flight; episodes are mapped across CPU

cores with ``multiprocessing.Pool`` and streamed to Parquet shard by shard.

"""
from __future__ import annotations

import multiprocessing as mp
import time
from dataclasses import dataclass
from functools import partial
from pathlib import Path
from typing import Dict, List

import numpy as np
import pyarrow as pa

from .config import SimConfig
from .design import RATE_SIGMA, Cell, TrafficProfile, locate
from .graph_generator import Topology, TopologyEvent, generate_timeline, generate_topology
from .physics_engine import (COST_QUANTUM, EcmpTable, LiveGraph, PotentialField, currents,
                             dijkstra_next_hops, live_graph, path_metrics, spray_next_hops,
                             steepest_next_hops)
from .telemetry_logger import SCHEMAS, completed_shards, list_column, write_shard

FIELD_ROUTER = "potential"  # the router whose potential field is stored


@dataclass(frozen=True)
class Flows:
    source: np.ndarray       # (F,) int16
    sink: np.ndarray         # (F,) int16
    mean_rate: np.ndarray    # (F,) float32 long-run packets/step
    idle_rate: np.ndarray    # (F,) float32
    burst_rate: np.ndarray   # (F,) float32
    min_hops: np.ndarray     # (F,) int16 on the base topology
    min_latency: np.ndarray  # (F,) int16 on the base topology
    total_capacity: float    # Σ over directed base links of capacity, packets/step


def sample_flows(cfg: SimConfig, cell: Cell, topo: Topology, base: LiveGraph,

                 rng: np.random.Generator) -> Flows:
    endpoints = topo.endpoints
    k = len(endpoints)
    n_flows = cfg.flows_per_endpoint * k
    pair = rng.choice(k * (k - 1), n_flows, replace=False)          # distinct ordered pairs
    si, ti = pair // (k - 1), pair % (k - 1)
    ti = ti + (ti >= si)
    source, sink = endpoints[si], endpoints[ti]
    min_hops, min_latency = path_metrics(base, source, sink)
    weight = np.exp(RATE_SIGMA * rng.standard_normal(n_flows))        # elephants and mice
    total_capacity = float(base.capacity.sum())
    mean = cell.load * total_capacity * weight / float(weight @ min_hops)
    idle, burst = cell.profile.rates(mean)
    return Flows(source, sink, mean.astype(np.float32), idle.astype(np.float32),
                 burst.astype(np.float32), min_hops, min_latency, total_capacity)


def sample_mmpp(cfg: SimConfig, profile: TrafficProfile, flows: Flows, rng: np.random.Generator):
    """Two-state MMPP per flow: (state (F, T) int8 with 1 = burst, arrivals (F, T) int32)."""
    n_flows = len(flows.source)
    state = np.zeros((n_flows, cfg.steps), np.int8)
    if profile.peak_ratio > 1.0:
        p_ib, p_bi = 1.0 / profile.mean_idle_steps, 1.0 / profile.mean_burst_steps
        state[:, 0] = rng.random(n_flows) < profile.duty  # stationary start
        u = rng.random((n_flows, cfg.steps))
        for t in range(1, cfg.steps):
            idle = state[:, t - 1] == 0
            state[:, t] = np.where(idle, u[:, t] < p_ib, u[:, t] >= p_bi)
    rate = np.where(state == 1, flows.burst_rate[:, None], flows.idle_rate[:, None])
    return state, rng.poisson(rate).astype(np.int32)


def _rank_within_groups(sorted_keys: np.ndarray) -> np.ndarray:
    """Position of every element inside its run of equal keys (keys must be sorted)."""
    n = len(sorted_keys)
    starts = np.flatnonzero(np.r_[True, sorted_keys[1:] != sorted_keys[:-1]])
    return np.arange(n) - np.repeat(starts, np.diff(np.r_[starts, n]))


