I am not sure we are doing the Team work or individual. But since I did not hear anything from my team mate I decided to go with my first assignment. I met many difficulties to adapt the existing c++ code to transfer some computations on GPU. In my first assignment I am doing different manipulations with the image. Image is a class. And this is one of the reason of delay with my second assignment. Apparently, I can't pass class to the Kernel. Kernel accepts only low types variable. So all the time I was trying different approaches how to parallelize my code. I even bought a new computer with CUDA compatible GPU card to be able to spend more time on the tasks. And finally, I found that method image.negate() (not image rotation) would be easy to try to compute on Kernel.I decided to make 1000 negates of image to make sense of parallelyzing the code. I rewrote some code in my first assignment, remade the profile for first assignment (1000 negate operations). It took around 13 seconds on Linux. I profiled my code with Kernel computations on CUDA profiler, and.....it took around 8 seconds....So here is my new profile for assignment 1: