To measure metrics/events for CUDA programs, I have tried using the command line like:
nvprof --metrics <<metric_name>>
I also measured the same metrics on the Visual profiler nvvp. I noticed no difference in the values I get.
I noticed a difference in output when I choose a metric like achieved_occupancy. But this varies with every execution and that's probably why I get different results each time I run it, irrespective of whether I am using nvvp or nvprof.
The question:
I was under the impression that nvvp and nvprof are exactly the same, and that nvvp is simply a GUI built on top of nvprof for ease of use. However I have been given this advice:
Always use the visual profiler. Never use the command line.
Also, this question says:
I do not want to use the command line profiler as I need the global load/store efficiency, replay and DRAM utilization, which are much more visible in the visual profiler.
Apart from 'dynamic' metrics like achieved_occupancy, I never noticed any differences in results. So, is this advice valid? Is there some sort of deficiency in the way nvprof works? I would like to know the advantages of using the visual profiler over the command line form, if there are any.
More specifically, are there metrics for which nvprof gives wrong results?
Note:
My question is not the same as this or this because these are asking about the difference between nvvp and Nsight.
I'm not sure why someone would give you the advice:
Never use the command line.
assuming by "command line" you do in fact mean nvprof. That's not sensible. There are situations where it makes sense to use nvprof. (Note that if you actually meant the command line profiler, then that advice might be somewhat sensible, although still a matter of preference. It is separate from nvprof so has a separate learning curve. I personally would use nvprof instead of the command line profiler.)
nvvp uses nvprof under the hood, in order to do all of its measurement work. However nvvp may combined measured metrics in various interesting ways, e.g. to facilitate guided analysis.
nvprof should not give you "wrong results", and if it did for some reason, then nvvp should be equally susceptible to such errors.
Use of nvvp vs. nvprof may be simply a matter of taste or preference.
Many folks will like the convenenience of the GUI. And the nvvp GUI offers a "Guided Analysis" mode which nvprof does not. I'm sure there could be created an exhaustive list of other differences if you go through the documentation. But whatever nvvp does, it does it using nvprof. It doesn't have an alternate method to query the device for profiler data -- it uses nvprof.
I would use nvprof when it's inconvenient to use nvvp, perhaps when I am running on a compute cluster node where it's difficult or impossible to launch nvvp. You might also use it if you are doing targetted profiling (measuring a single metric, e.g. shared_replay_overhead - nvprofis certainly quicker than firing up the GUI and running a session), or if you are collecting metrics for tabular generation over a large series of runs.
In most other cases, I personally would use nvvp. The timeline feature itself is hugely more convenient than trying to assemble a sequence in your head from the output of nvprof --print-gpu-trace ... which is essentially the same info as the timeline.