Writing GPU kernels used to feel like wizardry—arcane knowledge understood by few. But it’s really about unlocking fine-grained control over memory and threads, typically via CUDA (NVIDIA GPUs), Triton (OpenAI’s language for custom kernels), or ROCm (AMD GPUs).
Here’s a quick dive into CUDA, using examples to illustrate key optimization techniques:
- Vectorization
Simultaneously process contiguous data elements to reduce latency.
__global__ void vectorize_add(float *a, float *b, float *c, int n) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;