In convolution, two mathematical functions are combined to produce a third function. In image processing functions are usually called kernels. A kernel is nothing more than a (square) array of pixels (a small image so to speak). Usually, the values in the kernel add up to one. This is to make sure no energy is added or removed from the image after the operation.
Specifically, a Gaussian kernel (used for Gaussian blur) is a square array of pixels where the pixel values correspond to the values of a Gaussian curve (in 2D).
Each pixel in the image gets multiplied by the Gaussian kernel. This is done by placing the center pixel of the kernel on the image pixel and multiplying the values in the original image with the pixels in the kernel that overlap. The values resulting from these multiplications are added up and that result is used for the value at the destination pixel. Looking at the image, you would multiply the value at (0,0) in the input array by the value at (i) in the kernel array, the value at (1,0) in the input array by the value at (h) in the kernel array, and so on. and then add all these values to get the value for (1,1) at the output image.
To answer your second question first, the larger the kernel, the more expensive the operation. So, the larger the radius of the blur, the longer the operation will take.
To answer your first question, as explained above, convolution can be done by multiplying each input pixel with the entire kernel. However, if the kernel is symmetrical (which a Gaussian kernel is) you can also multiply each axis (x and y) independently, which will decrease the total number of multiplications. In proper mathematical terms, if a matrix is separable it can be decomposed into (M×1) and (1×N) matrices. For the Gaussian kernel above this means you can also use the following kernels:
You would now multiply each pixel in the input image with both kernels and add the resulting values to get the value for the output pixel.
For more information on how to see if a kernel is separable, follow this link.
Edit: the two kernels shown above use slightly different values. This is because the (sigma) parameter used for the Gaussian curve to create these kernels were slightly different in both cases. For an explanation on which parameters influence the shape of the Gaussian curve and thus the values in the kernel follow this link
Edit: in the second image above it says the kernel that is used is flipped. This of course only makes any difference if the kernel you use is not symmetric. The reason why you need to flip the kernel has to do with the mathematical properties of the convolution operation (see link for a more in depth explanation on convolution). Simply put: if you would not flip the kernel, the result of the convolution operation will be flipped. By flipping the kernel, you get the correct result.