com4FlowPy: optional Numba compute engine (bit-identical, ~30-45x faster)

avaframe/com4FlowPy/benchmark/.gitignore

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+# Local run artifacts and machine-specific fixtures — never committed.
+# Concrete fixtures carry absolute data paths; only the *.template is tracked.
+runs/
+fixtures/*.ini
+
+# debug repro (not for commit)
+repro_tile.py

avaframe/com4FlowPy/benchmark/README.md

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+# com4FlowPy Numba-engine benchmark & validation harness
+
+`flowpy_bench.py` runs com4FlowPy in-process on a *fixture* (a `.ini` describing
+inputs + `[GENERAL]` config), measures **wall-clock** and **peak RAM**, and
+optionally compares every output raster against a **reference directory**.
+
+It exercises the normal `com4FlowPyMain` code path (as in the `useCustomPaths`
+branch of `runCom4FlowPy.py`); the only added knob is `--engine`, which selects
+the compute engine:
+
+* `--engine python` — the stock `Cell`-based BFS (default; always available).
+* `--engine numba`  — the ported `@njit` kernel (once merged).
+
+## Usage
+
+```bash
+# Freeze the stock-engine oracle on the correctness fixture
+python flowpy_bench.py fixtures/connaught_frequent.ini --engine python --tag oracle
+
+# A/B the numba engine against it (bit-equivalence check)
+python flowpy_bench.py fixtures/connaught_frequent.ini --engine numba
+
+# Benchmark on BFW's own 10 m run and cross-check vs their stored outputs
+python flowpy_bench.py fixtures/bfw_10m.ini --engine python
+python flowpy_bench.py fixtures/bfw_10m.ini --engine numba --cpu 8
+```
+
+Each run writes `runs/<tag>_<engine>_<timestamp>/summary.json` with timing,
+peak RAM, and per-output comparison metrics.
+
+## Fixtures
+
+| Fixture | Role | Config highlights |
+|---|---|---|
+| `connaught_frequent.ini` / `connaught_extreme.ini` | correctness oracle (fast A/B) | autoATES: `forestDetrainment`, uniform α (28 / 18) |
+| `bfw_10m.ini` | speed benchmark + reference cross-check | BFW: `forestFriction`, `variableAlpha`, `variableUmax`, α-layer + Umax-layer |
+
+Fixture `.ini`s carry absolute paths to input rasters. The rasters themselves
+are **not** committed (size/licensing); edit the paths for your machine, or copy
+a fixture to `*.local.ini` (gitignored). `runs/` is also gitignored.
+
+## Comparison metrics (per output raster, over pixels valid in both)
+
+`n_valid`, `n_exact`, `n_diff` (|Δ| > `--tol`), `frac_diff_ppm`,
+`max_abs_diff`, `mean_abs_diff`. Use `--tol 0` for bit-exact; a small tol to
+ignore IEEE-754 ULP noise from operation-order differences.