@dataclass
class RouterRun:
    queue: np.ndarray            # (T, N) int16 buffer occupancy after admission, before forwarding
    node_drops: np.ndarray       # (T, N) int16 packets dropped at each node this step
    link_load: np.ndarray        # (T, 2E) int16 packets forwarded over each directed base link
    potential: np.ndarray        # (S, tracked, N) float32 field snapshots every `stride` steps, or None
    per_flow: Dict[str, np.ndarray]  # (T, F) int32: admitted, delivered, dropped, queued, in_transit, route_changes
    delay_sum: np.ndarray        # (T, F) sum of end-to-end delays of packets delivered this step
    in_flight: np.ndarray        # (F,) packets still queued or on a link when the episode ends
    delays: List[np.ndarray]     # per flow, end-to-end delay of every delivered packet
    propagation: List[np.ndarray]  # per flow, propagation part of that delay
    hops: List[np.ndarray]       # per flow, hop count of every delivered packet
    utilisation: float           # packets forwarded / directed link capacity, over all link-steps
    saturation: float            # fraction of directed link-steps forwarding at full capacity


def run_router(cfg: SimConfig, topo: Topology, flows: Flows, arrivals: np.ndarray,

               changes: list, router: str, field_stride: int) -> RouterRun:
    n, n_flows, steps, buffer = topo.n_nodes, len(flows.source), cfg.steps, cfg.buffer_size
    n_edges = len(topo.edges)
    source, sink = flows.source, flows.sink
    flow_ids = np.arange(n_flows)
    sinks, sink_index = np.unique(sink, return_inverse=True)          # per-sink tables serve every flow
    background = cfg.background_injection / (n - 1)
    tracked = min(cfg.tracked_flows, n_flows)
    field_based = router in ("potential", "potential_split", "potential_static")
    per_step = router in ("potential", "potential_split", "adaptive_shortest_path")

    # Packets in flight as a structure of arrays.
    p_flow = np.zeros(0, np.int16)
    p_birth = np.zeros(0, np.int32)
    p_node = np.zeros(0, np.int16)
    p_ready = np.zeros(0, np.int32)  # step at which the packet is (or becomes) available at p_node
    p_prop = np.zeros(0, np.int32)   # propagation delay accumulated so far
    p_hops = np.zeros(0, np.int16)
    p_id = np.zeros(0, np.int64)     # global creation order: FIFO tie-break and spraying coordinate
    next_id = 0

    queue_log = np.zeros((steps, n), np.int16)
    drop_log = np.zeros((steps, n), np.int16)
    link_log = np.zeros((steps, 2 * n_edges), np.int16)
    phi_log = (np.zeros((-(-steps // field_stride), tracked, n), np.float32)
               if router == FIELD_ROUTER else None)
    per_flow = {k: np.zeros((steps, n_flows), np.int32) for k in
                ("admitted", "delivered", "dropped", "queued", "in_transit", "route_changes")}
    delay_sum = np.zeros((steps, n_flows), np.float64)
    done_flow, done_delay, done_prop, done_hops = [], [], [], []
    util_num = util_den = 0.0
    saturated = link_steps = 0
    previous_hops = None
    change_i = 0

    for t in range(steps):
        # 1. Topology in force at this step; rebuild the routing state when links change.
        if change_i < len(changes) and changes[change_i][0] == t:
            g = live_graph(n, topo.edges, changes[change_i][1], topo.latency)
            change_i += 1
            if field_based:
                field = PotentialField(g, source, sink, cfg.source_injection)
                if router == "potential_static":
                    next_hop = steepest_next_hops(currents(field.solve(np.full(n, background)), g), g)
            elif router in ("shortest_path", "ecmp"):
                by_sink, dist = dijkstra_next_hops(g.latency.astype(np.float64), g, sinks)
                next_hop = by_sink[sink_index]
                if router == "ecmp":
                    ecmp = EcmpTable(dist, g)

        # 2. New packets appear at their sources.
        new = arrivals[:, t]
        n_new = int(new.sum())
        if n_new:
            new_flow = np.repeat(flow_ids, new).astype(np.int16)
            p_flow = np.concatenate([p_flow, new_flow])
            p_birth = np.concatenate([p_birth, np.full(n_new, t, np.int32)])
            p_node = np.concatenate([p_node, source[new_flow]])
            p_ready = np.concatenate([p_ready, np.full(n_new, t, np.int32)])
            p_prop = np.concatenate([p_prop, np.zeros(n_new, np.int32)])
            p_hops = np.concatenate([p_hops, np.zeros(n_new, np.int16)])
            p_id = np.concatenate([p_id, np.arange(next_id, next_id + n_new)])
            next_id += n_new

