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+# Metric-capture mechanisms for the batched-RE regression benchmark
+
+Research ticket: [#27](https://github.com/farrisric/mcpy/issues/27), child of wayfinder map [#23](https://github.com/farrisric/mcpy/issues/23).
+Date: 2026-08-18.
+Line numbers refer to commit `918ac54` (branch point of `research/metric-capture`).
+
+## Question
+
+How should the harness capture peak allocated GPU memory, a GPU-utilization figure, per-step wall time split into relaxation vs overhead, and per-replica relax iteration counts, without perturbing the timings it measures?
+
+## Recommendations (one mechanism per metric)
+
+### (a) Peak allocated GPU memory: `torch.cuda.reset_peak_memory_stats()` after warmup + `torch.cuda.max_memory_allocated()` at the end
+
+This is the established pattern in `benchmark/internal/re_batched_scaling.py`.
+The peak counter is reset before the run at line 173 (after `torch.cuda.empty_cache()` at line 172) and reset again inside the timed step wrapper at line 167 once warmup completes, so first-step allocation and compile transients are excluded.
+The single read happens once per config at line 189 (`peak_torch = torch.cuda.max_memory_allocated() / 1024 ** 2`).
+`remeasure_supported_np.py:54-61` uses the same reset-then-read pattern per relaxation.
+Use allocated, never reserved: `gcmc_memory_growth.py:5-14` documents that reserved tracks the caching-allocator pool, which never shrinks and grows with fragmentation, and the repo's history shows earlier reserved-metric numbers were misleading.
+Reading a counter costs nothing on the timed path, so this mechanism is perturbation-free by construction.
+Nothing is missing; the harness copies the `re_batched_scaling.py` pattern verbatim.
+
+### (b) GPU utilization: the threaded nvidia-smi sampler (`GpuSampler`), windowed to post-warmup
+
+`re_batched_scaling.py:64-93` already defines a `GpuSampler` thread that polls `nvidia-smi --query-gpu=memory.used,utilization.gpu --format=csv,noheader,nounits` via subprocess every 0.15 s and stores `(perf_counter, mem_MB, util_pct)` tuples.
+The post-warmup window is selected at line 192 (`sampler.window(warm_done_t[0])`) and mean/max utilization are computed at lines 194-196.
+The sampler runs in its own thread and subprocess, never touches the CUDA stream, and its CPU cost is negligible, so it does not perturb the timings.
+It also yields whole-device memory as a free cross-check of the allocated figure (the docstring at lines 9-10 notes smi includes the CUDA context and resident model).
+No pynvml or `torch.cuda.utilization` is used anywhere in the repo; introducing one would add a dependency for no gain.
+Nothing is missing; lift `GpuSampler` into the harness and report the post-warmup mean (with max as a secondary column if wanted).
+
+### (c) Per-step wall time split into relaxation vs overhead: synced `perf_counter` brackets at two nesting levels
+
+The house timing pattern is `time.perf_counter()` before, `torch.cuda.synchronize()` immediately before the stop read, and warmup steps excluded from the aggregate.
+It appears in `re_batched_scaling.py:161-168` (step wrapper), `benchmark_alchemi_vs_mace.py:69-82` (`time_call`), and `remeasure_supported_np.py:54-60`.
+For the total step time, wrap `BatchedReplicaExchange._batched_gcmc_step` exactly as `re_batched_scaling.py:159-170` already does; the method (defined at `mcpy/ensembles/batched_replica_exchange.py:208-233`) has no timing of its own.
+For the relaxation share, wrap `calculator.get_potential_energies`, which is the single funnel every batched relaxation passes through: the contract is enforced at `batched_replica_exchange.py:98-102` and the call sites are `_rebatch_initial_energies` (line 202) and `_batched_single_move` (line 278).
+Overhead is then total step time minus the summed relax time inside that step; no third timer is needed.
+The added `torch.cuda.synchronize()` at the end of the relax bracket is effectively free because the calculator already forces a device-to-host copy to return Python floats; the sync only moves the wait to a named point.
+Do not rely on the library's own `_last_step_seconds` (`mcpy/ensembles/grand_canonical_ensemble.py:356-359`): it is unsynced, only reaches the console logger, and stays 0.0 under batched RE because `_batched_gcmc_step` bypasses `GrandCanonicalEnsemble._run` entirely (`batched_replica_exchange.py:228-233`).
+Missing piece: only the two harness-side wrappers themselves; no library change is required, and keeping the timing in the harness keeps the production path unperturbed.
+
+### (d) Per-replica relax iteration counts: record graph retirement steps in `_run_compacted`
+
+The existing metric surface is the `last_relax_steps` / `total_relax_steps` counter pair on `AlchemiFCalculator` (`mcpy/calculators/alchemi_f_calculator.py:95-96`), set from `opt.step_count` on the single-graph path (lines 163-164) and aggregated across chunks at lines 315-316.
+`remeasure_supported_np.py:64,128` and `benchmark_alchemi_vs_mace.py:148-159` already consume these counters.
+They are not per-replica in batched mode: `last_relax_steps` becomes the per-chunk max and `total_relax_steps` a sum, because nvalchemi's `BaseDynamics.step_count` is batch-global (`nvalchemi/dynamics/base.py:1455,1950`).
+The per-graph signal does exist transiently: `_run_compacted` steps FIRE manually and receives the converged-graph indices each iteration (`batch, conv = opt.step(batch)` at `alchemi_f_calculator.py:231`, `n_steps += 1` at 232), then retires those graphs at lines 240-242 without recording when each one converged.
