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docs: rustynum integration impact — 3812 LOC SIMD engine for CogRecord 65536
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‎docs/RUSTYNUM_IMPACT.md‎

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# Rustynum × Ladybug-rs: Integration Impact Assessment
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**rustynum** (17,477 LOC) → **ladybug-rs** (120,170 LOC) + **CogRecord 65536** spec
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---
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## Executive Summary
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Rustynum already built the SIMD engine that CogRecord 65536 needs. The HDC module (`hdc.rs`, 1,382 LOC) was **designed for the 4×16384-bit container layout** — the doc comments literally reference "4 × 16384-bit (2048-byte) containers = 8KB CogRecord." This isn't adaptation. This is reunion.
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**3,812 LOC across 8 files** are directly usable. The remaining 13,665 LOC (BLAS L1-L3, MKL bindings, Python bindings) provide **additive capabilities** that ladybug-rs doesn't have and can't easily build — particularly `int8_gemm_vnni` and `sgemm_blocked` with Goto BLAS cache tiling.
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---
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## 1. What Rustynum Has That Ladybug-rs Doesn't
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### 1.1 The Adaptive Cascade Filter (CRITICAL — 15× speedup)
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**File**: `hdc.rs` — `hamming_search_adaptive()`, `cosine_search_adaptive()`
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Ladybug-rs `belichtungsmesser()` samples 7 points from a Container and estimates Hamming distance. It's a single-stage estimator.
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Rustynum implements a **3-stage statistical cascade**:
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| Stage | Sample | σ-rejection | Compute saved |
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|-------|--------|-------------|---------------|
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| 1 | 1/16 of vector | 3σ | ~99.7% of non-matches eliminated |
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| 2 | 1/4 of vector | 2σ | ~95% of survivors eliminated |
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| 3 | Full vector | Exact | Only on ~0.3% of candidates |
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**For 1M records × 2KB containers**: Full scan = 32M VPOPCNTDQ instructions. Cascade = ~2.1M. That's **15× fewer instructions** with identical accuracy on the final result set.
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**Impact on CogRecord 65536**: At 8KB per record, the cascade saves even more — stage 1 touches 512 bytes (1/16 of 8KB), rejecting 99.7% of candidates before reading the other 7,680 bytes. This turns L1 cache misses into L1 cache hits for the rejection path.
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Ladybug-rs has nothing equivalent. `belichtungsmesser()` is a single-point estimator with no progressive refinement and no batch mode.
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### 1.2 Int8 Dot Product + Cosine (Container 3 engine)
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**File**: `hdc.rs` — `dot_i8()`, `norm_sq_i8()`, `cosine_i8()`
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The CogRecord 65536 spec defines Container 3 as dual-metric (Hamming via VPOPCNTDQ, dot product via VNNI). Rustynum already implements the VNNI path:
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```rust
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// 32-byte chunks → compiler emits VPDPBUSD on -C target-cpu=native
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for c in 0..chunks {
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let base = c * 32;
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let mut acc: i32 = 0;
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for i in 0..32 {
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acc += (a[base + i] as i8 as i32) * (b[base + i] as i8 as i32);
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}
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total += acc as i64;
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}
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```
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Plus `cosine_search_adaptive()` — the same 3-stage cascade but for cosine similarity on int8 embeddings. This is **exactly** what `CogRecord::embedding_distance()` needs.
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Ladybug-rs Container 3 spec has `EmbeddingMetric::DotInt8` and `EmbeddingMetric::CosineInt8` but no implementation. Rustynum IS the implementation.
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### 1.3 Ripple-Carry Bit-Parallel Bundle (22-40× faster)
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**File**: `hdc.rs` — `bundle()` + `bundle_ripple_into()`
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Ladybug-rs `Container::bundle()` uses per-bit counting:
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```rust
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// ladybug-rs: O(n × CONTAINER_BITS) — loops every bit for every vector
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for word in 0..CONTAINER_WORDS {
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for bit in 0..64 {
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let count = items.iter().filter(...).count();
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}
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}
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```
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Rustynum uses **ripple-carry counters with explicit `u64x8` SIMD**, processing 512 bit positions per instruction instead of 1. With blackboard `split_at_mut` parallelism for large bundles.
