Merge pull request #641 from AdaWorldAPI/claude/v3-substrate-migration-review-o0yoxv

onebrc/lane-j: wire GridBatch through ndarray MultiLaneColumn (D-DNV-1)

Author: AdaWorldAPI

.claude/board/STATUS_BOARD.md

@@ -1,3 +1,14 @@
+## deepnsm-v3-convergence-v1 — DeepNSM is the encoder that fills reserved tenants
+
+Plan: `.claude/plans/deepnsm-v3-convergence-v1.md` (`E-V3-DEEPNSM-IS-THE-ENCODER-NOT-A-MIGRATION-1`). Static convergence PROVEN by #624 P0–P5; the memory layer is the genuinely-unbuilt seam. Extends `v3-convergence-wiring-v1` (wire-don't-invent).
+
+| D-id | Title | Crate(s) | Status | Evidence |
+|---|---|---|---|---|
+| D-DNV-1 | Gridlake carrier: `GridBatch::as_gridlake_columns` → `ndarray::simd::MultiLaneColumn` (i32 min/max, i64 sum, u64 count); the carrier the COCA `Cell` also rides | onebrc-probe (+ndarray) | In PR | lane-j feature pulls ndarray; 2 tests green (LE roundtrip cell-for-cell + unaligned-grid reject); lane_j.rs clippy-clean |
+| D-DNV-2 | deepnsm `SpoTriple` → `CausalEdge64` S/P/O+freq/conf → `MaterializedEdges`; run `nars_engine.all_projections()` (2³) over the COCA distance matrix | deepnsm + planner | Queued | buildable; extends #624 P3b |
+| D-DNV-3 | arm-discovery as the 2nd proposer leg into one SpoStore (shares palette256 oracle) | arm-discovery + deepnsm | Blocked (ARM-JIRAK-FLOOR) | D-ARM-7 Jirak noise floor is the hard prereq |
+| D-DNV-4 | Episodic-witness tenant + `basin=family` wake (`witness_tombstone` calcify chain) | contract + arigraph | Blocked (own wave + probe) | no episodic-witness ValueTenant; calcify chain is `todo!()`; basin=family doc-only |
+
 ## v3-substrate-integration-v1 — the .claude/v3/ consolidation (W0–W6)
 
 Plan: `.claude/v3/INTEGRATION-PLAN.md` (stub: `.claude/plans/v3-substrate-integration-v1.md`). Adopts (does not re-mint) D-MBX-A6, D-PERT-1, D-CC-*, D-VCW-3/5/7, D-CCF-4.

crates/onebrc-probe/Cargo.toml

@@ -43,8 +43,10 @@ lane-h = ["lane-g"]
 # flush-cache interleaving - same dep set as lane-g.
 lane-i = ["dep:lance-graph-contract", "dep:ractor", "dep:tokio"]
 # Lane J (parameterized batch pipeline): lane I's shape with grid /
-# sink-lanes / registry knobs — needs lane-i's RowOwner.
-lane-j = ["lane-i"]
+# sink-lanes / registry knobs — needs lane-i's RowOwner. Also pulls
+# ndarray for the gridlake carrier (`GridBatch::as_gridlake_columns` →
+# `ndarray::simd::MultiLaneColumn`; DeepNSM→V3 D-DNV-1).
+lane-j = ["lane-i", "dep:ndarray"]
 # All 8 batching-method presets (src/presets.rs) — the lab-sweep surface;
 # see FINDINGS.md (agnostic record) + COMMENTARY.md (interpretation).
 presets = ["lane-g", "lane-h", "lane-i", "lane-j"]

crates/onebrc-probe/src/lane_j.rs

@@ -43,6 +43,7 @@ use crate::lane_f::{fnv1a64, morton_slot};
 use crate::lane_i::RowOwner;
 use crate::{chunk_bounds, merge_maps, parse_temp_tenths, Stats};
 use lance_graph_contract::kanban::{ExecTarget, KanbanColumn, KanbanMove};
+use ndarray::simd::MultiLaneColumn;
 use ractor::{Actor, ActorProcessingErr, ActorRef, RpcReplyPort};
 use std::collections::{BTreeMap, VecDeque};
 use std::sync::atomic::{AtomicUsize, Ordering};
@@ -133,7 +134,7 @@ impl GridMemo {
 
