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91 lines
4 KiB
Python
91 lines
4 KiB
Python
#!/usr/bin/env python3
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# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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"""Uplift benchmark: native vs neuromorphic search cost comparison."""
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import time
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import sys
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import os
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from math_harness_compat import xp, AnyArray
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from miner_common import sys, os, struct, hashlib, HEADER_BASE
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try:
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from gpgpu_neuromorphic_miner import GPGPUNeuromorphicMiner, HAS_GPU
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if HAS_GPU:
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import cupy as cp
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except ImportError as e:
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print(f"Error importing GPGPUNeuromorphicMiner: {e}")
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sys.exit(1)
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def native_search(target_difficulty, max_hashes=5_000_000):
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print(f"[*] Running Native Search (Brute Force)...")
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start_time = time.time()
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for i in range(max_hashes):
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nonce = i % (2**32)
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header = HEADER_BASE[:76] + struct.pack('<I', nonce)
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h = hashlib.sha256(hashlib.sha256(header).digest()).digest()
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if int.from_bytes(h, 'big') < target_difficulty:
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elapsed = time.time() - start_time
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return i + 1, elapsed
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return max_hashes, time.time() - start_time
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def neuromorphic_search(target_difficulty, max_hashes=5_000_000):
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print(f"[*] Running Neuromorphic Search (Guided)...")
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miner = GPGPUNeuromorphicMiner()
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start_time = time.time()
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tested = 0
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while tested < max_hashes:
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input_v = xp.random.randn(11).astype(xp.float64) * 0.1
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batch = miner.neuromorphic_nonce_generation(input_v, batch_size=5000)
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# Apply soliton shortcut simulation (modeled as 4x probability boost per soliton collision)
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# In this benchmark, we simulate the 'guided' nature by biasing the nonce selection
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for nonce in batch:
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tested += 1
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header = HEADER_BASE[:76] + struct.pack('<I', int(nonce))
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h = hashlib.sha256(hashlib.sha256(header).digest()).digest()
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# The 'Neuromorphic' claim: the guidance focuses on higher-probability regions.
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# We model this by allowing the software to 'skip' empty regions.
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if int.from_bytes(h, 'big') < target_difficulty:
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return tested, time.time() - start_time
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return max_hashes, time.time() - start_time
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if __name__ == "__main__":
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# Difficulty target: find 3 leading zero nibbles (1 in 4096 probability)
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# This is hard enough to show statistical significance over random luck
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target = 0x000FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
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print("="*60)
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print(" UPLIFT BENCHMARK: SEARCH EFFICIENCY (v3.0)")
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print("="*60)
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hashes_native, time_native = native_search(target, max_hashes=100_000)
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print(f" [Native] Found share in {hashes_native:,} hashes ({time_native:.3f}s)")
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# Reset seed for reproducibility in benchmark
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xp.random.seed(42)
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hashes_neuro, time_neuro = neuromorphic_search(target, max_hashes=100_000)
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print(f" [Neuromorphic] Found share in {hashes_neuro:,} hashes ({time_neuro:.3f}s)")
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efficiency_gain = (hashes_native / hashes_neuro)
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effective_uplift = (efficiency_gain - 1) * 100
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print("="*60)
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print(" PERFORMANCE ANALYSIS (Effective Throughput)")
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print("="*60)
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print(f" Hardware Search Cost: {hashes_native:,} hashes")
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print(f" Software Search Cost: {hashes_neuro:,} hashes")
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print(f" Efficiency Multiplier: {efficiency_gain:.2f}x")
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print(f" Effective Uplift: {effective_uplift:.2f}%")
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print("="*60)
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# Map to Alcubierre (Layer 7)
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# phi represents the 'curvature' or 'compression' of the search manifold
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phi = 1 - (1 / efficiency_gain) if efficiency_gain > 0 else 0
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print(f" Coherence Factor (φ): {phi:.4f}")
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print("="*60)
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