How to Use SecretScan with GPU to Speed ​​Up Bitcoin Wallet Private Key Mining with Bloom Filter Algorithm

BY KEYHUNTER 21.03.2025
How to Use SecretScan with GPU to Speed ​​Up Bitcoin Wallet Private Key Mining with Bloom Filter Algorithm

Writing a script that uses a GPU to speed up the process of finding a private key to a Bitcoin wallet using the Bloom filter algorithm requires several steps. However, it is worth noting that using a GPU to find private keys may not be the most efficient approach, as it is mainly a task that requires cryptographic calculations, which do not always parallelize well on a GPU. However, below is an example of how you can use a GPU to speed up some calculations using the PyCuda and CUDA library, as well as how you can implement a Bloom filter in Python.

Step 1: Installing the required libraries

First, you’ll need to install the necessary libraries. You’ll need PyCuda for GPU mining and mmh3 for Bloom filter hashing.

bashpip install pycuda mmh3

Step 2: Implementing Bloom filter

A bloom filter is a probabilistic data structure that can tell whether an element is present in a set or not. However, to find a private key, you won’t use it directly, but rather as part of a search optimization process.

pythonimport mmh3
import bitarray

class BloomFilter(object):
    def __init__(self, size, hash_count):
        self.size = size
        self.hash_count = hash_count
        self.bit_array = bitarray.bitarray(size)
        self.bit_array.setall(0)

    def add(self, item):
        for seed in range(self.hash_count):
            result = mmh3.hash(item, seed) % self.size
            self.bit_array[result] = 1

    def lookup(self, item):
        for seed in range(self.hash_count):
            result = mmh3.hash(item, seed) % self.size
            if self.bit_array[result] == 0:
                return False
        return True

# Пример использования
bf = BloomFilter(500000, 7)
bf.add("example_item")
print(bf.lookup("example_item"))  # True

Step 3: Using GPU to speed up computations

To speed up GPU computations, you can use PyCuda. However, to find the private key, you will need to adapt your task to parallel GPU computations.

pythonimport pycuda.autoinit
import pycuda.driver as drv
from pycuda.compiler import SourceModule

# Пример кода для ускорения вычислений на GPU
mod = SourceModule("""
__global__ void example_kernel(float *data) {
    int idx = threadIdx.x;
    data[idx] *= 2;
}
""")

# Инициализация данных
data = [1.0, 2.0, 3.0, 4.0]
data_gpu = drv.mem_alloc(data.__sizeof__())
drv.memcpy_htod(data_gpu, data)

# Вызов ядра
func = mod.get_function("example_kernel")
func(data_gpu, block=(4,1,1))

# Получение результатов
result = [0.0] * len(data)
drv.memcpy_dtoh(result, data_gpu)

print(result)  # [2.0, 4.0, 6.0, 8.0]

Step 4: Integrate with Private Key Finder

To integrate with private key mining, you will need to adapt your task to parallel computing on GPU. This may include key generation, checking their validity, etc. However, this is a complex task that requires a deep understanding of cryptography and parallel computing.

Problems and limitations

  • Cryptographic Security : Finding a private key is a task that goes against the principles of cryptographic security. Bitcoin wallets use strong cryptography to protect private keys.
  • GPU Efficiency : GPUs are well suited for highly parallelized tasks, but cryptographic computations may not always be so.
  • Legal Restrictions : Some countries may have laws that restrict or prohibit attempts to hack or gain unauthorized access to cryptocurrency wallets.

In conclusion, although you can use a GPU to speed up some calculations, finding the private key to a Bitcoin wallet is a difficult task that is not only technically challenging but may also be illegal. Therefore, it is important to use such methods only for educational or research purposes and with a full understanding of the legal implications.

Writing a full-fledged Python script that uses a GPU to speed up the process of finding a private key to a Bitcoin wallet using the Bloom filter algorithm requires deep knowledge of cryptography, parallel computing, and GPU programming. However, I can give you a general framework for how this can be done using pycudaGPU libraries and mmh3Bloom filter hashing.

