beyond-sort

Entropy-driven adaptive sorting that beats C-level np.sort() on real-world data.

One line. Automatic. Faster than C.

import beyond_sort

result = beyond_sort.sort(data)         # auto-selects optimal strategy
strategy = beyond_sort.probe(data)      # inspect which strategy would be used
detail = beyond_sort.probe_detail(data) # full entropy analysis

Why is this faster than np.sort()?

np.sort() uses a single C-level introsort for all data. beyond-sort analyzes your data's structure in a single pass, then routes to the mathematically optimal algorithm compiled to native machine code:

Benchmarks

Tested on 32-core CPU, N = 100,000,000 (100 million integers):

vs Python sorted(): up to 16× faster.

The advantage grows with data size — at 10M elements: 1.1× faster; at 100M: 1.4-7.2× faster.

Architecture: 4-Layer Optimization

┌─────────────────────────────────────────────────┐
│  Layer 1: Symbol   Entropy probe (τ, runs,      │
│                    value range) → strategy route │
├─────────────────────────────────────────────────┤
│  Layer 2: Syntax   Numba @njit → LLVM native    │
│                    machine code, zero interpreter│
├─────────────────────────────────────────────────┤
│  Layer 3: Instr.   NumPy SIMD vectorization,    │
│                    cache-friendly memory access  │
├─────────────────────────────────────────────────┤
│  Layer 4: Parallel Value-range partitioning →   │
│                    prange multi-core, zero-merge │
└─────────────────────────────────────────────────┘

Key insight: Value-range partitioning

Instead of splitting the array by position (which requires merging), we split by value range. Each partition contains a non-overlapping range of values. After sorting each partition independently on separate cores, the results concatenate directly — zero merge step.

Install

pip install beyond-sort

Requirements: Python 3.10+, NumPy, Numba.

When to use beyond-sort

• Sorting large integer arrays (100K+ elements)
• Data with predictable structure (logs, time-series, sensor data, IDs)
• Batch processing where sorting is a bottleneck
• Any scenario where np.sort() isn't fast enough

API

beyond_sort.sort(data) → np.ndarray

Sort an integer array. Accepts list or numpy array. Returns sorted numpy array.

beyond_sort.probe(data) → str

Returns the strategy name without sorting. Useful for debugging.

beyond_sort.probe_detail(data) → dict

Full entropy analysis: tau (order degree), run count, value range, selected strategy.

beyond_sort.warmup()

Pre-compile all JIT paths. Call once at startup for consistent benchmark results.

License

Apache-2.0 — free to use commercially, patent rights reserved.