@@ -44,9 +44,10 @@ Combined for GPU is a big difference when measuring with measureExecTimeIdempote
Separable can possibly be parallelized better than combined.
#### Question 2.1: Which version of the averaging filter (unified, separable) is the most efficient? Why?
Separable, because it can be parallelized better.
Separable, fewer calculations and it can be parallelized better.
#### Question 3.1: In data-parallel skeletons like MapOverlap, all elements are processed independently of each other. Is this a good fit for the median filter? Why/why not?
Yes, it is a good fit because all pixels are independent of each other.
Could be more efficient if the current pixel checked its neighbours instead of processing it independently. With median filtering, large areas of almost the same color appears.
#### Question 3.2: Describe the sequence of instructions executed in your user-function. Is it data dependent? What does this mean for e.g., automatic vectorization, or the GPU backend?