 // ─── Grid batch table (the gridlake SoA unit at grid=4096: ~80 KB) ──────
 
-pub(crate) struct GridBatch {
+pub struct GridBatch {
     mins: Vec<i32>,
     maxs: Vec<i32>,
     sums: Vec<i64>,
@@ -180,6 +181,70 @@ impl GridBatch {
     }
 }
 
+// ─── Gridlake carrier: the batch table AS ndarray MultiLaneColumns ──────
+
+/// The lane-J `GridBatch` accumulators rendered as `ndarray::simd`
+/// [`MultiLaneColumn`] gridlake carriers — the SoA-contract carrier the
+/// proven 64×64 gridlake tile rides (`E-1BRC-GRIDLAKE-SWEETSPOT-1`), and the
+/// **same** `MultiLaneColumn` the COCA cognitive `Cell`
+/// (helix48/campq48/count/truth, `crates/deepnsm/examples/gridlake_coca_wire.rs`)
+/// composes from. This is the DeepNSM→V3 D-DNV-1 recognition
+/// (`.claude/plans/deepnsm-v3-convergence-v1.md`,
+/// `E-V3-DEEPNSM-IS-THE-ENCODER-NOT-A-MIGRATION-1`): the batch table is not a
+/// bespoke struct, it is typed lanes over one carrier — "wire, don't invent."
+///
+/// Lane widths follow the integer lanes ndarray added for exactly this
+/// (`iter_i32x16` "min/max tile columns", `iter_i64x8` "running sums"):
+/// min/max ride `I32x16`, sum rides `I64x8`, count (a non-negative
+/// accumulator) rides `U64x8`. Each column's backing buffer is a 64-byte
+/// multiple whenever `grid` is a multiple of 16 (i32·16 = i64·8 = u64·8 =
+/// 64 B), which the gridlake `grid = 4096` satisfies.
+pub struct GridlakeColumns {
+    pub mins: MultiLaneColumn,
+    pub maxs: MultiLaneColumn,
+    pub sums: MultiLaneColumn,
+    pub counts: MultiLaneColumn,
+}
+
+impl GridBatch {
+    /// Render the four accumulator columns as [`MultiLaneColumn`] gridlake
+    /// carriers (little-endian bytes, zero semantic change — a *reading*, not
+    /// a re-layout). `count` is widened `u32 → u64` to ride the unsigned
+    /// 64-bit accumulator lane. Returns `Err(())` if a column buffer is not
+    /// 64-byte aligned (i.e. `grid % 16 != 0`), mirroring
+    /// `MultiLaneColumn::new`'s own contract.
+    #[allow(clippy::result_unit_err)] // pass-through of MultiLaneColumn::new's Result<_, ()> alignment contract
+    pub fn as_gridlake_columns(&self) -> Result<GridlakeColumns, ()> {
+        fn col_i32(v: &[i32]) -> Result<MultiLaneColumn, ()> {
+            let mut b = Vec::with_capacity(v.len() * 4);
+            for &x in v {
+                b.extend_from_slice(&x.to_le_bytes());
+            }
+            MultiLaneColumn::new(Arc::from(b))
+        }
+        fn col_i64(v: &[i64]) -> Result<MultiLaneColumn, ()> {
+            let mut b = Vec::with_capacity(v.len() * 8);
+            for &x in v {
+                b.extend_from_slice(&x.to_le_bytes());
+            }
+            MultiLaneColumn::new(Arc::from(b))
+        }
+        fn col_u64_from_u32(v: &[u32]) -> Result<MultiLaneColumn, ()> {
+            let mut b = Vec::with_capacity(v.len() * 8);
+            for &x in v {
+                b.extend_from_slice(&(x as u64).to_le_bytes());
+            }
+            MultiLaneColumn::new(Arc::from(b))
+        }
+        Ok(GridlakeColumns {
+            mins: col_i32(&self.mins)?,
+            maxs: col_i32(&self.maxs)?,
+            sums: col_i64(&self.sums)?,
+            counts: col_u64_from_u32(&self.counts)?,
+        })
+    }
+}
+
 // ─── Laned sinks: each lane owns a contiguous row-range slice ───────────
 
 enum LaneMsg {
@@ -594,6 +659,74 @@ pub fn lane_j_grid_pipeline(data: &[u8], workers: usize) -> BTreeMap<String, Sta
 mod tests {
     use super::*;
 
