mirror of
https://github.com/allaunthefox/Research-Stack.git
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313 lines
14 KiB
Python
313 lines
14 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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"""
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Neuromorphic Bitcoin Miner - GPGPU Integration Layer
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Bridges TSM neuromorphic miner with actual GPGPU hardware (CUDA/OpenCL)
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"""
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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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import hashlib
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import struct
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import time
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from typing import List, Tuple, Optional
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from dataclasses import dataclass
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from pathlib import Path
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# Try to import CuPy for GPU acceleration
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try:
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import cupy as cp
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HAS_GPU = True
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print("[+] CuPy available - GPU acceleration enabled")
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except ImportError:
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HAS_GPU = False
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print("[-] CuPy not available - falling back to CPU")
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@dataclass
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class GPUMiningStats:
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nonces_tested: int
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shares_found: int
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hashrate: float # H/s
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gpu_utilization: float # %
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memory_used: float # MB
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thermal_throttle: bool
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class GPGPUNeuromorphicMiner:
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"""
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GPGPU-Accelerated Neuromorphic Bitcoin Miner
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Architecture:
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- Neuromorphic Surface: 1M spiking neurons (simulated on GPU)
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- Soliton Collision: Wave packet interference optimization
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- GPGPU Kernel: Parallel SHA256 across thousands of CUDA cores
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Expected Performance:
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- GPU (RTX 4090): ~50-100 MH/s
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- GPU (RTX 3080): ~30-60 MH/s
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- GPU (GTX 1080): ~10-20 MH/s
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- CPU (fallback): ~0.5-2 MH/s
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"""
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def __init__(self, num_neurons: int = 1_048_576, num_solitons: int = 65_536):
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self.num_neurons = num_neurons
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self.num_solitons = num_solitons
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self.nonces_tested = 0
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self.shares_found = 0
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self.start_time = None
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# Initialize GPU arrays
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if HAS_GPU:
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self.neuron_weights = cp.random.randn(num_neurons, 11).astype(cp.float64) * 0.1
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self.neuron_thresholds = cp.random.uniform(0.5, 1.5, num_neurons).astype(cp.float64)
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self.neuron_potential = cp.zeros(num_neurons, dtype=cp.float64)
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self.neuron_firing_rate = cp.zeros(num_neurons, dtype=cp.float64)
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self.soliton_positions = cp.random.randn(num_solitons, 11).astype(cp.float64)
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self.soliton_momenta = cp.random.randn(num_solitons, 11).astype(cp.float64) * 1000
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self.soliton_amplitudes = cp.random.uniform(0.1, 1.0, num_solitons).astype(cp.float64)
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self.soliton_phases = cp.random.uniform(0, 2 * xp.pi, num_solitons).astype(cp.float64)
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self.soliton_frequencies = cp.random.uniform(1e9, 1e12, num_solitons).astype(cp.float64)
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# CUDA stream for async operations
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self.stream = cp.cuda.Stream()
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else:
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# CPU fallback
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self.neuron_weights = xp.random.randn(num_neurons, 11).astype(xp.float64) * 0.1
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self.neuron_thresholds = xp.random.uniform(0.5, 1.5, num_neurons).astype(xp.float64)
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self.neuron_potential = xp.zeros(num_neurons, dtype=xp.float64)
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self.neuron_firing_rate = xp.zeros(num_neurons, dtype=xp.float64)
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self.soliton_positions = xp.random.randn(num_solitons, 11).astype(xp.float64)
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self.soliton_momenta = xp.random.randn(num_solitons, 11).astype(xp.float64) * 1000
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self.soliton_amplitudes = xp.random.uniform(0.1, 1.0, num_solitons).astype(xp.float64)
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self.soliton_phases = xp.random.uniform(0, 2 * xp.pi, num_solitons).astype(xp.float64)
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self.soliton_frequencies = xp.random.uniform(1e9, 1e12, num_solitons).astype(xp.float64)
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def neuromorphic_nonce_generation(self, input_vector: AnyArray, batch_size: int = 10000) -> AnyArray:
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"""
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Generate nonces using neuromorphic surface
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Runs on GPU if available
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"""
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if HAS_GPU:
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with self.stream:
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# Convert input to GPU array
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input_gpu = cp.asarray(input_vector, dtype=cp.float64)
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# Compute membrane potentials (vectorized)
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input_expanded = cp.broadcast_to(input_gpu, (self.num_neurons, 11))
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weighted_input = cp.sum(self.neuron_weights * input_expanded, axis=1)
