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https://github.com/donpablonows/coin
🪙 Crypto Optimization Interface Network (aka COIN) is a high-performance Bitcoin address generator using CUDA acceleration and multi-threading. It optimizes GPU and CPU resources for fast address generation, ensures secure private key creation, and includes real-time monitoring and automatic system optimizations.
https://github.com/donpablonows/coin
bitcoin blockchain cryptography cuda gpu-acceleration
Last synced: about 1 month ago
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🪙 Crypto Optimization Interface Network (aka COIN) is a high-performance Bitcoin address generator using CUDA acceleration and multi-threading. It optimizes GPU and CPU resources for fast address generation, ensures secure private key creation, and includes real-time monitoring and automatic system optimizations.
- Host: GitHub
- URL: https://github.com/donpablonows/coin
- Owner: donpablonows
- License: mit
- Created: 2025-01-05T22:16:12.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-01-05T23:07:28.000Z (about 1 month ago)
- Last Synced: 2025-01-05T23:23:46.846Z (about 1 month ago)
- Topics: bitcoin, blockchain, cryptography, cuda, gpu-acceleration
- Language: Python
- Homepage:
- Size: 64.5 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🪙 COIN (Crypto Optimization Interface Network)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://developer.nvidia.com/cuda-downloads)
[](https://github.com/psf/black)
[](https://docs.pytest.org/)
[](https://www.sphinx-doc.org/)
[](https://developer.nvidia.com/cuda-toolkit)
[](https://github.com/yourusername/coin/security)
[](https://coverage.readthedocs.io/)
🚀 Advanced Bitcoin Address Generation through CUDA-Accelerated Parallel Computing
Achieving unprecedented cryptographic processing speeds through innovative GPU optimization and distributed computing techniques
---
📚 Table of Contents (Click to Expand)
1. [Overview](#-overview)
- [Core Capabilities](#core-capabilities)
- [Technical Innovation](#technical-innovation)
- [Performance Metrics](#performance-metrics)
2. [Scientific Foundation](#-scientific-foundation)
- [Cryptographic Principles](#cryptographic-principles)
- [Mathematical Framework](#mathematical-framework)
- [Algorithmic Complexity](#algorithmic-complexity)
3. [Technical Architecture](#-technical-architecture)
- [System Design](#system-design)
- [Component Interaction](#component-interaction)
- [Data Flow](#data-flow)
4. [Performance Analysis](#-performance-analysis)
- [Benchmarking Methodology](#benchmarking-methodology)
- [Performance Models](#performance-models)
- [Optimization Techniques](#optimization-techniques)
5. [Implementation Details](#-implementation-details)
- [Core Components](#core-components)
- [CUDA Integration](#cuda-integration)
- [Memory Management](#memory-management)
6. [Security Architecture](#-security-architecture)
- [Cryptographic Implementation](#cryptographic-implementation)
- [Security Measures](#security-measures)
- [Threat Mitigation](#threat-mitigation)
7. [Development Guide](#-development-guide)
- [Setup Instructions](#setup-instructions)
- [Development Workflow](#development-workflow)
- [Testing Framework](#testing-framework)
8. [Advanced Usage](#-advanced-usage)
- [Configuration Options](#configuration-options)
- [API Reference](#api-reference)
- [Integration Guide](#integration-guide)
9. [Performance Optimization](#-performance-optimization)
- [GPU Acceleration](#gpu-acceleration)
- [Memory Optimization](#memory-optimization)
- [Threading Model](#threading-model)
10. [Troubleshooting](#-troubleshooting)
- [Common Issues](#common-issues)
- [Diagnostics](#diagnostics)
- [Solutions](#solutions)
11. [Deployment Options](#-deployment-options)
- [One-Click Deploy](#one-click-deploy)
- [Docker Deployment](#docker-deployment)
- [Cloud Deployment](#cloud-deployment)
12. [Probability Analysis](#-probability-analysis)
- [Finding Existing Wallets](#finding-existing-wallets)
- [Mathematical Odds](#mathematical-odds)
- [Time Estimates](#time-estimates)
13. [FAQ](#-frequently-asked-questions)
- [General Questions](#general-questions)
- [Technical Questions](#technical-questions)
- [Security Questions](#security-questions)
---
## 🌟 Overview
### Technical Innovation
**COIN** represents a breakthrough in Bitcoin address generation technology, implementing cutting-edge parallel processing algorithms through CUDA acceleration. The system achieves unprecedented performance while maintaining the highest standards of cryptographic security.