+    /// D-DNV-1 carrier foundation: the gridlake `GridBatch` renders
+    /// losslessly through `ndarray::simd::MultiLaneColumn` — the LE bytes
+    /// roundtrip cell-for-cell against the source accumulators, and the typed
+    /// integer lanes (i32 min/max, i64 sum, u64 count) each yield the right
+    /// window count over the carrier. This is the "wire, don't invent" proof
+    /// that the batch table IS a MultiLaneColumn composition (the carrier the
+    /// COCA cognitive Cell also rides).
+    #[test]
+    fn gridlake_batch_rides_multilane_column_losslessly() {
+        let grid = 4096usize;
+        let mut batch = GridBatch::new(grid);
+        // A spread of cells with distinct signed extremes + running sums,
+        // incl. the i32x16 / lane boundaries (15|16) and the tile edge (4095).
+        for (k, &slot) in [0u16, 1, 15, 16, 255, 256, 4095].iter().enumerate() {
+            let k = k as i32;
+            batch.observe(slot, -(k * 10) - 3);
+            batch.observe(slot, k * 7 + 11);
+            batch.observe(slot, 5);
+        }
+        let cols = batch
+            .as_gridlake_columns()
+            .expect("grid=4096 columns are 64-byte aligned");
+
+        // Typed-lane views are wired: 4096 i32 = 256 × i32x16;
+        // 4096 i64/u64 = 512 × i64x8/u64x8 (the lanes ndarray added for this).
+        assert_eq!(cols.mins.len_i32x16(), grid / 16);
+        assert_eq!(cols.mins.iter_i32x16().count(), grid / 16);
+        assert_eq!(cols.maxs.len_i32x16(), grid / 16);
+        assert_eq!(cols.sums.len_i64x8(), grid / 8);
+        assert_eq!(cols.sums.iter_i64x8().count(), grid / 8);
+        assert_eq!(cols.counts.len_u64x8(), grid / 8);
+        assert_eq!(cols.counts.iter_u64x8().count(), grid / 8);
+
+        // LE roundtrip is cell-for-cell exact against the source accumulators.
+        let dec_i32 = |c: &MultiLaneColumn| -> Vec<i32> {
+            c.as_bytes()
+                .chunks_exact(4)
+                .map(|b| i32::from_le_bytes(b.try_into().expect("4-byte i32 chunk")))
+                .collect()
+        };
+        let dec_i64 = |c: &MultiLaneColumn| -> Vec<i64> {
+            c.as_bytes()
+                .chunks_exact(8)
+                .map(|b| i64::from_le_bytes(b.try_into().expect("8-byte i64 chunk")))
+                .collect()
+        };
+        let dec_u64 = |c: &MultiLaneColumn| -> Vec<u64> {
+            c.as_bytes()
+                .chunks_exact(8)
+                .map(|b| u64::from_le_bytes(b.try_into().expect("8-byte u64 chunk")))
+                .collect()
+        };
+        assert_eq!(dec_i32(&cols.mins), batch.mins);
+        assert_eq!(dec_i32(&cols.maxs), batch.maxs);
+        assert_eq!(dec_i64(&cols.sums), batch.sums);
+        let counts_u64: Vec<u64> = batch.counts.iter().map(|&c| c as u64).collect();
+        assert_eq!(dec_u64(&cols.counts), counts_u64);
+    }
+
+    /// The carrier refuses a mis-aligned grid (not a multiple of 16) rather
+    /// than silently producing a non-64-byte column — the `MultiLaneColumn`
+    /// contract surfaced at the batch boundary.
+    #[test]
+    fn gridlake_carrier_rejects_unaligned_grid() {
+        let batch = GridBatch::new(72); // 72 % 16 != 0 → i32 col = 288 B, not 64-mult
+        assert!(batch.as_gridlake_columns().is_err());
+    }
+
     /// Parity across the knob matrix corners: gridlake (4096) and full
     /// (65536) grids × 1 and 8 sink lanes × registry on/off, all with a
     /// small batch to force multi-batch flush-cache recycling.