feat(1.1): supervise cpu group with ProcessPoolExecutor kernel offload

Hosts clusterer/campaign-clusterer/attribution/reuse-correlate in one
process. The two O(n^2) connected-components kernels (cluster_observations,
cluster_identities) offload to ONE shared forkserver pool via decnet.offload
.run_kernel, so they run in parallel instead of serialising under the GIL.

- offload.run_kernel: pool when installed + offload_if holds, else inline.
  Standalone workers and all tests run inline => behaviour unchanged
  (424 clustering/correlation tests green).
- offload_if gates on input size (>=256) to skip pickle cost on small passes.
- forkserver (not fork): supervisor is multithreaded via bus clients.
- attribution/reuse co-located but not offloaded yet (lighter; same run_kernel
  path extends to them if profiling shows contention).
- systemd unit Conflicts= the 4 units it replaces; no docker/raw-socket priv.

decnet/clustering/impl/connected_components.py

@@ -42,12 +42,17 @@ from decnet.clustering.impl.similarity import (
     combined_edge_weight,
 )
 from decnet.logging import get_logger
+from decnet.offload import run_kernel
 from decnet.profiler.identity_rollup import extract_fp_summaries
 from decnet.util.simhash import from_bytes8, to_bytes8
 from decnet.web.db.repository import BaseRepository
 
 log = get_logger("clustering.connected_components")
 
+# Below this many observations the O(n^2) pass is cheaper than the pickle
+# round-trip to a pool worker, so run inline even when a pool is installed.
+_OFFLOAD_MIN_OBSERVATIONS = 256
+
 # Per-session SimHash observations of the keystroke-rhythm biometric; the
 # rollup folds them into one identity-level centroid.
 _DIGRAPH_PRIMITIVE = "motor.digraph_simhash"
@@ -173,7 +178,11 @@ class ConnectedComponentsClusterer(Clusterer):
             obs = from_attacker_row(r)
             observations.append(obs)
             row_by_id[obs.observation_id] = r
-        labels = cluster_observations(observations)
+        labels = await run_kernel(
+            cluster_observations,
+            observations,
+            offload_if=len(observations) >= _OFFLOAD_MIN_OBSERVATIONS,
+        )
 
         # Group observations by predicted cluster.
         components: dict[str, list[str]] = {}