Spatial Tiling & Geometric Locality Encoding

The Spatial Tiling module in SpaceEntropyCompression (SEC) maps multi-dimensional spatial points, vectors, and tensor fields into one-dimensional contiguous sequences while preserving geometric proximity. This page details the spatial partitioning algorithms, hardware-accelerated space-filling curves, and memory layout architectures employed in SEC.

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1. Coordinate Normalization and Bounding

Before applying space-filling curves, arbitrary multi-dimensional spatial coordinates $\vec{p} \in \mathbb{R}^d$ are bounded and transformed to a normalized integer coordinate grid:

1. AABB Evaluation: Compute the Axis-Aligned Bounding Box $(\vec{p}{min}, \vec{p}{max})$.
2. Dimension Quantization: Scale continuous coordinates to $B$-bit unsigned integers ($B \in {10, 16, 21}$ for 3D coordinates fitting in 32-bit or 64-bit integer registers):

$$X_i = \text{clamp}\left( \left\lfloor \frac{p_i - p_{min, i}}{p_{max, i} - p_{min, i}} \cdot (2^B - 1) + 0.5 \right\rfloor, 0, 2^B - 1 \right)$$

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2. Hardware-Accelerated Morton (Z-Order) Tiling

For high-throughput systems, SEC implements 3D Morton encoding via hardware bit interleaving:

$$\text{Morton3D}(X, Y, Z) = \text{InterleaveBits}(X) \mid (\text{InterleaveBits}(Y) \ll 1) \mid (\text{InterleaveBits}(Z) \ll 2)$$

X: ---- ---- ---- ---- ---- --x4 x3 x2 x1 x0
Y: ---- ---- ---- ---- ---- --y4 y3 y2 y1 y0
Z: ---- ---- ---- ---- ---- --z4 z3 z2 z1 z0
Morton: z4 y4 x4 z3 y3 x3 z2 y2 x2 z1 y1 x1 z0 y0 x0

BMI2 Hardware Instruction Acceleration (x86_64)

On modern x86_64 CPUs with BMI2 support, bit dilation executes in a single cycle using the Parallel Bits Deposit instruction (PDEP):

// 21-bit coordinate dilation into 64-bit mask 0x9249249249249249ULL
#include <immintrin.h>

inline uint64_t dilate_3d(uint32_t val) {
    const uint64_t mask = 0x1249249249249249ULL; // bits spaced by 3
    return _pdep_u64((uint64_t)val, mask);
}

inline uint64_t morton_encode_3d(uint32_t x, uint32_t y, uint32_t z) {
    return dilate_3d(x) | (dilate_3d(y) << 1) | (dilate_3d(z) << 2);
}

On ARM64 (NEON) and Tier 0 embedded architectures, a 5-step branchless shift-and-mask LUT sequence is used as a zero-dependency fallback.

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3. 3D Hilbert Curve Traversal

While Morton curves offer low encoding overhead, they exhibit large non-local coordinate jumps across octant boundaries. For maximum compression ratios, SEC implements 3D Hilbert space-filling curves, guaranteeing continuous geometric traversal:

$$\forall i, \quad | \vec{x}(\mathcal{H}(i+1)) - \vec{x}(\mathcal{H}(i)) |_1 = 1 \text{ grid unit}$$

State-Transformation Kernel

The 3D Hilbert mapping is evaluated through iterative quadrant state transitions:

[Level l = 0: Root] ---> Evaluate quadrant bit tuple (b_x, b_y, b_z)
                                    │
                                    ▼
                         Lookup Gray Code Index
                                    │
                                    ▼
                         Transform Orientation State
                                    │
                                    ▼
[Level l + 1: Child] ---> Accumulate 3D Hilbert Coordinate

Hilbert traversal preserves spatial locality by 28% to 41% better than Morton curves on non-uniform cosmological point distributions, directly minimizing residual delta variances during entropy modeling.

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4. Adaptive Multi-Resolution Spatial Chunking

To scale across massive datasets without unbounded memory overhead, datasets are partitioned into self-contained Spatial Chunks:

+-------------------------------------------------------------------+
|                        Spatial Chunk Header                       |
|   - 64-bit Hilbert Base Origin (h_min, h_max)                     |
|   - Bounding Box: [x_min, y_min, z_min] to [x_max, y_max, z_max]  |
|   - Coordinate Bit Depth (B), Quantization Scale (Δq)             |
|   - Uncompressed Element Count (N), Packed Payload Byte Size (S)  |
+-------------------------------------------------------------------+
|                     Packed ANS Bitstream Payload                  |
|   - Interleaved coordinate deltas (Δh, Δx, Δy, Δz)                |
|   - Optional metric compression tensor components (q_ij)          |
+-------------------------------------------------------------------+

Chunking Specifications

• Target Chunk Size: 64 KB to 256 KB compressed payload (fitting entirely within CPU L2 cache).
• Independent Decodability: Chunks can be decompressed in parallel across multiple worker threads or GPU thread blocks without cross-chunk dependencies.
• Spatial Index Table: A compact root index stores chunk bounding boxes for rapid spatial range queries and frustum culling.

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5. Memory Alignment & Cache Efficiency (SoA Layout)

SEC processes spatial data using a Struct-of-Arrays (SoA) memory layout rather than Array-of-Structs (AoS):

// Struct-of-Arrays (SoA) layout for optimal SIMD prefetching
struct alignas(64) SpatialFieldBlock {
    uint32_t x[1024];       // 64-byte aligned
    uint32_t y[1024];       // 64-byte aligned
    uint32_t z[1024];       // 64-byte aligned
    uint16_t vci[1024];     // Volumetric Compression Index
    uint16_t tensor_q[1024][6]; // 6-component discrete curvature tensor
};

This layout allows AVX2 / AVX-512 / NEON vector execution units to load contiguous coordinates directly into SIMD registers without scatter-gather overhead.