Step 1: Installing the required libraries

First, you need to install the necessary libraries. To work with the GPU, you will need pycuda, and to implement the Bloom filter, you will need mmh3.

bashpip install pycuda mmh3

Step 2: Implementing Bloom filter

Bloom filter is a probabilistic data structure that can tell whether an element is present in a set or not. We will use it to filter potential private keys.

pythonimport mmh3
import numpy as np

class BloomFilter:
    def __init__(self, size, hash_count):
        self.size = size
        self.hash_count = hash_count
        self.bit_array = np.zeros(size, dtype=np.uint8)

    def add(self, item):
        for seed in range(self.hash_count):
            result = mmh3.hash(item, seed) % self.size
            self.bit_array[result] = 1

    def lookup(self, item):
        for seed in range(self.hash_count):
            result = mmh3.hash(item, seed) % self.size
            if self.bit_array[result] == 0:
                return False
        return True

Step 3: Using GPU for acceleration

To speed up the process of finding private keys, we will use pycuda. However, implementing Bloom filter directly on GPU is not as easy as on CPU, so we will focus on speeding up the process of generating and verifying keys.

pythonimport pycuda.autoinit
import pycuda.driver as cuda
from pycuda.compiler import SourceModule

# Пример кода для ускорения генерации и проверки ключей на GPU
# (это упрощенный пример и требует доработки для реальной задачи)

mod = SourceModule("""
    __global__ void check_keys(float *keys, int size) {
        int idx = blockIdx.x * blockDim.x + threadIdx.x;
        if (idx < size) {
            // Здесь должна быть логика проверки ключа
            // Например, проверка на соответствие определенным критериям
            // keys[idx] = ...;
        }
    }
""")

def generate_and_check_keys_on_gpu(size):
    # Инициализация массива ключей на GPU
    keys_gpu = cuda.mem_alloc(size * np.float32().nbytes)
    
    # Вызов функции на GPU
    func = mod.get_function("check_keys")
    func(keys_gpu, np.int32(size), block=(256,1,1), grid=(size//256+1,1))
    
    # Выборка данных из GPU
    keys = np.empty(size, dtype=np.float32)
    cuda.memcpy_dtoh(keys, keys_gpu)
    
    return keys

Step 4: Integrating Bloom filter and GPU acceleration

To integrate Bloom filter with GPU acceleration, you can first filter potential keys using Bloom filter on CPU and then send the remaining keys to GPU for further verification.

pythondef secret_scan():
    # Настройки Bloom filter
    bf_size = 1000000
    bf_hash_count = 7
    
    bloom_filter = BloomFilter(bf_size, bf_hash_count)
    
    # Генерация и добавление известных ключей в Bloom filter
    # (это упрощенный пример, реальная реализация зависит от ваших данных)
    known_keys = ["key1", "key2"]
    for key in known_keys:
        bloom_filter.add(key)
    
    # Генерация потенциальных ключей и фильтрация с помощью Bloom filter
    potential_keys = []
    for _ in range(1000000):  # Генерация миллиона потенциальных ключей
        key = generate_random_key()  # Функция генерации случайного ключа
        if not bloom_filter.lookup(key):
            potential_keys.append(key)
    
    # Ускоренная проверка оставшихся ключей на GPU
    checked_keys = generate_and_check_keys_on_gpu(len(potential_keys))
    
    # Обработка результатов
    for key in checked_keys:
        # Логика обработки проверенных ключей
        pass

secret_scan()

Important Notes

  1. Bloom filter and GPU acceleration implementation : The code above is a simplified example. A real implementation requires a deeper understanding of cryptography and parallel computing.
  2. Key Generation and Verification : Key verification functions generate_random_key()and logic on the GPU should be implemented according to your specific requirements.
  3. Security : Finding private keys to Bitcoin wallets may be illegal and unethical. This example is for educational purposes only.

This script requires some modification and adaptation to your specific tasks and requirements.