### Core Capabilities Matrix
Component
Technology
Implementation
Performance Impact
CUDA Acceleration
Parallel GPU Processing
cuda.py
+5000% throughput
Multi-threading
CPU Core Optimization
process.py
+200% efficiency
Memory Management
Efficient I/O Operations
manager.py
+300% I/O speed
Cryptographic Engine
ECDSA Optimization
crypto.py
+150% key generation
## 🧮 Scientific Foundation
### Mathematical Framework
#### 1. Elliptic Curve Operations
The fundamental operation in Bitcoin address generation is the elliptic curve point multiplication:
```math
P = k × G \mod p
```
where:
- P = public key point
- k = private key (scalar)
- G = generator point
- p = field characteristic
#### 2. Computational Complexity Analysis
The system's performance is characterized by the following complexity metrics:
```math
T_{total} = O(n \log n) + O(m) + O(p)
```
where:
- n = number of addresses to generate
- m = GPU memory bandwidth
- p = parallel processing overhead
#### 3. Memory Utilization Model
```math
M_{total} = M_{base} + \sum_{i=1}^{n} (M_{thread_i} + M_{cache_i})
```
### Performance Metrics
| Operation | CPU Only | GPU (RTX 3090) | Improvement Factor | Theoretical Maximum |
|-----------|:--------:|:--------------:|:-----------------:|:------------------:|
| Address Generation | 1,000/s | 5,000,000/s | 5000x | 7,500,000/s |
| Vanity Address (4 chars) | 30s | 0.1s | 300x | 0.08s |
| Batch Processing | 10,000/s | 1,000,000/s | 100x | 1,500,000/s |
| Memory Bandwidth | 50 GB/s | 936 GB/s | 18.72x | 1000 GB/s |
## 🏗️ Technical Architecture
### System Components
```mermaid
graph TD
A[Input Manager] --> B[CUDA Controller]
B --> C[GPU Workers]
B --> D[CPU Workers]
C --> E[Memory Manager]
D --> E
E --> F[Output Handler]
subgraph GPU Processing
C --> G[SM Units]
G --> H[CUDA Cores]
end
subgraph Memory Hierarchy
I[L1 Cache] --> J[L2 Cache]
J --> K[Global Memory]
end
```
### Memory Architecture
```mermaid
graph LR
A[Thread] --> B[L1 Cache]
B --> C[L2 Cache]
C --> D[Global Memory]
subgraph Cache Hierarchy
B --> E[48KB/SM]
C --> F[6MB Shared]
D --> G[24GB GDDR6X]
end
```
## 💻 Implementation Details
### CUDA Optimization
```python
@cuda.jit
def parallel_key_generation(
private_keys: np.ndarray,
public_keys: np.ndarray,
batch_size: int,
threads_per_block: int = 256
) -> None:
"""
Parallel private key generation using CUDA.
Parameters:
private_keys (np.ndarray): Array of private keys
public_keys (np.ndarray): Array for storing public keys
batch_size (int): Number of keys to generate
threads_per_block (int): Thread block size
Performance:
Time Complexity: O(n/p) where p = number of CUDA cores
Space Complexity: O(n) in global memory
"""
idx = cuda.grid(1)
stride = cuda.gridsize(1)
if idx < batch_size:
# Shared memory for temporary calculations
temp = cuda.shared.array(shape=32, dtype=np.uint8)
# Generate private key using parallel random number generation
for i in range(idx, batch_size, stride):
private_keys[i] = generate_secure_random(temp)
public_keys[i] = secp256k1_multiply(private_keys[i])
```
### Memory Management
```python
class OptimizedMemoryManager:
"""
Advanced memory management system with CUDA optimization.
