What is shared memory and why use it?

Shared memory in the context of GPU and CUDA is a memory region accessible to all threads within a single thread block. It resides on the GPU itself and is much faster than global memory because it has low latency and high bandwidth.

Using shared memory allows efficient data exchange between threads within a block without the need to access slow global memory. This is especially important for machine learning algorithms and parallel computations, where sharing intermediate results is often required.

Example: when implementing convolution or matrix operations, data can first be loaded into shared memory, computations performed, and then the result stored back in global memory, significantly speeding up execution.

CUDA code example:

__shared__ float tile[32][32];

int tx = threadIdx.x;
int ty = threadIdx.y;

// Load data from global memory into shared memory
tile[ty][tx] = input[y * width + x];
__syncthreads();

// Use data from shared memory for calculations
float val = tile[ty][tx] * 2.0f;

// Write the result back to global memory
output[y * width + x] = val;

Thus, shared memory helps optimize performance by reducing the number of slow global memory accesses.