        # 3. Packets reaching a node this step: deliver at the sink, otherwise admit (drop-tail).
        per_flow["admitted"][t] = new
        arriving = p_ready == t
        if arriving.any():
            at_sink = arriving & (p_node == sink[p_flow])
            remove = at_sink.copy()
            if at_sink.any():
                f, d = p_flow[at_sink], (t - p_birth[at_sink]).astype(np.int32)
                np.add.at(per_flow["delivered"][t], f, 1)
                np.add.at(delay_sum[t], f, d)
                done_flow.append(f)
                done_delay.append(d)
                done_prop.append(p_prop[at_sink])
                done_hops.append(p_hops[at_sink])
            entering = np.flatnonzero(arriving & ~at_sink)
            if len(entering):
                space = buffer - np.bincount(p_node[p_ready < t], minlength=n)
                entering = entering[np.lexsort((p_id[entering], p_node[entering]))]  # per node, oldest first
                node = p_node[entering]
                lost = entering[_rank_within_groups(node) >= space[node]]
                if len(lost):
                    np.add.at(per_flow["dropped"][t], p_flow[lost], 1)
                    np.add.at(drop_log[t], p_node[lost], 1)
                    at_source = lost[p_birth[lost] == t]
                    per_flow["admitted"][t] -= np.bincount(p_flow[at_source], minlength=n_flows)
                    remove[lost] = True
            keep = ~remove
            p_flow, p_birth, p_node, p_ready = p_flow[keep], p_birth[keep], p_node[keep], p_ready[keep]
            p_prop, p_hops, p_id = p_prop[keep], p_hops[keep], p_id[keep]

        # 4. Telemetry snapshot of every buffer, then the routing decision.
        in_buffer = p_ready <= t
        queue = np.bincount(p_node[in_buffer], minlength=n)
        queue_log[t] = queue
        per_flow["queued"][t] = np.bincount(p_flow[in_buffer], minlength=n_flows)
        per_flow["in_transit"][t] = np.bincount(p_flow[~in_buffer], minlength=n_flows)
        if per_step:
            if router == "adaptive_shortest_path":
                cost = np.round((g.latency + queue[g.src] / g.capacity) / COST_QUANTUM)
                by_sink, _ = dijkstra_next_hops(cost, g, sinks)
                next_hop = by_sink[sink_index]
            else:
                phi = field.solve(background + cfg.congestion_gain * queue / buffer)
                cur = currents(phi, g)
                next_hop = steepest_next_hops(cur, g)
                if router == FIELD_ROUTER and t % field_stride == 0:
                    phi_log[t // field_stride] = phi[:tracked]
        if previous_hops is not None and next_hop is not previous_hops:
            per_flow["route_changes"][t] = (next_hop != previous_hops).sum(axis=1)
        previous_hops = next_hop

        # 5. Forwarding: each directed link takes the oldest packets routed over it, up to capacity.
        idx = np.flatnonzero(in_buffer)
        if len(idx):
            node, flow = p_node[idx], p_flow[idx]
            if router == "potential_split":
                hop = spray_next_hops(cur, g, node, flow, p_id[idx])
            elif router == "ecmp":
                hop = ecmp.hops(sink_index[flow], node, p_id[idx])
            else:
                hop = next_hop[flow, node]
            routable = hop >= 0
            idx, hop = idx[routable], hop[routable]
            link = g.dir_edge[p_node[idx], hop]
            order = np.lexsort((p_id[idx], p_ready[idx], link))
            idx, link = idx[order], link[order]
            forward = _rank_within_groups(link) < g.capacity[link]
            idx, link = idx[forward], link[forward]
            p_ready[idx] = t + g.latency[link]
            p_prop[idx] += g.latency[link]
            p_hops[idx] += 1
            p_node[idx] = g.dst[link]
            load = np.bincount(link, minlength=len(g.src))
            link_log[t] = np.bincount(g.base, weights=load, minlength=2 * n_edges).astype(np.int16)
            util_num += load.sum()
            saturated += int((load == g.capacity).sum())
        util_den += g.capacity.sum()
        link_steps += len(g.src)