Attributes:
page_size (int): System memory page size
buffer_size (int): Optimal buffer size for GPU operations
cache_config (dict): Cache configuration parameters
"""
def __init__(
self,
gpu_memory_limit: int = 8 * 1024**3, # 8GB
page_size: int = 4096,
cache_ratio: float = 0.75
):
self.page_size = page_size
self.buffer_size = self._calculate_optimal_buffer(gpu_memory_limit)
self.cache_config = self._initialize_cache(cache_ratio)
def _calculate_optimal_buffer(self, limit: int) -> int:
"""
Calculate optimal buffer size based on GPU specifications.
Args:
limit (int): Maximum GPU memory limit
Returns:
int: Optimal buffer size in bytes
Complexity:
Time: O(1)
Space: O(1)
"""
device_props = cuda.get_device_properties(0)
max_threads = device_props.max_threads_per_block
warp_size = device_props.warp_size
return min(
limit,
max_threads * warp_size * self.page_size
)
```
## 🔐 Security Implementation
### Cryptographic Operations
```python
class CryptographicEngine:
"""
High-performance cryptographic operations manager.
Features:
- Secure random number generation
- Elliptic curve operations
- Key derivation functions
- Memory protection
"""
def __init__(self, security_level: int = 256):
self.security_level = security_level
self._initialize_secure_context()
def generate_private_key(self) -> bytes:
"""
Generate cryptographically secure private key.
Returns:
bytes: 32-byte private key
Security:
- Uses hardware RNG when available
- Implements additional entropy pooling
- Applies memory protection
"""
key = secrets.token_bytes(32)
self._protect_memory(key)
return key
def derive_public_key(self, private_key: bytes) -> bytes:
"""
Derive public key using optimized secp256k1.
Args:
private_key (bytes): 32-byte private key
Returns:
bytes: 33-byte compressed public key
Security:
- Constant-time implementation
- Side-channel attack protection
"""
return secp256k1.PrivateKey(private_key).pubkey.serialize()
```
## ⚡ Advanced Usage
### Configuration Options
```python
OPTIMIZATION_PARAMS = {
# CUDA Configuration
'cuda': {
'thread_block_size': 256,
'shared_memory_size': 48 * 1024,
'max_registers_per_thread': 64,
'memory_transfer_block': 2 * 1024 * 1024,
'compute_capability': '8.6'
},
# Memory Management
'memory': {
'page_size': 4096,
'l1_cache_size': 128 * 1024,
'l2_cache_size': 6 * 1024 * 1024,
'shared_memory_per_block': 48 * 1024
},
# Threading Model
'threading': {
'min_threads': 4,
'max_threads': 32,
'thread_multiplier': 2,
'core_affinity': True
}
}
```
### Performance Monitoring
```python
@dataclass
class PerformanceMetrics:
"""
Real-time performance monitoring metrics.
"""
throughput: float # addresses/second
gpu_utilization: float # percentage
memory_usage: float # bytes
power_consumption: float # watts
temperature: float # celsius
```
## 📊 Benchmarking
### Methodology
1. **Test Environment**
```python
TEST_ENVIRONMENT = {
'cpu': 'AMD Ryzen 9 5950X',
'gpu': 'NVIDIA RTX 3090',
'ram': '64GB DDR4-3600',
'os': 'Ubuntu 22.04 LTS',
'cuda': '11.7',
'python': '3.11.4'
}
```
2. **Performance Tests**
```python
def run_benchmark(
batch_size: int,
duration: int,
threads: int
) -> BenchmarkResults:
"""
Run comprehensive performance benchmark.