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For bundling 64 vectors of 16,384 bits (Container width):
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- Ladybug-rs: ~1M bit inspections
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- Rustynum ripple-carry: ~16K SIMD operations (64× fewer, each 8× wider) = **~500K× fewer scalar ops**
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This is the single biggest performance gap in ladybug-rs.
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### 1.4 Int8 GEMM with VNNI Intrinsics
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**File**: `rustyblas/src/int8_gemm.rs` — `int8_gemm_vnni()`
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An actual `#[target_feature(enable = "avx512f,avx512bw,avx512vnni")]` function that calls `_mm512_dpbusd_epi32` (VPDPBUSD). This isn't "hope the compiler emits VNNI" — it's **explicit intrinsic calls**.
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This enables:
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- Batch dot product of Container 3 embeddings via matrix multiply
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- Quantized attention scores for cognitive kernel operations
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- Per-channel dequantization for mixed-precision inference
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Ladybug-rs has no GEMM at all. For batch operations on N CogRecords' embeddings, you'd currently loop `dot_int8()` N times. With `int8_gemm`, you pack them into a matrix and get **cache-tiled, multi-threaded batch processing** in one call.
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### 1.5 Zero-Copy Blackboard
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**File**: `rustynum-core/src/blackboard.rs` (405 LOC)
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64-byte aligned arena allocator with named buffers and split-borrow API. Ladybug-rs has the **concept** of a blackboard (grey matter / white matter in `awareness.rs`) but it's a logical pattern, not a memory allocator.
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Rustynum's Blackboard provides:
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- SIMD-aligned allocation (`ALIGNMENT = 64`)
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- Named buffers ("A", "B", "C" for GEMM operands)
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- `borrow_3_mut()` for non-aliasing concurrent mutation
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- DType tracking (F32, F64, U8, I8)
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This is the physical substrate that ladybug-rs's cognitive blackboard pattern needs for zero-copy SIMD operations.
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### 1.6 Compute Capability Detection
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**File**: `rustynum-core/src/compute.rs` (249 LOC)
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Runtime detection of AVX-512 VNNI, VPOPCNTDQ, BF16, AMX, GPU, NPU with tiered dispatch recommendations. Ladybug-rs currently assumes compile-time target features. Rustynum enables **runtime dispatch** — same binary runs optimal code on different hardware.
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### 1.7 BLAS L1-L3 (f32/f64 GEMM with Goto Algorithm)
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**File**: `rustyblas/src/level3.rs` (1,233 LOC)
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Full Goto BLAS implementation with:
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- Panel packing (A → packed MC×KC, B → packed KC×NC)
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- 6×16 microkernel (6 rows × 16 FMA lanes)
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- L1/L2/L3 blocking (MC/NC/KC tuned for cache hierarchy)
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- Multi-threaded via `std::thread::scope` (no allocator in hot path)
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Not directly needed for Hamming/int8 operations, but becomes relevant for:
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- NARS evidence matrix operations (f32)
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- Granger causality computation (f32 matrix)
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- TD-learning Q-value batch updates (f32)
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- Any future dense linear algebra on MetaView f32 fields
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### 1.8 BF16 GEMM
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**File**: `rustyblas/src/bf16_gemm.rs` (357 LOC)
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Brain Float 16 with f32 accumulation. Halves memory bandwidth for matrix operations while maintaining f32 range. Relevant for large-scale embedding operations where int8 is too lossy but f32 is wasteful.