    done_flow = np.concatenate(done_flow) if done_flow else np.zeros(0, np.int16)
    done_delay = np.concatenate(done_delay) if done_delay else np.zeros(0, np.int32)
    done_prop = np.concatenate(done_prop) if done_prop else np.zeros(0, np.int32)
    done_hops = np.concatenate(done_hops) if done_hops else np.zeros(0, np.int16)
    in_flight = np.bincount(p_flow, minlength=n_flows).astype(np.int32)
    conserved = per_flow["delivered"].sum(0) + per_flow["dropped"].sum(0) + in_flight
    assert np.array_equal(arrivals.sum(axis=1), conserved), "packet conservation"
    order = np.argsort(done_flow, kind="stable")
    bounds = np.searchsorted(done_flow[order], np.arange(n_flows + 1))

    def by_flow(values: np.ndarray) -> List[np.ndarray]:
        values = values[order]
        return [values[bounds[f]:bounds[f + 1]] for f in flow_ids]

    return RouterRun(queue_log, drop_log, link_log, phi_log, per_flow, delay_sum, in_flight,
                     by_flow(done_delay), by_flow(done_prop), by_flow(done_hops),
                     util_num / util_den, saturated / link_steps)


def field_stride(cfg: SimConfig, n_nodes: int, n_flows: int) -> int:
    tracked = min(cfg.tracked_flows, n_flows)
    return max(1, -(-4 * n_nodes * tracked * cfg.steps // cfg.field_budget_bytes))


def simulate_episode(cfg: SimConfig, episode_id: int) -> Dict[str, pa.Table]:
    """Sample one scenario, replay it under every configured router, return its telemetry tables."""
    cells = cfg.cells
    cell_index, replicate, split = locate(episode_id, len(cells))
    cell = cells[cell_index]
    rng = np.random.default_rng([cfg.seed, episode_id])
    topo = generate_topology(cfg, cell, rng)
    base = live_graph(topo.n_nodes, topo.edges, topo.capacity, topo.latency)
    flows = sample_flows(cfg, cell, topo, base, rng)
    mmpp_state, arrivals = sample_mmpp(cfg, cell.profile, flows, rng)
    events, changes = generate_timeline(cfg, topo, cell.dynamics, rng)
    stride = field_stride(cfg, topo.n_nodes, len(flows.source))
    runs = {r: run_router(cfg, topo, flows, arrivals, changes, r, stride) for r in cfg.routers}
    return _episode_tables(cfg, episode_id, cell, cell_index, replicate, split, topo, flows,
                           mmpp_state, arrivals, events, stride, runs)


def _stat(values: List[np.ndarray], fn, empty=np.nan, dtype=np.float32) -> np.ndarray:
    return np.array([fn(v) if len(v) else empty for v in values], dtype)


def _episode_tables(cfg: SimConfig, episode_id: int, cell: Cell, cell_index: int, replicate: int,

                    split: str, topo: Topology, flows: Flows, mmpp_state: np.ndarray,

                    arrivals: np.ndarray, events: List[TopologyEvent], stride: int,

                    runs: Dict[str, RouterRun]) -> Dict[str, pa.Table]:
    n, n_flows, steps, n_edges = topo.n_nodes, len(flows.source), cfg.steps, len(topo.edges)
    tracked = min(cfg.tracked_flows, n_flows)
    step = np.arange(steps, dtype=np.int32)
    offered = arrivals.sum(axis=1).astype(np.int32)
    profile = cell.profile

    def ep(size: int) -> np.ndarray:
        return np.full(size, episode_id, np.int32)

    def text(value: str, size: int) -> pa.Array:
        return pa.array([value] * size, pa.string())

