I think the tree view rather than the graph would be easier to understand.

> On Jan 14, 2019, at 6:34 PM, Kostis Kaffes <[email protected]> wrote:
> 
> Thanks! I have tried the per thread option. Attached you will find a call 
> graph and see what I mean by convoluted. There are also some unknowns that do 
> not help the situation.
> 
> I am using the in-process transport in order to avoid being dominated by IO. 
> My goal is to see if it is feasible to lower gRPC latency to a few μs and 
> what that might require.
> Hence, even small overheads might matter.
> 
> On Monday, January 14, 2019 at 4:16:21 PM UTC-8, robert engels wrote:
> But I will also save you some time - it is a fraction of the time spent in IO 
> - so don’t even both measuring it. gRPC is simple buffer translation at its 
> heart - trivially simple.
> 
> MAYBE if you had a super complex protocol message you could get it to 
> register CPU time in those areas with any significance in compared to the IO 
> time, but doubtful.
> 
> By IO time, I mean even on a local machine with no “physical network”.
> 
> Any CPU time used will be dominated by malloc/free - so a no dynamic memory 
> messaging system will probably out perform gRPC - but still it will be 
> dominated by the IO.
> 
> This is based on my testing of gRPC in Go.
> 
> 
> 
>> On Jan 14, 2019, at 6:11 PM, robert engels <ren...@ <>earthlink.net 
>> <http://earthlink.net/>> wrote:
>> 
>> If you use the “perf report per thread” you should have all the information 
>> you need, unless you are using a single threaded test.
>> 
>> Stating “convoluted” doesn’t really help - maybe an example of what you mean?
>> 
>>> On Jan 14, 2019, at 5:59 PM, Kostis Kaffes <kka...@ <>stanford.edu 
>>> <http://stanford.edu/>> wrote:
>>> 
>>> Hi folks,
>>> 
>>> As part of a research project, I am trying to benchmark a C++ gRPC 
>>> application. More specifically, I want to find out how much time is spent 
>>> in each layer of the stack as it is described here 
>>> <https://grpc.io/blog/grpc-stacks>. I tried using perf but the output is 
>>> too convoluted. Any idea on tools I could use or existing results on this 
>>> type of benchmarking?
>>> 
>>> Thanks!
>>> Kostis
>>> 
>>> 
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