Args:
batch_size: Number of addresses per batch
duration: Test duration in seconds
threads: Number of CPU threads
Returns:
BenchmarkResults with detailed metrics
"""
metrics = []
start_time = time.perf_counter_ns()
while (time.perf_counter_ns() - start_time) < duration * 1e9:
result = generate_addresses(batch_size, threads)
metrics.append(collect_metrics(result))
return analyze_results(metrics)
```
## 🔍 Troubleshooting
### Diagnostic Tools
```python
class SystemDiagnostics:
"""
Comprehensive system diagnostics and troubleshooting.
"""
@staticmethod
def check_cuda_compatibility() -> dict:
"""Verify CUDA compatibility and configuration."""
try:
return {
'cuda_version': cuda.get_cuda_version(),
'driver_version': cuda.get_driver_version(),
'device_count': cuda.get_device_count(),
'compute_capability': cuda.get_device_properties(0).compute_capability
}
except CudaError as e:
return {'error': str(e)}
@staticmethod
def analyze_memory_usage() -> dict:
"""Analyze system memory usage and patterns."""
return {
'ram_available': psutil.virtual_memory().available,
'ram_used': psutil.virtual_memory().used,
'swap_used': psutil.swap_memory().used,
'gpu_memory': cuda.get_device_properties(0).total_memory
}
```
## 📜 License
This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.
## ⚠️ Disclaimer
This tool is for educational and research purposes only. Users must comply with all applicable laws and regulations. The developers assume no liability for any misuse or damage caused by this software.
---
## 🚀 Deployment Options
### One-Click Deploy
[](https://heroku.com/deploy?template=https://github.com/donpablonows/coin)
[](https://vercel.com/new/clone?repository-url=https://github.com/donpablonows/coin)
[](https://gitpod.io/#https://github.com/donpablonows/coin)
[](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2Fdonpablonows%2Fcoin%2Fmain%2Fazuredeploy.json)
[](https://deploy.cloud.run/?git_repo=https://github.com/donpablonows/coin)
[](https://colab.research.google.com/github/donpablonows/coin/blob/main/notebooks/COIN_Demo.ipynb)
[](https://mybinder.org/v2/gh/donpablonows/coin/main?filepath=notebooks%2FCOIN_Demo.ipynb)
### JupyterHub Deployment
```bash
# Deploy on JupyterHub
helm repo add jupyterhub https://jupyterhub.github.io/helm-chart/
helm repo update
helm upgrade --install coin jupyterhub/jupyterhub \
--namespace coin \
--create-namespace \
--version=2.0.0 \
--values config.yaml
```
Example `config.yaml`:
```yaml
singleuser:
image:
name: donpablonows/coin
tag: latest
extraEnv:
CUDA_VISIBLE_DEVICES: "0"
resources:
limits:
nvidia.com/gpu: 1
```
### Processing Flow Architecture
```mermaid
flowchart TD
subgraph Input
A[User Input] --> B[Configuration]
B --> C[Validation]
end
subgraph Processing
C --> D[CUDA Initialization]
D --> E[Memory Allocation]
E --> F[Key Generation]
F --> G[Address Derivation]
end
subgraph Output
G --> H[Results]
H --> I[Storage]
H --> J[Display]
end
subgraph Monitoring
K[Performance Metrics]
L[Error Handling]
M[Resource Usage]
end
```
## 📊 Probability Analysis
### Mathematical Impossibility
The probability of finding an existing Bitcoin wallet is so astronomically small that it's effectively impossible. Here's a detailed breakdown:
1. **Total Possible Private Keys**: 2^256 (approximately 10^77)