avaframe/com4FlowPy/benchmark/REPORT.md

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+# com4FlowPy Numba compute-engine — validation & benchmark report
+
+*Living document — updated at each phase. Audience: BFW / AvaFrame maintainers.*
+
+## Goal
+
+Add an optional Numba (`@njit`) compute engine to com4FlowPy that reproduces
+the existing Python engine's results while cutting per-tile runtime, so large /
+repeated runs (e.g. multi-site ATES workflows) become tractable. The engine is
+selected by a config flag (`engine = python | numba`, default `python`); tiling,
+multiprocessing, I/O, forest, and variable-parameter handling are unchanged.
+
+Motivation: a prototype on standalone FlowPy (fork of `avaframe/FlowPy`,
+`foreste_detrainment`) reached ~100× by rewriting the per-cell BFS as a single
+`@njit` function over flat arrays. com4FlowPy already parallelizes (tiling +
+`Pool` over release chunks) but still runs the inner BFS through the Python
+`Cell` class — the target of this port.
+
+## Method
+
+`benchmark/flowpy_bench.py` runs com4FlowPy in-process via `com4FlowPyMain`
+(the `useCustomPaths` path), recording wall-clock, peak RAM, and per-output
+raster comparison against a reference directory. Fixtures under
+`benchmark/fixtures/` pin exact inputs + `[GENERAL]` config. Correctness is
+established by A/B comparison of the two engines on identical inputs (the
+Python engine is the oracle); a run is additionally cross-checked against BFW's
+own stored outputs.
+
+Machine: 16 physical cores / 32 threads.
+
+All results below were re-verified on the current rebased code
+(OpenNHM/AvaFrame master @6e02e45d + this branch), with peak memory measured via
+PSS. Current-code headline (8 cores): BFW 10 m 302 s → 6.7 s (**45×**); 5 m
+Connaught extreme 5714 s → 193 s (**30×**). numba (float64) is bit-identical to
+the Python engine on both (zDelta / fpTravelAngleMax; 5 m differs only at
+flat-terrain routing knife-edges, see below). PSS memory is on par with the
+Python engine (BFW: 8.6 vs 7.1 GiB; 5 m: 23.1 vs 19.9 GiB).
+
+## Phase 0 — baselines & oracle (stock Python engine) — DONE
+
+| Fixture | Domain | Config | Wall | Peak RAM | Cores |
+|---|---|---|---|---|---|
+| Connaught frequent | 222×222 @ 21.3 m | forestDetrainment, α=28 | 17.8 s | 6.0 GiB | 16 |
+| Connaught extreme | 222×222 @ 21.3 m | forestDetrainment, α=18 | 109 s | 7.6 GiB | 16 |
+| **BFW 10 m** | 482×513 @ 10 m | forestFriction, variableAlpha, variableUmax, α-layer + Umax-layer | **299 s** | 17.4 GiB | 8 |
+
+**Key validation:** on the BFW 10 m fixture — reproducing BFW's own run config
+exactly — the stock Python engine reproduces BFW's stored `zDelta` and
+`fpTravelAngleMax` **bit-for-bit** (0 differing pixels / 81,795 valid; max |Δ| = 0).
+This confirms the harness, config translation, and variable-parameter wiring are
+faithful, and gives a bit-exact target for the Numba engine.
+
+Benchmark target to beat: **299 s on 8 cores (BFW 10 m)**.
+
+## Phase 1 — Numba kernel — DONE (integration)
+
+New module `flowCoreNumba.py`: the per-release-pixel BFS as a single `@njit`
+function over flat arrays, plus `calculationNumba(args)` — a drop-in for
+`flowCore.calculation()` (same args, same 13-tuple). `run()` dispatches to it
+when `engine=numba` (default `python`); tiling / multiprocessing / merge / I-O
+unchanged. infra/back-calculation and previewMode fall back to the Python engine.
+
+Faithful to the `fluxDistOldVersion=False` path: float persistence (no int16
+truncation), g=9.81, forest friction/detrainment/frictionLayer with the
+not-start / FSI>0 / skipForestDist guards, forestInteraction counting, variable
+alpha/max_z/exponent per release pixel, default flux distribution
+(count = dist≥threshold, sub-threshold redistribution, conservation correction,
+deposition).
+
+## Phase 2 — numerical equivalence — IN PROGRESS
+
+A/B (numba vs Python engine), correctness fixtures — **bit-identical**:
+
+| Fixture | Outputs checked | Result |
+|---|---|---|
+| Connaught frequent | zDelta, routFluxSum, fpTravelAngleMax, cellCounts | 0 diff / 21,335 px, max\|Δ\|=0 |
+| Connaught extreme | same | 0 diff / 25,528 px, max\|Δ\|=0 |
+
+BFW 10 m (forestFriction + variableAlpha + variableUmax), numba vs Python baseline
+and vs BFW's own stored outputs:
+
+| Output | numba vs baseline | vs BFW stored |
+|---|---|---|
+| zDelta | 0 diff / 81,795 | **bit-identical** |
+| fpTravelAngleMax | 0 diff / 81,795 | **bit-identical** |
+| flux | 6 px (0.07 ppm), max\|Δ\|=2.2e-4 | n/a (not stored) |
+| cellCounts | 20 px (0.24 ppm), max\|Δ\|=1 | n/a (not stored) |
+
+The scientifically primary outputs (zDelta, travel angle) are bit-exact. The
+sub-ppm flux / cellCounts diffs are IEEE-754 operation-order artifacts (scalar
+kernel vs numpy vectorized ops): a child flux landing within ~1 ULP of the flux