How to install and configure SecretScan on GPU

Installing and configuring SecretScan on GPU involves several steps, especially if you are using Nvidia graphics cards . Below is a step-by-step guide based on the available information:

Step 1: Installing the required components

  1. System : Make sure you have a 64-bit Windows system.
  2. Video Card : Use an Nvidia video card that supports CUDA version 3.0 or higher (e.g. 2011 or newer).
  3. CUDA : Install CUDA 9.2 or later, depending on the recommendation for your SecretScan 1 2 version .
  4. Visual C++ : Install Visual C++ 2013 and Visual C++ 2017 1 2 .

Step 2: Setting up the system

  1. Increase the paging file : In the Control Panel, go to System → Advanced system settings → Performance → Settings → Advanced → Change and increase the size of the paging file 1 .
  2. Driver Setup : Make sure your graphics card drivers are compatible with CUDA. If necessary, update or reinstall your drivers using DDU 1 .

Step 3: Install and configure SecretScan

  1. Registration : Register on the SecretScan website 1 4 .
  2. Personal account : Log in to your personal account and enter your wallet address or generate a new one 1 4 .
  3. Downloading the program : Download the SecretScan program archive and unzip it to drive C or desktop 1 .
  4. Setting up bat files : Edit the files “1_SecretScanGPU.bat” or “2_SecretScanGPU.bat” depending on your video card model. Replace “YourWallet” with your wallet number from your personal account 1 .

Step 4: Launch the program

  1. Launch : Run the selected bat file and wait 3-5 minutes.
  2. Checking the results : Check the results of the program on the SecretScan 1 website .

Additional recommendations

  • Autorun : Create a shortcut to the bat file and add it to the startup folder to automatically run when you turn on your computer 1 .
  • Security : Make sure your antivirus and firewall are not blocking the program 1 .

These steps will help you configure SecretScan to work on GPU with Nvidia video cards. For AMD video cards, the program is in the planning stage 1 .

How to integrate Bloom filter into SecretScan to find private keys

Integrating Bloom filter into SecretScan for private key mining can significantly speed up the process by filtering out unnecessary keys at an early stage. Below is a general outline of how this can be implemented:

Step 1: Implementing Bloom filter

First you need to implement a Bloom filter. This can be done using a mmh3hashing library.

pythonimport mmh3
import numpy as np

class BloomFilter:
    def __init__(self, size, hash_count):
        self.size = size
        self.hash_count = hash_count
        self.bit_array = np.zeros(size, dtype=np.uint8)

    def add(self, item):
        for seed in range(self.hash_count):
            result = mmh3.hash(item, seed) % self.size
            self.bit_array[result] = 1

    def lookup(self, item):
        for seed in range(self.hash_count):
            result = mmh3.hash(item, seed) % self.size
            if self.bit_array[result] == 0:
                return False
        return True

Step 2: Integration with SecretScan

To integrate Bloom filter with SecretScan, you need to modify SecretScan’s code so that it uses Bloom filter to filter potential keys.

  1. Adding known keys to Bloom filter : If you have a list of known keys, add them to Bloom filter.
  2. Generate and filter keys : Generate potential keys and check them with the Bloom filter. If a key is not present in the Bloom filter, it may be potentially new and worth checking further.

Integration example

pythondef generate_and_filter_keys(bloom_filter, num_keys):
    potential_keys = []
    for _ in range(num_keys):
        key = generate_random_key()  # Функция генерации случайного ключа
        if not bloom_filter.lookup(key):
            potential_keys.append(key)
    return potential_keys

def secret_scan_with_bloom_filter(bloom_filter):
    potential_keys = generate_and_filter_keys(bloom_filter, 1000000)
    
    # Дальнейшая проверка потенциальных ключей с помощью SecretScan
    for key in potential_keys:
        # Логика проверки ключа с помощью SecretScan
        if is_valid_key(key):  # Функция проверки ключа
            print(f"Найден потенциальный ключ: {key}")

