CPU and GPU timer in cuda visual profiler

So there are 2 timers in cuda visual profiler,

GPU Time: It is the execution time for the method on GPU. CPU Time:It is sum of GPU time and CPU overhead to launch that Method. At driver generated data level, CPU Time is only CPU overhead to launch the Method for non-开发者_StackOverflowblocking Methods; for blocking methods it is sum of GPU time and CPU overhead. All kernel launches by default are non-blocking. But if any profiler counters are enabled kernel launches are blocking. Asynchronous memory copy requests in different streams are non-blocking.

If I have a real program, what's the actual exectuion time? I measure the time, there is a GPU timer and a CPU timer as well, what's the difference?

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You're almost there -- now that you're aware of some of the various options, the final step is to ask yourself exactly what time you want to measure. There's no right answer to this, because it depends on what you're trying to do with the measurement. CPU time and GPU time are exactly what you want when you are trying to optimize computation, but they may not include things like waiting that actually can be pretty important. You mention “the actual exectuion time” — that's a start. Do you mean the complete execution time of the problem — from when the user starts the program until the answer is spit out and the program ends? In a way, that's really the only time that actually matters.

For numbers like that, in Unix-type systems I like to just measure the entire runtime of the program; /bin/time myprog, presumably there's a Windows equivalent. That's nice because it's completely unabigious. On the other hand, because it's a total, it's far too broad to be helpful, and it's not much good if your code has a big GUI component, because then you're also measuring the time it takes for the user to click their way to results.

If you want elapsed time of some set of computations, cuda has very handy functions cudaEvent* which can be placed at various parts of the code — see the CUDA Best Practices Guide, s 2.1.2, Using CUDA GPU Timers — these you can put before and after important bits of code and print the results.

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gpu timer is based on events. that means that when an event is created it will be set in a queue at gpu for serving. so there is a small overhead there too. from what i have measured though the differences are of minor importance