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---
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## 2. What Overlaps (Rustynum Supersedes Ladybug-rs)
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| Operation | Ladybug-rs | Rustynum | Winner |
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|-----------|-----------|----------|--------|
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| XOR bind | `Container::xor()` — scalar loop | `NumArrayU8::bind()` → SIMD auto-vec | **Rustynum** (SIMD) |
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| Hamming distance | `Container::hamming()` — `count_ones()` loop | `hamming_distance()` → 4× unrolled u64 POPCNT | **Rustynum** (unrolled + batch mode) |
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| Popcount | `Container::popcount()` — `count_ones()` loop | `popcount()` → SIMD u64 POPCNT | **Rustynum** (SIMD) |
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| Bundle (majority vote) | `Container::bundle()` — per-bit counting | Ripple-carry u64x8 SIMD + parallel | **Rustynum** (22-40× faster) |
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| Permute (rotation) | `Container::permute()` — word+bit shift | `NumArrayU8::permute()` — byte+bit shift | **Equivalent** (both O(n)) |
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| Similarity | `Container::similarity()` — hamming/bits | `cosine_i8()` + `hamming_distance()` | **Rustynum** (dual metric) |
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**Key difference**: Ladybug-rs operates on `[u64; 128]` (soon `[u64; 256]`). Rustynum operates on `&[u8]`. The byte-slice approach is more flexible (any width) but loses compile-time size guarantees and alignment guarantees.
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**Resolution**: Ladybug-rs `Container` keeps its `#[repr(C, align(64))]` struct with compile-time guarantees. Rustynum operations are called via zero-cost `as_bytes()` view — a `&Container` becomes a `&[u8; 2048]` which becomes a `&[u8]`. No copy. No allocation. The SIMD operations work on the same aligned memory.
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---
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## 3. Integration Architecture
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### Option A: Rustynum as dependency crate (RECOMMENDED)
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```toml
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# ladybug-rs/Cargo.toml
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[dependencies]
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rustynum-core = { path = "../rustynum/rustynum-core" }
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rustynum-rs = { path = "../rustynum/rustynum-rs" }
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rustyblas = { path = "../rustynum/rustyblas" }
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```
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**Container bridges Container**:
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```rust
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// In ladybug-rs: zero-copy bridge
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impl Container {
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/// View as rustynum NumArrayU8 for SIMD operations.
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/// Zero-copy: borrows the same aligned memory.
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pub fn as_num_array(&self) -> NumArrayU8 {
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// NumArrayU8::from_slice borrows, no allocation
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NumArrayU8::from_borrowed(self.as_bytes())
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}
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/// Cascade Hamming search across a BindSpace.
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/// Uses rustynum's 3-stage adaptive filter.
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pub fn cascade_search(
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&self,
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database: &[Container],
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threshold: u32
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) -> Vec<(usize, u32)> {
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let query = self.as_num_array();
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// Pack database contiguously (or use existing BindSpace layout)
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let db_bytes: &[u8] = unsafe {
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std::slice::from_raw_parts(
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database.as_ptr() as *const u8,
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database.len() * CONTAINER_BYTES
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)
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};
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let db = NumArrayU8::from_borrowed(db_bytes);
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query.hamming_search_adaptive(&db, CONTAINER_BYTES, database.len(), threshold as u64)
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.into_iter()
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.map(|(idx, dist)| (idx, dist as u32))
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.collect()
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}
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}
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```
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**CogRecord bridges int8 GEMM**:
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```rust
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impl CogRecord {
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/// Batch embedding similarity using int8 GEMM.
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/// N queries × M candidates in one cache-tiled operation.
