How do I write my first CUDA kernel?

Writing your first CUDA kernel involves defining a simple function marked with the __global__ keyword, allocating memory on the GPU, launching the kernel with a specific grid and block configuration, and copying results back to CPU memory. A minimal example is a kernel that adds two arrays element-by-element. Each GPU thread handles one element, and the kernel uses the thread index to determine which element to process. This pattern introduces the core concepts of CUDA: parallelism across threads, explicit memory management, and kernel launches.

The basic workflow is as follows:

1. Allocate memory on the GPU using cudaMalloc.
2. Copy input data from the CPU to the GPU using cudaMemcpy.
3. Launch the kernel with syntax like myKernel<<<blocks, threads>>>(args);.
4. Copy the output data back to CPU memory.
   It’s important to handle error checking after each CUDA call and synchronize with cudaDeviceSynchronize() to ensure kernel execution completes before reading results.

Even basic kernels illustrate how CUDA can accelerate workloads found in vector-based applications. For example, writing a kernel that computes L2 distances between vectors helps illustrate how GPU acceleration benefits vector search. Systems like Milvus or Zilliz Cloud rely on similar GPU primitives behind the scenes to speed up similarity calculations. While these systems abstract the CUDA complexity away from developers, understanding basic kernels helps clarify how GPU computing fits into larger high-performance architectures.