+`BaseDynamics.step` returning the converged-index tensor (`nvalchemi/dynamics/base.py:1865`) is the only per-graph iteration signal nvalchemi exposes on the paths mcpy uses, so no upstream plumbing is needed.
+Missing piece: a small addition to `AlchemiFCalculator` that appends `(original graph row -> n_steps)` at the existing retirement point into a `last_relax_steps_per_graph` list (plus the cap-hit case for graphs that never converge), which the harness reads after each step and maps back to replicas via the chunk layout.
+The bookkeeping is a few integer appends at retirement points where CPU-side work (`_harvest`, lines 257-271) already happens, so it does not perturb the timed path.
+If windowed reporting is wanted, follow the house counter style of dual interval-plus-cumulative tallies with a reset method, as in `MoveSelector` (`mcpy/moves/move_selector.py:66-71,138-165`).
+
+## Inventory of existing measurement code
+
+### `benchmark/internal/re_batched_scaling.py`
+
+Peak allocated memory via `reset_peak_memory_stats` (lines 167, 173) and `max_memory_allocated` (line 189), with resident model-plus-context memory captured separately via `memory_allocated()` after a warm forward (line 236) and attached to every row (line 245).
+Whole-device memory and utilization via the threaded `GpuSampler` nvidia-smi poller (lines 64-93), windowed post-warmup (lines 192-196).
+Step timing via a monkey-patched `_batched_gcmc_step` wrapper with `synchronize()` before the stop read (lines 159-168) and warmup exclusion (line 197, default warmup 2 at line 383).
+OOM configs are caught and recorded as `status='oom'` (lines 180-183).
+No relax-iteration counting and no alloc-conf handling.
+
+### `benchmark/internal/gcmc_memory_growth.py`
+
+Per-step instantaneous triple read after `synchronize()` (lines 84-91): `memory_allocated`, `memory_reserved`, and a one-shot nvidia-smi helper (lines 41-48).
+Its docstring (lines 5-14) is the canonical allocated-vs-reserved pitfall text and documents running under `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True` set in the environment.
+End-of-run `empty_cache()` probe distinguishes fragmentation from a leak (lines 99-106).
+No timing, no utilization, no iteration counting.
+
+### `benchmark/internal/remeasure_supported_np.py`
+
+Per-relax `synchronize(); empty_cache(); reset_peak_memory_stats()` then reads of both `max_memory_allocated` and `max_memory_reserved` (lines 50-65, repeated for the batched point at lines 96-104).
+Relax timing is median-of-3 `perf_counter` with pre-stop `synchronize()` (lines 57-60, 69) and explicit uncounted compile warmups (lines 87, 91).
+GCMC throughput is aggregate: one `perf_counter` pair around `gcmc.run(GCMC_STEPS)` divided by step count (lines 123-127), which cannot split relax from overhead.
+Reads `calc.last_relax_steps` per relax (line 64) and `calc.total_relax_steps` after the run (line 128).
+
+### `benchmark/internal/benchmark_alchemi_vs_mace.py`
+
+Generic `time_call(fn, n_warmup, n_repeats)` helper: untimed warmups, then `sync(); t0; fn(); sync(); append` (lines 69-82), aggregated as a mean (lines 116-117, 153-154).
+Resets `total_relax_steps = 0` per system and averages over warmup-plus-repeats calls (lines 148-149, 157-159); note the warmup relaxations are included in the step average even though excluded from timing.
+No memory or utilization capture.
+
+### `mcpy/ensembles/`
+
+`BaseEnsemble` initializes `_last_step_seconds` (`base_ensemble.py:51`); `GrandCanonicalEnsemble._run` fills it unsynced and logs it to the console only, on outfile-interval steps (`grand_canonical_ensemble.py:356-365`).
+`BatchedReplicaExchange` has no timing, no memory, and no iteration instrumentation; `_batched_gcmc_step` (lines 208-233) is bare and calls each replica's `write_outfile` directly, bypassing `_run`.
+`write_outfile` rows carry step, atom counts, energy, and move-acceptance ratios only (`grand_canonical_ensemble.py:143-176`; `batched_replica_exchange.py:525-543`).
+The exchange counters with midpoint snapshots (`batched_replica_exchange.py:113-119,148-150`) are a ready-made template for windowed metrics.
+No ensemble reads `last_relax_steps` or `total_relax_steps`.
+
+### `mcpy/calculators/`
+
+`AlchemiFCalculator` holds the only relax-iteration counters (details in recommendation d).
+`MACE_F_Calculator` mirrors the counter pair from ASE `opt.nsteps` (`mace_f_calculator.py:27-28,54-55`).
+`BaseCalculator` discards its optimizer and exposes no counters (`base_calculator.py:14-22`).
+`AlchemiCalculator` is energy-only with no relaxation or instrumentation; `energy_only=True` drops forces so no autograd graph is built (`alchemi_calculator.py:78-83`).
+`_alchemi_common.py` imports `DynamicsContext` and the freeze/NaN hooks (lines 16, 19) but records no metrics; a custom `AFTER_STEP` or `ON_CONVERGE` hook receiving `DynamicsContext` (`nvalchemi/hooks/_context.py:60`, carrying `step_count` and `converged_mask`) is a viable alternative capture point for (d), but the `_run_compacted` retirement point is simpler because the loop already owns `n_steps` and the compaction bookkeeping.
+
+### Orphan data note
+
+`benchmark/internal/relax_step_spread.csv` and `relax_step_spread_smoke.csv` exist but no script in the repo references them; the script that produced per-graph relax-step spread data appears to have been deleted.