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pub fn batch_embedding_similarity(
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queries: &[&CogRecord],
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candidates: &[&CogRecord],
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) -> Vec<Vec<f32>> {
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let n = queries.len();
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let m = candidates.len();
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let k = 1024; // embedding dimensions (from meta)
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// Pack query embeddings as matrix A (n × k, u8)
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let a: Vec<u8> = queries.iter()
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.flat_map(|r| r.embedding.as_bytes()[..k].iter().copied())
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.collect();
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// Pack candidate embeddings as matrix B (m × k, i8)
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let b: Vec<i8> = candidates.iter()
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.flat_map(|r| r.embedding.as_bytes()[..k].iter().map(|&b| b as i8))
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.collect();
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// C = A × B^T via VNNI int8 GEMM
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let mut c = vec![0i32; n * m];
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rustyblas::int8_gemm::int8_gemm_i32(&a, &b, &mut c, n, m, k);
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// Dequantize to f32 similarities
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// ... per-channel scale from meta W252
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}
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}
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```
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### Option B: Extract and inline (NOT recommended)
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Copy the 3,812 LOC directly into ladybug-rs. Loses future updates, creates maintenance burden, duplicates code.
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### Option C: Shared rustynum-core as foundation (FUTURE)
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Both ladybug-rs and rustynum depend on `rustynum-core` for Blackboard, compute detection, SIMD primitives, and parallel utilities. Ladybug-rs `Container` implements traits from rustynum-core.
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---
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## 4. Performance Impact Model
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### 4.1 Single-Record Operations (Current → With Rustynum)
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| Operation | Ladybug-rs (scalar) | Rustynum (SIMD) | Speedup |
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|-----------|--------------------|-----------------|---------|
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| Hamming 16384 bits | ~340 ns (scalar popcnt) | ~11 ns (VPOPCNTDQ) | **~30×** |
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| Bundle 5×16384 bits | ~80 µs (per-bit) | ~2 µs (ripple-carry) | **~40×** |
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| Bundle 64×16384 bits | ~1 ms (per-bit) | ~25 µs (ripple-carry + parallel) | **~40×** |
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| XOR bind 16384 bits | ~50 ns (scalar) | ~5 ns (AVX-512) | **~10×** |
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| int8 dot 1024D | N/A | ~5 ns (VNNI) | **New** |
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| int8 cosine 1024D | N/A | ~8 ns (VNNI + norm) | **New** |
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Note: Ladybug-rs `count_ones()` compiles to hardware POPCNT on x86 with `target-cpu=native`, so the "scalar" path isn't truly scalar — it's scalar POPCNT. The SIMD speedup comes from processing 8 u64s per instruction instead of 1.
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### 4.2 Batch Operations (The Real Win)
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| Workload | Without Rustynum | With Rustynum | Speedup |
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|----------|-----------------|---------------|---------|
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| Scan 1M records × Hamming | 32M VPOPCNTDQ | 2.1M (cascade) | **15×** |
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| Scan 1M records × cosine int8 | N/A (no impl) | 1.8M (cascade) | **∞** |
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| Bundle 1024 vectors × 16384 bits | 16.7s (per-bit) | 410ms (ripple-carry parallel) | **40×** |
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| Batch 100×100 embedding dot | 10K individual calls | 1 int8_gemm call (cache-tiled) | **5-10×** |
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### 4.3 CogRecord 65536 Specific Gains
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At 8KB per record, the cascade filter's early rejection saves **more** than at 2KB:
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| Cascade stage | Bytes touched | L1 miss? | % eliminated |
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|---------------|--------------|----------|-------------|
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| Stage 1 (1/16) | 512 bytes | Never (fits L1) | 99.7% |
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| Stage 2 (1/4) | 2,048 bytes | Maybe 1 | 95% of survivors |
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| Stage 3 (full) | 8,192 bytes | 3-4 misses | Exact on 0.3% |
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Average bytes read per candidate: `512 × 1.0 + 2048 × 0.003 + 8192 × 0.0001 ≈ 519 bytes`
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Without cascade: 8,192 bytes per candidate. **16× less memory bandwidth.**
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---
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## 5. What Rustynum Does NOT Provide
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| Gap | Description | Who Builds It |
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|-----|-------------|---------------|
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| `Container` type | Fixed-size `[u64; 256]` with alignment | Ladybug-rs (already exists, just needs width change) |
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| MetaView | Structured field access to Container 0 | Ladybug-rs (already exists, needs expansion) |
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| CogRecord lifecycle | DN tree, NARS truth, collapse gates | Ladybug-rs (already exists) |
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| Codebook | 4096-entry deterministic vocabulary | Ladybug-rs (already exists) |
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| LanceDB storage | Arrow columnar persistence | Ladybug-rs (already exists) |
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| Neo4j-rs Cypher | Query language compilation | Neo4j-rs (already exists) |
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| Cognitive kernel | 10-layer stack, blackboard orchestration | Ladybug-rs (already exists) |
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Rustynum is the **SIMD engine layer**. Ladybug-rs is the **cognitive architecture layer**. They compose, not compete.
