Learning CUDA with a Weak GPU or No GPU at All? Yes, You Can!

26 Jan, 2025

Hello everyone!

Today, I want to discuss a question I often receive, especially after starting my 100 Days of CUDA Challenge. It’s a question that CUDA learners wonder about, and it’s critical for those who don’t have access to high-end hardware. The question is:

Can I learn CUDA with a weak GPU—or without a GPU at all?

In this post, which will be a quick and straightforward guide, I'll show you that you absolutely can! And the best part is—you can do it using Google Colab, a platform that is free for most users.

For many, Colab seems limited to Python applications exclusively, but that’s not true. With a few simple tweaks, you can use it for other programming environments, including CUDA development. Let’s get started!

Using Colab for CUDA: A Step-by-Step Guide

This section will include hands-on steps to help you compile and run CUDA code in Colab.

Step 1: Write a CUDA File in Colab

To begin, write your CUDA code in a cell. But to save it as a .CU file, you’ll need to include a special prefix on the cell. Here's how:

1. In the first cell, write your CUDA code, but before the code, include the following line:

   %%writefile vector_multiplication.cu
   

   Replace vector_multiplication.cu with the desired name of your CUDA file.

   Example Code for CUDA File:

code:

%%writefile vector_multiplication.cu

#include <stdio.h>
__global__ void multiplyVectors(float *a, float *b, float *c, int n) {
    int idx = threadIdx.x + blockIdx.x * blockDim.x;
    if (idx < n) {
        c[idx] = a[idx] * b[idx];
    }
}

int main() {
    const int n = 512;
    float a[n], b[n], c[n];
    int size = n * sizeof(float);

    float *dev_a, *dev_b, *dev_c;
    cudaMalloc((void **)&dev_a, size);
    cudaMalloc((void **)&dev_b, size);
    cudaMalloc((void **)&dev_c, size);

    for (int i = 0; i < n; ++i) {
        a[i] = b[i] = i;
    }

    cudaMemcpy(dev_a, a, size, cudaMemcpyHostToDevice);
    cudaMemcpy(dev_b, b, size, cudaMemcpyHostToDevice);

    multiplyVectors<<<2, 256>>>(dev_a, dev_b, dev_c, n);
    cudaMemcpy(c, dev_c, size, cudaMemcpyDeviceToHost);

    cudaFree(dev_a);
    cudaFree(dev_b);
    cudaFree(dev_c);

    for (int i = 0; i < 10; ++i) {
        printf("c[%d] = %f\n", i, c[i]);
    }
    return 0;
}

Step 2: Compile Your CUDA File

After creating your .cu file, you’ll need to compile it. To do this, you’ll use nvcc, which is the NVIDIA CUDA Compiler. In a new code cell, write the following:

code:

!nvcc vector_multiplication.cu -o vector_multiplication

Try adding -arch=sm_75 (for T4 gpu) to the command since sometimes you need to tell the compiler what architecture you are working with!

This command tells Colab to compile the vector_multiplication.cu file into an executable named vector_multiplication.

Step 3: Run Your Compiled CUDA Code

Now that your code is compiled, you can run it with the following command in a new cell:

code:

!./vector_multiplication

And voila! You’ve successfully compiled and executed CUDA code on Colab.

Why Use Colab for CUDA Learning?

1. Accessibility:
   If you don’t have access to a powerful GPU, Colab allows you to use free NVIDIA GPUs for your CUDA learning journey.

2. Cost-Effective:
   Most users can take advantage of Colab’s free-tier GPUs without spending a dime.

3. Flexibility:
   Using the method above, you can also run other programming languages or frameworks—not just CUDA.

Helpful Tips

• Check GPU Availability in Colab:
  Before you proceed, ensure that your Colab environment is set up with GPU support. To check, run the following:

  !nvidia-smi
  

  If your GPU is correctly allocated, you’ll see the GPU model and memory details here:

• Stay Within Limits:
  Colab’s free tier has resource restrictions. Avoid running on GPU unless you are going to use it.

Here's What I Learned

Learning CUDA doesn’t require an expensive or powerful GPU. Platforms like Colab have made high-performance computing more accessible than ever. By using simple hacks like the ones I described above, you can explore CUDA programming without needing to invest in specialized hardware.

Final Thoughts

I hope this quick tutorial shows you that having a weak GPU—or no GPU at all—isn’t a barrier to entering the CUDA world. With tools like Colab, learning CUDA becomes accessible and straightforward for learners and enthusiasts everywhere.

Thank you for reading, and best of luck with your CUDA learning journey. Feel free to ask me any questions.

Until next time, happy coding!

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