crustybike — Distributed Mining & Topological Analysis

A distributed Bitcoin mining system focused on topological analysis of the "Patoshi" pattern in early Bitcoin blocks. Uses Apple MLX as a tensor math library (not for ML training) for geometric determinant computation, and PyOpenCL for GPU-accelerated SHA-256 mining.

Repository: /Users/alefita/workdir/crustybike/ Branch model: main (protected), feature branches per hypothesis Tags: v0.0.1, v0.0.2, v.0.0.3, v2.2350-h16

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Architecture

Layer	Technology	Purpose
Solver	MLX (mlx.core)	Topological evaluation — geometric determinants on Metal GPU
Mining	PyOpenCL	Cartesian Miner — Double SHA-256 + DAA distance
Merkle	MLX + Metal	Zero-copy permutations via mx.take(), on-GPU tree construction
Workers	Python asyncio	QUIC-based distributed mining
TTS	mlx_audio	Audio generation (side feature)

Compute Stack

• MLX ≥ 0.31.1 (macOS) / MLX[cuda] (Linux x86_64) — Tensor math, zero-copy gather
• PyOpenCL — GPU mining kernels
• scikit-learn, umap-learn, pysr — Offline analysis (notebooks)
• No PyTorch — Zero imports, zero references

Kernel Architecture

V1 (Legacy) — Scientific Validation

• chronobreaker_combined.cl (1272 lines, 14 kernels)
• cartesian_miner.cl (132 lines)

V2 (Production) — Lean Mining

• cartesian_miner.cl (132 lines) — Mining kernel
• topology_eval.cl (193 lines) — Swift skip via discriminant

Metal

• cartesian_miner.metal (128 lines) — MLX Metal conventions
• merkle_sha256.metal (130 lines) — On-GPU Merkle tree

Key Optimizations

Pattern	Description
Zero-copy Merkle	mx.take() GPU-side gather, no host copy
Swift Skip	b² - 4ac < 0 → skip dispatch entirely
TDR Recovery	Full OpenCL context rebuild after GPU timeout
Atomic CAS	atomic_cmpxchg for race-free nonce discovery
Tropa de Choque	10 concurrent prefetch workers
Midstate caching	SHA-256 first chunk computed once

Related Pages

• compute-optimization — Hub
• mlx-opencl-bridge — Zero-copy pattern
• opencl-kernel-design — Kernel inventory
• gpu-async-pipelines — TDR recovery, prefetch architecture

Status

• ✅ Production mining via Cartesian Miner
• ✅ Topological solver on MLX
• ✅ Zero-copy Merkle permutations
• ✅ TDR recovery
• ✅ Metal kernels present
• ⚠️ Metal kernels not explicitly loaded by Python
• ⚠️ float32 exclusively
• 🔜 Potential Metal→MLX native pipeline