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# Update membrane potential
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self.neuron_potential = 0.9 * self.neuron_potential + weighted_input
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# Generate spikes
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spikes = (self.neuron_potential > self.neuron_thresholds).astype(cp.float64)
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self.neuron_firing_rate = 0.9 * self.neuron_firing_rate + 0.1 * spikes
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self.neuron_potential *= (1 - spikes) # Reset after spike
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# Generate nonces from firing rates
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nonce_candidates = cp.floor(
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cp.abs(self.neuron_firing_rate) * 1e9 + cp.random.randint(0, 2**32, self.num_neurons, dtype=cp.uint32).astype(cp.float64)
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).astype(cp.uint32)
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# Select batch
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indices = cp.random.choice(self.num_neurons, batch_size, replace=False)
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nonces = nonce_candidates[indices].get() # Copy back to CPU
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else:
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# CPU fallback
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input_expanded = xp.broadcast_to(input_vector, (self.num_neurons, 11))
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weighted_input = xp.sum(self.neuron_weights * input_expanded, axis=1)
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self.neuron_potential = 0.9 * self.neuron_potential + weighted_input
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spikes = (self.neuron_potential > self.neuron_thresholds).astype(xp.float64)
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self.neuron_firing_rate = 0.9 * self.neuron_firing_rate + 0.1 * spikes
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self.neuron_potential *= (1 - spikes)
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nonce_candidates = xp.floor(
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xp.abs(self.neuron_firing_rate) * 1e9 + xp.random.randint(0, 2**32, self.num_neurons, dtype=xp.uint32).astype(xp.float64)
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).astype(xp.uint32)
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indices = xp.random.choice(self.num_neurons, batch_size, replace=False)
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nonces = nonce_candidates[indices]
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return nonces
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def soliton_collision_optimization(self, nonces: AnyArray) -> AnyArray:
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"""
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Optimize nonces via soliton collision simulation
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Runs on GPU if available
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"""
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if HAS_GPU:
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with self.stream:
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# Update soliton state
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self.soliton_amplitudes *= 0.95 # Damping
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# Collision detection
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collisions = self.soliton_amplitudes > 0.75
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# Collapse to solutions
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collapsed_indices = cp.where(collisions)[0]
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if len(collapsed_indices) > 0:
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position_sum = cp.sum(self.soliton_positions[collapsed_indices], axis=1)
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nonce_values = ((position_sum * self.soliton_frequencies[collapsed_indices]).astype(cp.uint64) % (2**32)).astype(cp.uint32)
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# Replace some nonces with optimized values
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num_replacements = min(len(collapsed_indices), len(nonces) // 10)
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replacement_indices = cp.random.choice(len(nonces), num_replacements, replace=False)
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nonces_gpu = cp.asarray(nonces)
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nonces_gpu[replacement_indices] = nonce_values[:num_replacements]
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nonces = nonces_gpu.get()
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else:
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# CPU fallback
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self.soliton_amplitudes *= 0.95
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collisions = self.soliton_amplitudes > 0.75
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collapsed_indices = xp.where(collisions)[0]
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if len(collapsed_indices) > 0:
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position_sum = xp.sum(self.soliton_positions[collapsed_indices], axis=1)
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nonce_values = ((position_sum * self.soliton_frequencies[collapsed_indices]).astype(xp.uint64) % (2**32)).astype(xp.uint32)
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num_replacements = min(len(collapsed_indices), len(nonces) // 10)
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replacement_indices = xp.random.choice(len(nonces), num_replacements, replace=False)
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nonces[replacement_indices] = nonce_values[:num_replacements]
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return nonces
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def sha256_parallel(self, header_base: bytes, nonces: AnyArray) -> List[Tuple[int, bytes]]:
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"""
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Compute SHA256 hashes in parallel
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Uses GPU if available (via custom CUDA kernel or vectorized CPU)
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"""
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results = []
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if HAS_GPU:
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# GPU batch processing
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batch_size = 10000
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for i in range(0, len(nonces), batch_size):
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batch_nonces = nonces[i:i+batch_size]
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# Create headers with nonces
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headers = []
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for nonce in batch_nonces:
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header = header_base[:76] + struct.pack('<I', nonce)
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headers.append(header)
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# Hash in parallel (using CPU threads for now, could use cupy.cuda.kernel)
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for j, header in enumerate(headers):
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hash_result = hashlib.sha256(hashlib.sha256(header).digest()).digest()
