Is there any procedure to find the Cuda platform-agnostic execution time of a certain inference operation? I want to compare the execution time of two different inference operations. But, the execution time depends on the already existing load on the GPU and many other factors. I tried the profiler example given here. But, it shows different CUDA times for each independent trial on the same GPU. And, the CUDA times are completely different if I change the GPU. Therefore, it is not an accurate estimation. In computer science, we find the number of CPU cycles to compare the performance of two algorithms. Is there a similar analogy for GPU using PyTorch?