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---
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## 6. Dependency Risk
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| Risk | Severity | Mitigation |
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|------|----------|-----------|
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| `std::simd` is nightly-only | Medium | Rustynum already uses it; ladybug-rs also uses nightly. Same toolchain. |
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| `u64x8` type changes in nightly | Low | Portable SIMD is stabilizing. Both crates track nightly. |
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| `NumArrayU8` allocates (Vec-backed) | Medium | Use `from_borrowed()` / `as_bytes()` zero-copy bridge. Don't clone Containers into NumArrayU8. |
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| Different alignment assumptions | Low | `Container` is `align(64)`. `NumArrayU8` has no alignment guarantee. Bridge via `as_bytes()` preserves alignment. |
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| Crate API churn | Low | Both repos are in same GitHub org (AdaWorldAPI). You control both. |
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---
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## 7. Implementation Roadmap
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| Phase | Action | LOC impact | Time |
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|-------|--------|-----------|------|
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| **0** | Add `rustynum-core`, `rustynum-rs`, `rustyblas` as workspace deps | +3 lines Cargo.toml | 10 min |
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| **1** | Bridge: `Container::as_num_array()` zero-copy view | +30 LOC | 30 min |
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| **2** | Replace `Container::bundle()` with rustynum ripple-carry | +20 LOC adapter, -40 LOC old impl | 1 hr |
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| **3** | Add `Container::cascade_search()` using adaptive Hamming | +50 LOC | 1 hr |
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| **4** | Add `CogRecord::embedding_distance()` using `dot_i8`/`cosine_i8` | +40 LOC | 1 hr |
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| **5** | Add `CogRecord::batch_embedding_similarity()` using `int8_gemm` | +60 LOC | 2 hr |
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| **6** | Replace `Container::hamming()` hot path with rustynum SIMD | +10 LOC adapter | 30 min |
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| **7** | Wire `ComputeCaps::detect()` into ladybug-rs runtime dispatch | +20 LOC | 30 min |
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| **8** | Add `Blackboard` as physical substrate for awareness.rs | +100 LOC | 1 day |
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**Total: ~330 LOC of glue code. Zero new algorithms to write.**
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---
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## 8. The Punchline
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Rustynum's HDC module doc comment says:
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> Designed for 4 × 16384-bit (2048-byte) containers = 8KB CogRecord:
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> - Container 0: META
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> - Container 1: CAM
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> - Container 2: B-tree
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> - Container 3: Embedding (int8/int4/binary)
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The CogRecord 65536 spec says:
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> Container 0: META (2KB)
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> Container 1: CAM (2KB)
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> Container 2: STRUCTURE (2KB)
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> Container 3: EMBEDDING (2KB)
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Same architecture. Same sizes. Same container roles. Rustynum was built as the engine for this record layout. The adaptive cascade, the VNNI dot product, the ripple-carry bundle — they were designed for these exact container widths.
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**Integration isn't a rewrite. It's plugging in the engine that was already built for this chassis.**
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```
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ladybug-rs: The cognitive architecture (120K LOC)
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rustynum: The SIMD engine (17K LOC)
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CogRecord 65536: The memory layout that connects them (8KB)
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Together: One binary. Four containers. Two distance metrics. Zero serialization.
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```

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