LoginWhat is NVIDIA’s CUDA and How is it Used in Cybersecurity?
Welcome back my aspiring cyberwarriors!
You have likely heard of the company NVIDIA. Not only are the dominant company in computer graphics adapters (if you are gamer, you likely have one) and now, artificial intelligence. In recent weeks, they have become the most valuable company in the world ($5 trillion).
The two primary reasons that Nvidia has become so important to artificial intelligence are:
- Nvidia chips can process data in multiple threads, in some cases, thousands of threads. This makes doing complex calculations in parallel possible, making them much faster.
- Nvidia created a development environment named CUDA for harnessing the power of these powerful CPU’s. This development environment is a favorite among artificial intelligence, data analytics, and cybersecurity professionals.
Let’s a brief moment to examine this powerful environment.
What is CUDA?
Most computers have two main processors:
CPU (Central Processing Unit): General-purpose, executes instructions sequentially or on a small number of cores. These CPU’s such as Intel and AMD provide the flexibility to run many different applications on your computer.
GPU (Graphics Processing Unit): These GPU’s were originally designed to draw graphics for applications such as games and VR environments. These GPU’s contain hundreds or thousands of small cores that excel at doing the same thing many times in parallel.
CUDA (Compute Unified Device Architecture) is NVIDIA’s framework that lets you take control of the GPU for general computing tasks. In other words, CUDA lets you write code that doesn’t just render graphics—it crunches numbers at massive scale. That’s why it’s a favorite for machine learning, password cracking, and scientific computing.
Why Should Hackers & Developers Care?
CUDA matters as an important tool in your cybersecurity toolkit because:
Speed: A GPU can run password hashes or machine learning models orders of magnitude faster than a CPU.
Parallelism: If you need to test millions of combinations, analyze huge datasets, or simulate workloads, CUDA gives you raw power.
Applications in Hacking: Tools like Hashcat and Pyrit use CUDA to massively accelerate brute-force and dictionary attacks. Security researchers who understand CUDA can customize or write their own GPU-accelerated tools.
The CUDA environment sees the GPU as a device with:
Threads: The smallest execution unit (like a tiny worker).
Blocks: Groups of threads.
Grids: Groups of blocks.
Think of it like this:
- A CPU worker can cook one meal at a time.
- A GPU is like a kitchen with thousands of cooks—we split the work (threads), organize them into brigades (blocks), and assign the whole team to the job (grid).
Coding With CUDA
CUDA extends C/C++ with some keywords.
Here’s the simple workflow:
- You write a kernel function (runs on the GPU).
- You call it from the host code (the CPU side).
- Launch thousands of threads in parallel → GPU executes them fast.
Example skeleton code:
c__global__ void add(int *a, int *b, int *c) {
int idx = threadIdx.x;
c[idx] = a[idx] + b[idx];
}
int main() {
// Allocate memory on host and device
// Copy data to GPU
// Run kernel with N threads
add<<<1, N>>>(dev_a, dev_b, dev_c);
// Copy results back to host
}
The keywords:
__global__→ A function (kernel) run on the GPU.threadIdx→ Built-in variable identifying which thread you are.<<<1, N>>>→ Tells CUDA to launch 1 block of N threads.
This simple example adds two arrays in parallel. Imagine scaling this to millions of operations at once!
The CUDA Toolchain Setup
If you want to try CUDA make certain you have the following items:
1. an NVIDIA GPU.
2. the CUDA Toolkit (contains compiler nvcc).
3. Write your CUDA programs in C/C++ and compile it with nvcc.
Run and watch your GPU chew through problems.
To install the CUDA toolkit in Kali Linux, simply enter;
kali > sudo apt install nvidia-cuda-toolkit
Next, write your code and compile it with nvcc, such as;
kali > nvcc hackersarise.cu -o hackersarise
Practical Applications of CUDA
CUDA is already excelling at hacking and computing applications such as;
- Password cracking (Hashcat, John the Ripper with GPU support).
- AI & ML (TensorFlow/PyTorch use CUDA under the hood). Our application of using Wi-Fi to see through walls uses CUDA.
- Cryptanalysis (breaking encryption) & simulation tasks.
- Network packet analysis at high scale.
As a beginner, start with small projects—then explore how to take compute-heavy tasks and offload them to the GPU.
Summary
CUDA is NVIDIA’s way of letting you program GPUs for general-purpose computing. To the hacker or cybersecurity pro, it’s a way to supercharge computation-heavy tasks.
Learn the thread-block-grid model, write simple kernels, and then think: what problems can I solve dramatically faster if run in parallel?
