[Typo] In the Interesting Kernel Section

[aymuos15]

Interesting Kernels
Diagonal Matrix Multiplication

Problem 13 in level 1 involves multiplying a matrix by another diagonal matrix:

torch.diag(A) @ B
torch.diag() takes in a vector of the diagonal elements of a matrix and returns a 2-D square tensor with the elements of input as the diagonal. The result is a matrix-matrix multiplication.

Mathematically, multiplying a matrix by a diagonal matrix is equivalent to scaling each row (or column, if the diagonal matrix is on the right side) of the original matrix by the corresponding diagonal element. As a result, the diagonal matrix doesn’t need to be explicitly constructed, reducing both memory usage and computational overhead.

This is the problem that gets the >12x speedup over torch and torch.compile() in level 1 for multiple models, one example of these generated CUDA kernel is below:

__global__ void diag_matmul_kernel(
    const float* diag,
    const float* mat,
    float* out,
    const int N,
    const int M) {
    
    const int row = blockIdx.y * blockDim.y + threadIdx.y;
    const int col = blockIdx.x * blockDim.x + threadIdx.x;
    
    if (row < N && col < M) {
        out[row * M + col] = diag[row] * mat[row * M + col];
    }
}

I think this should be problem 12?