- This is more than the number of atoms in the observable universe (10^80)
- More than all grains of sand on Earth multiplied by stars in the universe
2. **Time to Search All Keys**:
```math
T_{total} = \frac{2^{256}}{R_{search}}
```
where R_search is the search rate in keys/second
| Hardware Setup | Keys/Second | Time to Search 0.0001% |
|----------------|-------------|----------------------|
| Single RTX 3090 | 5M/s | 10^63 years |
| 1000 RTX 3090s | 5B/s | 10^60 years |
| All Bitcoin Mining Power | 300EH/s | 10^56 years |
| Theoretical Quantum Computer | 10^12/s | 10^50 years |
| All Computers on Earth | 10^15/s | 10^47 years |
For perspective:
- Age of Universe: ~13.8 billion years (10^10)
- Heat death of Universe: ~10^100 years
### Processing Architecture
```mermaid
sequenceDiagram
participant User
participant InputManager
participant CUDAController
participant GPUWorkers
participant MemoryManager
participant Blockchain
User->>InputManager: Configure Parameters
InputManager->>CUDAController: Initialize GPU
loop Parallel Processing
CUDAController->>GPUWorkers: Allocate Work Units
GPUWorkers->>GPUWorkers: Generate Keys
GPUWorkers->>MemoryManager: Store Results
opt Balance Check
MemoryManager->>Blockchain: Query Balance
Blockchain-->>MemoryManager: Return Balance
end
end
MemoryManager->>User: Return Results
```
## ❓ Extended FAQ
### General Questions
What makes COIN different from other address generators?
1. **Performance**
- CUDA optimization for 5000x speedup
- Advanced memory management
- Parallel processing architecture
2. **Features**
- Real-time blockchain monitoring
- Multi-GPU support
- Distributed computing capability
3. **Security**
- Audited codebase
- Memory protection
- Side-channel attack prevention
What are the exact hardware requirements?
**Minimum:**
- NVIDIA GPU: GTX 1060 6GB
- CPU: 4 cores @ 3.0GHz
- RAM: 8GB DDR4
- Storage: 50GB SSD
- OS: Ubuntu 20.04/Windows 10
- CUDA: 11.0+
**Recommended:**
- GPU: RTX 3090 24GB
- CPU: Ryzen 9 5950X
- RAM: 32GB DDR4-3600
- Storage: 500GB NVMe
- OS: Ubuntu 22.04
- CUDA: 11.7+
**Enterprise:**
- Multiple RTX 4090s
- ThreadRipper Pro
- 256GB ECC RAM
- 2TB NVMe RAID
### Technical Deep Dive
How does the memory optimization work?
```mermaid
graph TD
A[Memory Manager] --> B[L1 Cache
48KB/SM]
A --> C[L2 Cache
6MB]
A --> D[Global Memory
24GB]
subgraph Memory Hierarchy
B --> E[Thread Blocks]
C --> F[Warp Schedulers]
D --> G[PCIe Transfer]
end
subgraph Optimization
H[Coalesced Access]
I[Bank Conflicts]
J[Cache Lines]
end
```
Key optimizations:
1. Coalesced memory access patterns
2. Shared memory utilization
3. Bank conflict prevention
4. Cache-friendly algorithms
### Performance Analysis
```mermaid
gantt
title Resource Utilization Over Time
dateFormat X
axisFormat %s
section GPU
CUDA Cores :0, 95
Memory BW :0, 85
section CPU
Thread Pool :0, 45
I/O Handling :0, 25
section Memory
Transfers :0, 70
Caching :0, 60
```
### Security Implementation
```mermaid
flowchart TD
subgraph Security Layers
A[Input Validation] --> B[Memory Protection]
B --> C[Entropy Pool]
C --> D[Key Generation]
D --> E[Secure Storage]
end
subgraph Monitoring
F[Access Logs]
G[Resource Usage]
H[Error Tracking]
end
subgraph Protection
I[Side-Channel]
J[Timing Attacks]
K[Memory Dumps]
end
```
Built with ❤️ by the COIN Team