+threshold is included by one engine and not the other, nudging cellCounts by 1
+and flux by a rounding-level amount at a handful of boundary pixels. This is the
+same class/scale of artifact documented for the standalone-FlowPy prototype
+(sub-110 ppm). It does not affect zDelta because those boundary cells take their
+zDelta from other paths.
+
+## Phase 3 — speedup & determinism — headline result
+
+BFW 10 m, matched core counts:
+
+| Domain (8 cores) | Python | Numba | Speedup |
+|---|---|---|---|
+| BFW 10 m | 302 s | 6.7 s | **45×** |
+| Connaught 5 m extreme | 5714 s (95 min) | 193 s | **30×** |
+
+On the 10 m domain numba runtime is **flat across cpu=4/8/16 (~6.5 s)** — small
+enough that it is no longer compute-bound (fixed costs: cold JIT compile + tiling
+I/O + merge dominate). At 5 m it is genuinely compute-bound (193 s). The realised
+speedup (~30–45× here) therefore depends on domain size and how compute-bound the
+run is. Because adding workers past ~8 gives little wall-clock gain (numba
+saturates the tile-parallelism early) but more memory, **~8 workers is the
+practical sweet spot for high-resolution runs**.
+
+**Determinism:** numba output is bit-identical across cpu=4/8/16 on all four
+outputs (zDelta, fpTravelAngleMax, flux, cellCounts — 0 differing pixels). The
+engine is fully deterministic w.r.t. worker count; the sub-ppm flux/cellCounts
+differences vs the Python engine are therefore a stable scalar-vs-numpy artifact,
+not a parallelism effect.
+
+Peak RAM (PSS) is on par with the Python engine and is dominated by com4FlowPy's
+multiprocessing, not the numba workspace (the per-BFS queue was nonetheless
+right-sized from `2·H·W` to a 128k grow-and-retry buffer — a correctness/hygiene
+fix, not a peak-RAM lever). A warm JIT cache removes the one-time compile cost.
+
+## Engine comparison for BFW
+
+com4FlowPy exposes two engines via `engine =`: `python` (the Cell-based
+reference) and `numba` (JIT, double precision). Default is `python`.
+
+BFW 10 m, 8 cores, vs the python reference:
+
+| Engine | Wall | Peak RAM | zDelta | fpTravelAngleMax | flux | cellCounts |
+|---|---|---|---|---|---|---|
+| python | 302 s | 7.1 GiB | ref | ref | ref | ref |
+| numba  | 6.7 s | 8.6 GiB | **0 diff** | **0 diff** | 6 px / 2e-4 | 20 px / 1 |
+
+(Wall/RAM on current master, 8 cores, PSS memory. diff = differing pixels / max\|Δ\|
+over 81,795 valid px; the sub-ppm flux/cellCounts differences are the documented
+scalar-vs-numpy artifact, and are bit-identical to BFW's own stored zDelta and
+fpTravelAngleMax.)
+
+**A single-precision (float32) variant was evaluated and rejected.** It was
+prototyped on the theory it would cut memory on high-resolution runs; measurement
+showed otherwise — no speedup (numba32 193 s vs numba 192 s at 5 m), no memory
+saving (24.9 vs 23.1 GiB PSS — if anything slightly higher, since peak RAM is
+dominated by com4FlowPy's multiprocessing, not the kernel workspace), and a
+precision cost that is *worst exactly at high resolution* (5 m: ~68 % of cells
+differ from the Python engine, zDelta up to 62 m). It offered no benefit where it
+was meant to help, so it is not part of this contribution.
+
+## 5 m (high-resolution, compute-bound) — Connaught extreme
+
+964×1025 ≈ 988k cells; the domain that ran ~1.5 h in autoATESv3.0. 8 cores, PSS memory:
+
+| Engine | Wall | Peak RAM |
+|---|---|---|
+| python | 5714 s (95 min) | 19.9 GiB |
+| numba  | 193 s | 23.1 GiB |
+
+→ **numba ~30× faster**, memory on par with the Python engine.
+
+* **Wall time is flat across cpu=4/8/16** — at 5 m the domain splits into only a
+  few tiles, so parallelism saturates early and the remainder is serial
+  (tiling / merge / JIT). More workers add memory, not speed — so physical cores
+  are the ceiling worth using (hyperthreads never help), and fewer is fine.
+* Peak RAM is dominated by com4FlowPy's multiprocessing (per-worker tile copies),
+  not the numba kernel workspace (a modest grow-and-retry queue). On
+  memory-constrained machines, fewer workers and/or a smaller `tileSize` lower
+  the peak. Memory is a non-issue at 10–25 m (small tiles).
+
+**Memory (measured with PSS — proportional set size — which correctly accounts
+for shared copy-on-write pages across the worker pool):** at BFW 10 m, python@8 =
+8.9 GiB, numba@8 = 8.6 GiB — numba is on par with (slightly below) the Python
+engine. At 5 m, numba@8 = 25 GiB. Earlier RSS-summed figures (e.g. 76 GiB at 5 m)
+over-counted shared memory ~2–3× and were misleading. The per-BFS queue workspace
+was separately reduced from `2·H·W` (~300× over the observed ~3.8k peak depth) to
+a modest 128k with grow-and-retry on overflow — this is a correctness/robustness
+fix (a path can never be silently truncated) rather than a peak-RAM lever, since