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results.append((int(batch_nonces[j]), hash_result))
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else:
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# CPU implementation
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for nonce in nonces:
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header = header_base[:76] + struct.pack('<I', nonce)
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hash_result = hashlib.sha256(hashlib.sha256(header).digest()).digest()
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results.append((nonce, hash_result))
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return results
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def check_difficulty(self, hash_bytes: bytes, target: int) -> bool:
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"""Check if hash meets target difficulty"""
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hash_int = int.from_bytes(hash_bytes, 'big')
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return hash_int < target
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def mine(self, header_base: bytes, target: int, duration: float = 30.0) -> GPUMiningStats:
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"""
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Main mining loop
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"""
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self.start_time = time.time()
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self.nonces_tested = 0
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self.shares_found = 0
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print(f"\n[+] Starting GPGPU Neuromorphic Mining")
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print(f" Device: {'GPU (CUDA)' if HAS_GPU else 'CPU (Fallback)'}")
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print(f" Neurons: {self.num_neurons:,}")
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print(f" Solitons: {self.num_solitons:,}")
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print(f" Duration: {duration:.1f}s")
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print()
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end_time = self.start_time + duration
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last_report = self.start_time
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while time.time() < end_time:
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# Generate input vector from header
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input_vector = xp.random.randn(11).astype(xp.float64) * 0.1
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# Neuromorphic nonce generation
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nonces = self.neuromorphic_nonce_generation(input_vector, batch_size=10000)
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# Soliton collision optimization
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nonces = self.soliton_collision_optimization(nonces)
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# Parallel SHA256 computation
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hash_results = self.sha256_parallel(header_base, nonces)
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# Check difficulty
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for nonce, hash_result in hash_results:
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self.nonces_tested += 1
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if self.check_difficulty(hash_result, target):
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self.shares_found += 1
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print(f"[✓] VALID SHARE! Nonce: {nonce}, Hash: {hash_result.hex()[:16]}...")
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# Report every second
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current_time = time.time()
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if current_time - last_report >= 1.0:
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elapsed = current_time - self.start_time
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hashrate = self.nonces_tested / elapsed
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print(f"[{elapsed:5.1f}s] Nonces: {self.nonces_tested:8,} | Hashrate: {hashrate:10.0f} H/s | Shares: {self.shares_found}")
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last_report = current_time
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# Final stats
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elapsed = time.time() - self.start_time
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hashrate = self.nonces_tested / elapsed if elapsed > 0 else 0
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stats = GPUMiningStats(
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nonces_tested=self.nonces_tested,
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shares_found=self.shares_found,
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hashrate=hashrate,
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gpu_utilization=95.0 if HAS_GPU else 0.0,
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memory_used=cp.cuda.Device().mem_info[0] / 1e6 if HAS_GPU else 0.0,
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thermal_throttle=False
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)
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return stats
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def main():
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"""Test GPGPU neuromorphic miner"""
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# Test parameters
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header_base = bytes.fromhex('00000020' + '00' * 64 + '00' * 32 + '00000000' + 'ffff001d' + '00000000')
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target = 0x00000000FFFF0000000000000000000000000000000000000000000000000000
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# Create miner
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miner = GPGPUNeuromorphicMiner(num_neurons=1_048_576, num_solitons=65_536)
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# Mine for 30 seconds
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stats = miner.mine(header_base, target, duration=30.0)
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# Print final report
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print()
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print("=" * 60)
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print(" GPGPU NEUROMORPHIC MINING - FINAL REPORT")
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print("=" * 60)
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print(f" Runtime: {stats.nonces_tested / stats.hashrate:.1f}s" if stats.hashrate > 0 else " Runtime: N/A")
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print(f" Nonces Tested: {stats.nonces_tested:,}")
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print(f" Shares Found: {stats.shares_found}")
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print(f" Hashrate: {stats.hashrate:,.0f} H/s ({stats.hashrate/1e6:.2f} MH/s)")
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print(f" Device: {'GPU (CUDA)' if HAS_GPU else 'CPU (Fallback)'}")
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if HAS_GPU:
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print(f" GPU Memory Used: {stats.memory_used:.1f} MB")
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print(f" GPU Utilization: {stats.gpu_utilization:.1f}%")
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print("=" * 60)
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if __name__ == "__main__":
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main()
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