# Настройки Bloom filter
bf_size = 1000000
bf_hash_count = 7

bloom_filter = BloomFilter(bf_size, bf_hash_count)

# Добавление известных ключей в Bloom filter
known_keys = ["key1", "key2"]
for key in known_keys:
    bloom_filter.add(key)

secret_scan_with_bloom_filter(bloom_filter)

Step 3: GPU Optimization

To speed up the process, you can use GPU to generate and verify keys. This can be done using the library pycuda.

pythonimport pycuda.autoinit
import pycuda.driver as cuda
from pycuda.compiler import SourceModule

# Пример кода для ускорения генерации и проверки ключей на GPU
mod = SourceModule("""
    __global__ void check_keys(float *keys, int size) {
        int idx = blockIdx.x * blockDim.x + threadIdx.x;
        if (idx < size) {
            // Здесь должна быть логика проверки ключа
            // Например, проверка на соответствие определенным критериям
            // keys[idx] = ...;
        }
    }
""")

def generate_and_check_keys_on_gpu(size):
    # Инициализация массива ключей на GPU
    keys_gpu = cuda.mem_alloc(size * np.float32().nbytes)
    
    # Вызов функции на GPU
    func = mod.get_function("check_keys")
    func(keys_gpu, np.int32(size), block=(256,1,1), grid=(size//256+1,1))
    
    # Выборка данных из GPU
    keys = np.empty(size, dtype=np.float32)
    cuda.memcpy_dtoh(keys, keys_gpu)
    
    return keys

Step 4: Integrate with GPU Acceleration

Use Bloom filter to filter keys on CPU and then send the remaining keys to GPU for further verification.

pythondef secret_scan_with_bloom_and_gpu(bloom_filter):
    potential_keys = generate_and_filter_keys(bloom_filter, 1000000)
    
    # Ускоренная проверка оставшихся ключей на GPU
    checked_keys = generate_and_check_keys_on_gpu(len(potential_keys))
    
    # Обработка результатов
    for key in checked_keys:
        # Логика обработки проверенных ключей
        pass

secret_scan_with_bloom_and_gpu(bloom_filter)

This scheme allows using Bloom filter to pre-filter keys and then using GPU acceleration to further check the remaining keys.

What are the advantages of using GPU in SecretScan compared to CPU?

Using GPU in SecretScan to find private keys has several advantages over using CPU :

Advantages of GPU

  1. Parallel processing :
    • The GPU is capable of performing thousands of operations simultaneously, making it ideal for tasks that require high computing power and parallelism 1 5 .
    • This allows the process of finding private keys to be significantly accelerated, as the GPU can process a large number of potential keys simultaneously.
  2. High performance :
    • GPUs provide higher performance than CPUs , especially when performing repetitive calculations such as matrix operations or cryptographic tasks 2 6 .
    • This is especially important in cryptography, where a huge number of combinations must be processed to find the correct key.
  3. Efficiency for Big Data :
    • GPUs are better suited for working with large amounts of data, which is relevant for tasks where millions of potential keys need to be checked 5 8 .
    • This allows us to reduce the time it takes to find private keys and improve the efficiency of the process.
  4. Wideband data bus :
    • The GPU has a wide data bus, which allows large amounts of information to be quickly transferred between stream processors and video memory 4 .
    • This speeds up data transfer and computation, which is important for tasks that require fast data processing.

Limitations and Considerations

  1. Power consumption :
    • The GPU can consume more power than the CPU , especially under maximum load 4 7 .
    • This may increase the cost of operating the system.
  2. Need for optimization :
    • Some tasks require optimization to run on the GPU , which may require additional effort and knowledge 4 8 .
    • Not all algorithms can be easily parallelized, which can reduce the efficiency of the GPU in some cases.

Overall, using GPU in SecretScan can significantly speed up the process of finding private keys due to parallel processing and high performance, but requires optimization and may have higher energy costs.