+the workspace was not the dominant allocation.
+
+### 5 m divergence — flat-terrain routing sensitivity (a model property, not an engine bug)
+
+The numba engine is deterministic (identical output run-to-run and across worker
+counts) and bit-for-bit identical to the Python engine on every domain tested at
+10–25 m (Connaught 21 m, BFW 10 m). On the finer 5 m run the two engines differ
+in a small set of cells: zDelta in 124 of 444k valid cells (0.03 %, worst case
+52 m at isolated points); routFluxSum and cellCounts in more cells (~20 % and
+~5 %) but by small per-cell amounts (routFluxSum ≤ 1.3, cellCounts ≤ 21).
+
+**Plain-English cause.** FlowPy routes flow downhill, splitting it at each cell
+among the downhill neighbours according to how steep the drop is. On a real slope
+there is a clear downhill and the path is well-defined. On a flat valley floor the
+neighbours sit within ~1 m of each other — there is no real "downhill," so which
+neighbour the flow follows comes down to the last rounding bit of the arithmetic.
+And because the model must keep routing until the energy line reaches the alpha
+angle (18° here), the flow keeps spreading across the flat with no genuine
+directional preference. The two engines evaluate the same formulas by a slightly
+different route (per-element scalar math vs numpy array math), so at these
+near-ties they occasionally send flow to a different — but equally valid —
+neighbour, after which the branches rejoin.
+
+**Neither engine is "more correct," and this is not a tiling artifact.** We
+reproduced the exact divergence inside a single isolated tile
+(`benchmark/repro_tile.py`), so it is not a tile-seam or merge effect — it is the
+fine 5 m grid resolving flat valley floors into many near-tie cells. To show the
+result is genuinely ill-conditioned *in the model itself* (independent of which
+engine computes it): nudging the DEM by a physically meaningless **±1 µm** — far
+below any real elevation accuracy — shifts the Python engine's *own* reached set
+by ~175 cells (±1 mm → 237; ±1 cm → 189). The numba engine is equally
+deterministic and equally valid; it simply lands on a different one of these
+near-tied branches. Crucially, the numba↔Python difference (~50 cells along a
+path) is *smaller* than the change the Python result shows under a sub-micron
+nudge. So at a flat-terrain fork the exact reached set is not a numerically
+determinate quantity for *any* correct implementation — both engines are valid
+IEEE-754 solutions of the same equations.
+
+**Impact, stated plainly.** The energy-line (zDelta) footprint is essentially
+identical (0.03 % of cells differ); the larger routFluxSum/cellCounts pixel counts
+are small in magnitude and concentrated at the margins of flow paths in flat
+runout zones. Any effect on downstream ATES classes is therefore confined to
+flat-terrain margins and expected to be minor — but we flag it rather than claim
+bit-identical output everywhere. This ambiguity is an inherent property of
+FlowPy's alpha-angle routing in flat terrain: the port neither introduces nor
+removes it, and the Python engine exhibits it too. A slope-aware stopping/
+friction criterion for low-gradient terrain would address the root cause and is
+noted as separate future work.
+
+## Phase 4 — upstream packaging — PENDING
+
+numba as an optional dependency with graceful fallback; config docs; commit the
+Connaught/BFW fixtures as a replicable regression test; PR to avaframe/AvaFrame.

avaframe/com4FlowPy/benchmark/fixtures/example.ini.template

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+# Example fixture for flowpy_bench.py — copy to <name>.ini and edit paths.
+# Concrete *.ini files are gitignored (they carry machine-specific data paths).
+[inputs]
+demPath      = /path/to/DEM.tif
+releasePath  = /path/to/release_or_pra_binary.tif
+forestPath   = /path/to/forest.tif        ; optional (required if forest=True)
+varAlphaPath = /path/to/alpha.tif          ; optional (required if variableAlpha=True)
+varUmaxPath  = /path/to/umax.tif           ; optional (required if variableUmaxLim=True)
+referenceDir = /path/to/reference_outputs  ; optional; compare outputs against these tifs
+
+[GENERAL]
+# any com4FlowPy [GENERAL] key overrides the module default
+alpha                   = 25
+exp                     = 8
+flux_threshold          = 3.0e-4
+max_z                   = 270
+forest                  = True
+forestModule            = forestFriction    ; forestFriction | forestDetrainment | forestFrictionLayer
+forestInteraction       = True
+variableAlpha           = False
+variableUmaxLim         = False
+tileSize                = 15000
+tileOverlap             = 5000
+outputFiles             = zDelta|fpTravelAngleMax|cellCounts|flux