    tables = {
        "episodes": pa.table({
            "episode_id": ep(1), "cell_id": np.array([cell_index], np.int16),
            "replicate": np.array([replicate], np.int16), "split": [split],
            "topology": [cell.topology], "size": np.array([cell.size], np.int16),
            "traffic_profile": [cell.traffic_profile], "load_level": [cell.load_level],
            "dynamics_level": [cell.dynamics_level],
            "n_nodes": np.array([n], np.int16), "n_edges": np.array([n_edges], np.int32),
            "n_flows": np.array([n_flows], np.int16), "tracked_flows": np.array([tracked], np.int16),
            "steps": np.array([steps], np.int32), "field_stride": np.array([stride], np.int16),
            "offered_load": np.array([cell.load], np.float32),
            "total_capacity": np.array([flows.total_capacity], np.float32),
            "edge_u": [topo.edges[:, 0]], "edge_v": [topo.edges[:, 1]],
            "capacity": [topo.capacity], "latency": [topo.latency],
            "node_role": [topo.node_role], "node_x": [topo.node_xy[:, 0]], "node_y": [topo.node_xy[:, 1]],
            "flow_source": [flows.source], "flow_sink": [flows.sink],
            "flow_mean_rate": [flows.mean_rate], "flow_idle_rate": [flows.idle_rate],
            "flow_burst_rate": [flows.burst_rate],
            "p_idle_to_burst": np.array([1.0 / profile.mean_idle_steps if profile.peak_ratio > 1 else 0.0], np.float32),
            "p_burst_to_idle": np.array([1.0 / profile.mean_burst_steps if profile.peak_ratio > 1 else 0.0], np.float32),
        }, schema=SCHEMAS["episodes"]),
        "events": pa.table({
            "episode_id": ep(len(events)),
            "kind": [e.kind for e in events],
            "start": np.array([e.start for e in events], np.int32),
            "end": np.array([e.end for e in events], np.int32),
            "node": np.array([e.node for e in events], np.int16),
            "edge_u": np.array([topo.edges[e.edge, 0] if e.edge >= 0 else -1 for e in events], np.int16),
            "edge_v": np.array([topo.edges[e.edge, 1] if e.edge >= 0 else -1 for e in events], np.int16),
            "factor": np.array([e.factor for e in events], np.float32),
        }, schema=SCHEMAS["events"]),
    }

    parts = {name: [] for name in ("router_summary", "flow_summary", "flow_telemetry",
                                   "network_telemetry", "link_telemetry")}
    for router, run in runs.items():
        pf = run.per_flow
        delivered, dropped = pf["delivered"].sum(0), pf["dropped"].sum(0)
        all_delays = np.concatenate(run.delays)
        with np.errstate(invalid="ignore", divide="ignore"):
            flow_mean_delay = (run.delay_sum / pf["delivered"]).astype(np.float32)
            step_mean_delay = (run.delay_sum.sum(1) / pf["delivered"].sum(1)).astype(np.float32)
        parts["router_summary"].append(pa.table({
            "episode_id": ep(1), "router": [router],
            "offered": np.array([offered.sum()], np.int32), "delivered": np.array([delivered.sum()], np.int32),
            "dropped": np.array([dropped.sum()], np.int32), "in_flight": np.array([run.in_flight.sum()], np.int32),
            "loss_ratio": np.array([dropped.sum() / max(offered.sum(), 1)], np.float32),
            "mean_delay": np.array([all_delays.mean() if len(all_delays) else np.nan], np.float32),
            "p99_delay": np.array([np.percentile(all_delays, 99) if len(all_delays) else np.nan], np.float32),
            "mean_queue": np.array([run.queue.mean()], np.float32),
            "max_queue": np.array([run.queue.max()], np.int32),
            "link_utilisation": np.array([run.utilisation], np.float32),
            "link_saturation": np.array([run.saturation], np.float32),
            "route_changes": np.array([pf["route_changes"].sum()], np.int32),
        }, schema=SCHEMAS["router_summary"]))
        quantiles = np.array([np.percentile(d, [50, 95, 99]) if len(d) else [np.nan] * 3
                              for d in run.delays], np.float32)
        parts["flow_summary"].append(pa.table({
            "episode_id": ep(n_flows), "router": text(router, n_flows),
            "flow": np.arange(n_flows, dtype=np.int16), "source": flows.source, "sink": flows.sink,
            "mean_rate": flows.mean_rate, "min_hops": flows.min_hops, "min_latency": flows.min_latency,
            "offered": offered, "delivered": delivered, "dropped": dropped, "in_flight": run.in_flight,
            "loss_ratio": (dropped / np.maximum(offered, 1)).astype(np.float32),
            "mean_delay": _stat(run.delays, np.mean), "delay_std": _stat(run.delays, np.std),
            "p50_delay": quantiles[:, 0], "p95_delay": quantiles[:, 1], "p99_delay": quantiles[:, 2],
            "max_delay": _stat(run.delays, np.max, -1, np.int32),
            "mean_queueing_delay": np.array([(d - p).mean() if len(d) else np.nan
                                             for d, p in zip(run.delays, run.propagation)], np.float32),
            "mean_path_latency": _stat(run.propagation, np.mean),
            "mean_hops": _stat(run.hops, np.mean),
            "route_changes": pf["route_changes"].sum(0).astype(np.int32),
        }, schema=SCHEMAS["flow_summary"]))
        tf = slice(0, tracked)
        parts["flow_telemetry"].append(pa.table({
            "episode_id": ep(steps * tracked), "router": text(router, steps * tracked),
            "step": np.repeat(step, tracked), "flow": np.tile(np.arange(tracked, dtype=np.int16), steps),
            "mmpp_state": mmpp_state[tf].T.ravel(), "offered": arrivals[tf].T.ravel(),
            "admitted": pf["admitted"][:, tf].ravel(), "delivered": pf["delivered"][:, tf].ravel(),
            "dropped": pf["dropped"][:, tf].ravel(), "queued": pf["queued"][:, tf].ravel(),
            "in_transit": pf["in_transit"][:, tf].ravel(), "mean_delay": flow_mean_delay[:, tf].ravel(),
            "route_changes": pf["route_changes"][:, tf].ravel(),
        }, schema=SCHEMAS["flow_telemetry"]))
        parts["network_telemetry"].append(pa.table({
            "episode_id": ep(steps), "router": text(router, steps), "step": step,
            "offered": arrivals.sum(0).astype(np.int32), "admitted": pf["admitted"].sum(1),
            "delivered": pf["delivered"].sum(1), "dropped": pf["dropped"].sum(1),
            "queued": pf["queued"].sum(1), "in_transit": pf["in_transit"].sum(1),
            "mean_delay": step_mean_delay, "route_changes": pf["route_changes"].sum(1),
            "queue_depth": list_column(run.queue, pa.int16()),
            "node_dropped": list_column(run.node_drops, pa.int16()),
        }, schema=SCHEMAS["network_telemetry"]))
        parts["link_telemetry"].append(pa.table({
            "episode_id": ep(steps), "router": text(router, steps), "step": step,
            "load_uv": list_column(run.link_load[:, :n_edges], pa.int16()),
            "load_vu": list_column(run.link_load[:, n_edges:], pa.int16()),
        }, schema=SCHEMAS["link_telemetry"]))
        if run.potential is not None:
            snapshots = run.potential.shape[0]
            tables["potential_field"] = pa.table({
                "episode_id": ep(snapshots), "step": (np.arange(snapshots) * stride).astype(np.int32),
                "potential": list_column(run.potential.reshape(snapshots, -1), pa.float32()),
            }, schema=SCHEMAS["potential_field"])
    for name, chunks in parts.items():
        tables[name] = pa.concat_tables(chunks)
    return tables