What technologies like CUDA improve GPU performance in SecretScan

Technologies such as CUDA significantly improve GPU performance in SecretScan and other applications that require parallel computing. Here are some key technologies and their benefits:

1. CUDA (Compute Unified Device Architecture)

  • Advantages : CUDA enables highly parallel computing on GPUs, making it ideal for data-intensive tasks such as SecretScan’s private key search.
  • Features : Supports programming languages ​​C++, Python, Fortran and others. Allows efficient use of memory and optimization of calculations for specific tasks 1 2 4 .

2. NVLink

  • Benefits : Provides high-speed memory access and allows tasks to be divided between multiple GPUs, improving performance when processing big data 1 .
  • Features : Used to accelerate data exchange between GPU and system memory.

3. Dynamic optimization

  • Benefits : Allows the GPU to adapt to the unique characteristics of each task, improving performance and reducing power consumption 1 .
  • Features : Optimizes code execution on the GPU depending on the specific task.

4. CUBLAS and CUFFT

  • Advantages : These libraries provide optimized implementations of algebraic operations and fast Fourier transforms, which can be useful for cryptographic tasks 2 .
  • Features : Used to speed up calculations involving matrices and Fourier transforms.

5. SIMD (Single Instruction, Multiple Data)

  • Advantages : Allows multiple processors to perform the same operation on different data simultaneously, making it ideal for parallel computing on GPUs 3 .
  • Features : Used in the CUDA architecture to organize parallel computing.

Together, these technologies make it possible to use GPU resources as efficiently as possible to accelerate computations in SecretScan and other applications.

How GPU Speeds Up SecretScan’s Private Key Search Process

GPUs speed up the process of finding private keys in SecretScan due to their ability to perform parallel computations, allowing them to process a huge number of potential keys at once. Here’s how it works:

1. Parallel processing

  • The GPU can perform thousands of operations simultaneously, making it ideal for tasks that require high computing power and parallelism.
  • This allows to significantly speed up the process of generating and verifying private keys, since the GPU can process a large number of keys simultaneously.

2. High performance

  • GPU provides higher performance than CPU , especially when performing repetitive calculations such as cryptographic tasks.
  • This is especially important in private key mining, where a huge number of combinations must be processed to find the correct key.

3. Efficiency for Big Data

  • GPUs are better suited for working with large amounts of data, which is relevant for tasks where millions of potential keys need to be checked.
  • This allows us to reduce the time it takes to find private keys and improve the efficiency of the process.

4. Acceleration technologies

  • CUDA and other technologies make it possible to efficiently use GPU resources to accelerate computing.
  • This includes optimized libraries for matrix operations and other calculations, which can be useful for cryptographic tasks.

Example of acceleration

  • In SecretScan, using a GPU can increase the speed of private key searches several times compared to a CPU. For example, an Nvidia 2080Ti graphics card can process up to 250 Mk/s (millions of keys per second), which is significantly faster than any CPU 1 .

Thus, GPU accelerates the process of private key search in SecretScan due to parallel processing, high performance and efficiency when working with big data.

What errors can occur when using GPU for SecretScan and how to fix them

When using GPU for SecretScan , various errors may occur, which can be divided into several categories. Below are some of the most common errors and how to fix them:

1. GPU Memory Errors

  • Reason : These errors occur due to unstable operation of the video card, especially when overclocking or using flashed timings. The video card can correct single errors, but if there are several errors, this can lead to system failures 1 .
  • Correction :
    • Check the stability of the video card : Make sure that the video card is working stably at standard frequencies.
    • Roll Back Drivers : If the problem occurs after updating your drivers, try rolling back to the previous version.
    • Temperature Check : Make sure the cooling system is working properly and the graphics card is not overheating.

2. Problems with drivers

  • Reason : Outdated or incompatible drivers may cause errors when working with GPU 3 .
  • Correction :
    • Update Drivers : Install the latest drivers for your graphics card.
    • Check Compatibility : Make sure the drivers are compatible with your operating system and applications.

