I am interested in bench-marking different parts of my program for speed. I having tried us开发者_StackOverflow社区ing info(statistics)
and erlang:now()
I need to know down to the microsecond what the average speed is. I don't know why I am having trouble with a script I wrote.
It should be able to start anywhere and end anywhere. I ran into a problem when I tried starting it on a process that may be running up to four times in parallel.
Is there anyone who already has a solution to this issue?
EDIT:
Willing to give a bounty if someone can provide a script to do it. It needs to spawn though multiple process'. I cannot accept a function like timer.. at least in the implementations I have seen. IT only traverses one process and even then some major editing is necessary for a full test of a full program. Hope I made it clear enough.
Here's how to use eprof, likely the easiest solution for you:
First you need to start it, like most applications out there:
23> eprof:start().
{ok,<0.95.0>}
Eprof supports two profiling mode. You can call it and ask to profile a certain function, but we can't use that because other processes will mess everything up. We need to manually start it profiling and tell it when to stop (this is why you won't have an easy script, by the way).
24> eprof:start_profiling([self()]).
profiling
This tells eprof to profile everything that will be run and spawned from the shell. New processes will be included here. I will run some arbitrary multiprocessing function I have, which spawns about 4 processes communicating with each other for a few seconds:
25> trade_calls:main_ab().
Spawned Carl: <0.99.0>
Spawned Jim: <0.101.0>
<0.100.0>
Jim: asking user <0.99.0> for a trade
Carl: <0.101.0> asked for a trade negotiation
Carl: accepting negotiation
Jim: starting negotiation
... <snip> ...
We can now tell eprof to stop profiling once the function is done running.
26> eprof:stop_profiling().
profiling_stopped
And we want the logs. Eprof will print them to screen by default. You can ask it to also log to a file with eprof:log(File)
. Then you can tell it to analyze the results. We tell it to collapse the run time from all processes into a single table with the option total
(see the manual for more options):
27> eprof:analyze(total).
FUNCTION CALLS % TIME [uS / CALLS]
-------- ----- --- ---- [----------]
io:o_request/3 46 0.00 0 [ 0.00]
io:columns/0 2 0.00 0 [ 0.00]
io:columns/1 2 0.00 0 [ 0.00]
io:format/1 4 0.00 0 [ 0.00]
io:format/2 46 0.00 0 [ 0.00]
io:request/2 48 0.00 0 [ 0.00]
...
erlang:atom_to_list/1 5 0.00 0 [ 0.00]
io:format/3 46 16.67 1000 [ 21.74]
erl_eval:bindings/1 4 16.67 1000 [ 250.00]
dict:store_bkt_val/3 400 16.67 1000 [ 2.50]
dict:store/3 114 50.00 3000 [ 26.32]
And you can see that most of the time (50%) is spent in dict:store/3. 16.67% is taken in outputting the result, another 16.67% is taken by erl_eval (this is why you get by running short functions in the shell -- parsing them becomes longer than running them).
You can then start going from there. That's the basics of profiling run times with Erlang. Handle with care, eprof can be quite a load on a production system or for functions that run for too long. Especially on a production system.
You can use eprof or fprof.
The normal way to do this is with timer:tc. Here is a good explanation.
I can recommend you this tool: https://github.com/virtan/eep
You will get something like this https://raw.github.com/virtan/eep/master/doc/sshot1.png as a result.
Step by step instruction for profiling all processes on running system:
On target system:
1> eep:start_file_tracing("file_name"), timer:sleep(20000), eep:stop_tracing().
$ scp -C $PWD/file_name.trace desktop:
On desktop:
1> eep:convert_tracing("file_name").
$ kcachegrind callgrind.out.file_name
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