def run_sweep(cfg: SimConfig, data_dir: Path, workers: int, log=print) -> None:
    """Map every unfinished shard of episodes across `workers` processes and stream it to Parquet."""
    n_shards = -(-cfg.episodes // cfg.shard_episodes)

    def shard_ids(shard: int) -> range:
        return range(shard * cfg.shard_episodes, min((shard + 1) * cfg.shard_episodes, cfg.episodes))

    done = completed_shards(data_dir, n_shards, cfg.routers)
    todo = [s for s in range(n_shards) if s not in done]
    if done:
        log(f"Resuming: {len(done)}/{n_shards} shards already complete.")
    if not todo:
        log("Nothing to do: every shard is complete.")
        return
    episodes_todo = [e for s in todo for e in shard_ids(s)]
    log(f"Simulating {len(episodes_todo)} episodes x {len(cfg.routers)} routers "
        f"({len(cfg.cells)} design cells) on {workers} workers ...")
    start, finished = time.time(), 0
    with mp.get_context("spawn").Pool(workers) as pool:  # same start method on every platform
        results = pool.imap(partial(simulate_episode, cfg), episodes_todo, chunksize=1)
        for shard in todo:
            ids = shard_ids(shard)
            write_shard(data_dir, shard, [next(results) for _ in ids])
            finished += len(ids)
            elapsed = time.time() - start
            eta = elapsed / finished * (len(episodes_todo) - finished)
            log(f"  shard {shard + 1:>4}/{n_shards}  episodes {finished:>6}/{len(episodes_todo)}  "
                f"elapsed {elapsed / 3600:5.2f} h  eta {eta / 3600:5.2f} h")
    log(f"Done in {(time.time() - start) / 3600:.2f} h.")