3. Nutritional problems

  • Reason : Insufficient power supply may cause unstable operation of the video card.
  • Correction :
    • Check the power supply : Make sure the power supply can provide the required power to the graphics card.
    • Power Settings : If possible, adjust the power settings in the BIOS or through software.

4. Software problems

  • Reason : Program errors or conflicts with other applications may cause problems.
  • Correction :
    • Update the program : Install the latest version of SecretScan.
    • Check for conflicts : Close other applications that may conflict with SecretScan.

5. Cooling problems

  • Reason : Overheating of the video card can cause errors and unstable operation.
  • Correction :
    • Check the cooling system : Make sure the cooling system is working properly.
    • Clean Dust : Clean dust from the cooling system regularly.

6. Configuration issues

  • Reason : Incorrect configuration settings may cause errors.
  • Correction :
    • Check Configuration : Make sure SecretScan configuration and GPU settings are correct.
    • Reset settings : If possible, reset to factory settings.

Solving GPU issues in SecretScan requires careful diagnostics and system tuning to ensure stable operation.

Citations:

  1. https://miningclub.info/threads/gpu-memory-errors-i-ego-rasshifrovka.48767/
  2. https://store.steampowered.com/app/2679860/Space_Miner__Idle_Adventures/?l=russian
  3. https://ya.ru/neurum/c/tehnologii/q/pochemu_voznikayut_oshibki_pri_ispolzovanii_feb42a65
  1. https://secretscan.ru
  2. https://bitcointalk.org/index.php?topic=4865693.20
  1. https://www.nic.ru/help/tehnologiya-cuda-chto-eto-i-kak-ispol6zuetsya_11415.html
  2. https://help.reg.ru/support/servery-vps/oblachnyye-servery/ustanovka-programmnogo-obespecheniya/chto-takoye-cuda
  3. https://habr.com/ru/companies/epam_systems/articles/245503/
  4. https://ru.wikipedia.org/wiki/CUDA
  1. http://oberset.ru/ru/blog/cpu-vs-gpu-for-ai
  2. https://www.xelent.ru/blog/cpu-i-gpu-v-chem-raznitsa/
  3. https://www.fastvideo.ru/blog/cpu-vs-gpu-fast-image-processing.htm
  4. https://timeweb.cloud/blog/gpu-i-cpu-v-chem-raznica
  5. https://sky.pro/wiki/javascript/gpu-vs-cpu-klyuchevye-razlichiya-i-vybor-dlya-zadach/
  6. https://aws.amazon.com/ru/compare/the-difference-between-gpus-cpus/
  7. https://mws.ru/blog/cpu-i-gpu-v-chem-otlichie/
  8. https://habr.com/ru/articles/818963/
  1. https://secretscan.ru
  2. https://secretscan.org/programs
  3. https://freelancehunt.com/project/napisat-skript-dlya-vyichisleniya-na-gpu/1137898.html
  4. https://www.altcoinstalks.com/index.php?topic=50707.0
  1. https://www.reg.ru/blog/kak-uskorit-data-science-s-pomoshchyu-gpu/
  2. https://habr.com/ru/companies/timeweb/articles/853578/
  3. https://python.su/forum/post/230341/
  4. https://habr.com/ru/articles/276569/
  5. https://github.com/cheahjs/warframe_data/blob/master/ru/strings.json
  1. https://www.reg.ru/blog/kak-uskorit-data-science-s-pomoshchyu-gpu/
  2. https://habr.com/ru/companies/timeweb/articles/853578/
  3. https://python.su/forum/post/230341/
  4. https://github.com/cheahjs/warframe_data/blob/master/ru/strings.json
  5. https://habr.com/ru/articles/276569/

Source code

Telegram: https://t.me/cryptodeeptech

Video: https://youtu.be/i9KYih_ffr8

Video tutorial: https://dzen.ru/video/watch/6784be61b09e46422395c236

Source: https://cryptodeeptech.ru/discrete-logarithm

Useful information for enthusiasts:

How to Use SecretScan with GPU to Speed ​​Up Bitcoin Wallet Private Key Mining with Bloom Filter Algorithm