How does OpenMP actually reduce clock cycles?

Question

It might be a silly question but, with OpenMP you can achieve to distribute the number of operations between all the cores your CPU has. Of course, it is going to be faster in 99% times because you went from a single core doing N operations to K cores doing the same amount operations at the same time.

Despite of this, the total amount of clock cycles should be the same, right? Because the number of operations is the same. Or I am wrong?

Answer 1

I think the answer depends on how you define the total amount of clock cycles. If you define it as the sum of all the clock cycles from the different cores then you are correct and there will not be fewer clock cycles. But if you define it as the amount of clock cycles for the "main" core between initiating and completing the distributed operations then it is hopefully fewer.

Answer 2

This question boils down more or less to the difference between CPU time and elapsed time. Indeed, we see more often than none here questions which start by "my code doesn't scale, why?", for which the first answer is "How did you measure the time?" (I let you make a quick search and I'm sure you'll find many results)

But to illustrate more how things work, let's imagine you have a fixed-size problem, for which you have an algorithm that is perfectly parallelized.You have 120 actions to do, each taking 1 second. Then, 1 CPU core would take 120s, 2 cores would take 60s, 3 cores 40s, etc. That is the elapsed time that is decreasing. However, 2 cores, running for 60 seconds in parallel, will consume 120s of CPU time. This means that the overall number of clock cycles won't have reduced compared to having only one CPU core running.

In summary, for a perfectly parallelized problem, you expect to see your elapsed time scaling down perfectly with the number of cores used, and the CPU time to remain constant.

In reality, what you often see is the elapsed time to scale down less than expected, due to parallelization overheads and/or imperfect parallelization. By the meantime, you see the CPU time slightly increasing with the number of cores used, for the same reasons.