If you have time critical code that is running too slow. Do not
waste your time trying to guess what might be slowing it
down. The best approach is to profile your code to find the
bottlenecks and concentrate your efforts on optimizing
them. Profiling Erlang code is in first hand done with the tools
fprof
and eprof
. But also the tools cover
and cprof
may be useful.
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Do not optimize code that is not time critical. When time is not of the essence it is not worth the trouble trying to gain a few microseconds here and there. In this case it will never make a notable difference. |
If you have a big system it might be interesting to run profiling on a simulated an limited scenario to start with. But bottlenecks has a tendency to only appear or cause problems, when there are many things going on at the same time, and when there are many nodes involved. Therefore it is desirable to also run profiling in a system test plant on a real target system.
When your system is big you do not want to run the profiling tools on the whole system. You want to concentrate on processes and modules that you know are central and stand for a big part of the execution.
When analyzing the result file from the profiling activity you will in first hand look for functions that are called many times and has a long "own execution time" (time excluded calls to other functions). Functions that just are called very many times can also be interesting, as even small things can add up to quite a bit if they are repeated often. Then you need to ask yourself what can I do to reduce this time. Appropriate types of questions to ask yourself are:
These questions are not always trivial to answer. You might need to do some benchmarks to back up your theory, to avoid making things slower if your theory is wrong. See benchmarking.
fprof
measures the execution time for each function,
both own time i.e how much time a function has used for its
own execution, and accumulated time i.e. including called
functions. The values are displayed per process. You also get
to know how many times each function has been
called. fprof
is based on trace to file in order to
minimize runtime performance impact. Using fprof is just a
matter of calling a few library functions, see fprof manual
page under the application tools.
fprof
is introduced in version R8 of Erlang/OTP. Its
predecessor eprof
that is based on the Erlang trace BIFs,
is still available, see eprof manual page under the
application tools. Eprof shows how much time has been used by
each process, and in which function calls this time has been
spent. Time is shown as percentage of total time, not as
absolute time.
cover
's primary use is coverage analysis to verify
test cases, making sure all relevant code is covered.
cover
counts how many times each executable line of
code is executed when a program is run. This is done on a per
module basis. Of course this information can be used to
determine what code is run very frequently and could therefore
be subject for optimization. Using cover is just a matter of
calling a few library functions, see cover manual
page under the application tools.
cprof
is something in between fprof
and
cover
regarding features. It counts how many times each
function is called when the program is run, on a per module
basis. cprof
has a low performance degradation (versus
fprof
and eprof
) and does not need to recompile
any modules to profile (versus cover
).
Tool | Results | Size of result | Effects on program execution time | Records number of calls | Records Execution time | Records called by | Records garbage collection |
fprof
|
per process to screen/file | large | slowdown | yes | total and own | yes | yes |
eprof
|
per process/function to screen/file | medium | significant slowdown | yes | only total | no | no |
cover
|
per module to screen/file | small | moderate slowdown | yes, per line | no | no | no |
cprof
|
per module to caller | small | small slowdown | yes | no | no | no |
A benchmark is mainly a way to compare different constructs that logically have the same effect. In other words you can take two sequential algorithms and see which one is most efficient. This is achieved by measuring the execution time of several invocations of the algorithms and then comparing the result. However measuring runtime is far from an exact science, and running the same benchmark two times in a row might not give exactly the same figures. Although the trend will be the same, so you may draw a conclusion such as: Algorithm A is substantially faster than B, but you can not say that: Algorithm A is exactly 3 times faster than B.
If you want to write a benchmark program yourself there are a few things you must consider in order to get meaningful results.
To help you with this we provide a benchmarking framework
located in the doc/efficiency_guide directory of the Erlang/OTP
installation, which consists
of bench.erl
,
bench.hrl
,
and all.erl
. To
find out how it works please consult
README
, you can
also look at the example benchmark:
call_bm.erl
.
Here follows an example of running the benchmark defined in
call_bm.erl
in a unix environment:
unix_prompt> ls all.erl bench.erl bench.hrl call_bm.erl unix_prompt> erl Erlang (BEAM) emulator version 5.1.3 [threads:0] Eshell V5.1.3 (abort with ^G) 1> c(bench). {ok,bench} 2> bench:run(). Compiling call_bm.erl... Running call_bm: local_call external_call fun_call apply_fun apply_mfa ok 3> halt(). unix_prompt> ls all.erl bench.erl bench.hrl call_bm.erl index.html unix_prompt>
The resulting index.html file may look like: index.html .
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The results of a benchmark can only be considered valid for the Erlang/OTP version that you run the benchmark on. Performance is dependent on the implementation which may change between releases. |