Performance

Measuring and improving run-time or compile-time performance

This page is about computer performance in the context of Perl 6.

First, profile your code

Make sure you're not wasting time on the wrong code: start by identifying your "critical 3%" by "profiling" your code's performance. The rest of this document shows you how to do that.

Time with now - INIT now

Expressions of the form now - INIT now, where INIT is a phase in the running of a Perl 6 program, provide a great idiom for timing code snippets.

Use the m: your code goes here perl6 channel evalbot to write lines like:

m: say now - INIT now
rakudo-moar abc1234: OUTPUT«0.0018558␤»

The now to the left of INIT runs 0.0018558 seconds later than the now to the right of the INIT because the latter occurs during the INIT phase.

Profile locally

When using the MoarVM backend, the Rakudo compiler's --profile command line option writes the profile data to an HTML file. If the profile data is too big, it could take a long time for a browser to open the file. In that case, output to a file with a .json extension, then open the file with Qt viewer.

To deal with even larger profiles, output to a file with a .sql extension. This will write the profile data as a series of SQL statements, suitable for opening in SQLite.

# create a profile 
perl6 --profile --profile-filename=demo.sql -e 'say (^20).combinations(3).elems'
 
# create a SQLite database 
sqlite3 demo.sqlite
 
# load the profile data 
sqlite> .read demo.sql
 
# the query below is equivalent to the default view of the "Routines" tab in the HTML profile 
sqlite> select
      case when r.name = "" then "<anon>" else r.name end,
      r.file,
      r.line,
      sum(entriesas entries,
      sum(case when rec_depth = 0 then inclusive_time else 0 endas inclusive_time,
      sum(exclusive_timeas exclusive_time
    from
      call c,
      routines r
    where
      c.id = r.id
    group by
      c.id
    order by
      inclusive_time desc
    limit 30;

To learn how to interpret the profile info, use the prof-m: your code goes here evalbot (explained above) and ask questions on the channel.

Profile compiling

If you want to profile the time and memory it takes to compile your code, use Rakudo's --profile-compile option.

Create or view benchmarks

Use perl6-bench.

If you run perl6-bench for multiple compilers (typically, versions of Perl 5, Perl 6, or NQP), results for each are visually overlaid on the same graphs, to provide for quick and easy comparison.

Share problems

Once you've used the above techniques to identify the code to improve, you can then begin to address (and share) the problem with others:

Solve problems

This bears repeating: make sure you're not wasting time on the wrong code. Start by identifying the "critical 3%" of your code.

Line by line

A quick, fun, productive way to try improve code line-by-line is to collaborate with others using the perl6 evalbot camelia.

Routine by routine

With multidispatch, you can drop in new variants of routines "alongside" existing ones:

# existing code generically matches a two arg foo call: 
multi sub foo(Any $aAny $b{ ... }
 
# new variant takes over for a foo("quux", 42) call: 
multi sub foo("quux"Int $b{ ... }

The call overhead of having multiple foo definitions is generally insignificant (though see discussion of where below), so if your new definition handles its particular case more efficiently than the previously existing set of definitions, then you probably just made your code that much more efficient for that case.

Speed up type-checks and call resolution

Most where clauses – and thus most subsets – force dynamic (run-time) type checking and call resolution for any call it might match. This is slower, or at least later, than compile-time.

Method calls are generally resolved as late as possible (dynamically at run-time), whereas sub calls are generally resolved statically at compile-time.

Choose better algorithms

One of the most reliable techniques for making large performance improvements, regardless of language or compiler, is to pick a more appropriate algorithm.

A classic example is Boyer-Moore. To match a small string in a large string, one obvious way to do it is to compare the first character of the two strings and then, if they match, compare the second characters, or, if they don't match, compare the first character of the small string with the second character in the large string, and so on. In contrast, the Boyer-Moore algorithm starts by comparing the *last* character of the small string with the correspondingly positioned character in the large string. For most strings, the Boyer-Moore algorithm is close to N times faster algorithmically, where N is the length of the small string.

The next couple sections discuss two broad categories for algorithmic improvement that are especially easy to accomplish in Perl 6. For more on this general topic, read the wikipedia page on algorithmic efficiency, especially the 'See also' section near the end.

Change sequential/blocking code to parallel/non-blocking

This is another very important class of algorithmic improvement.

See the slides for Parallelism, Concurrency, and Asynchrony in Perl 6 and/or the matching video.

Use existing high performance code

There are plenty of high performance C libraries that you can use within Perl 6 and NativeCall makes it easy to create wrappers for them. There's experimental support for C++ libraries, too.

If you want to use Perl 5 modules in Perl 6, mix in Perl 6 types and the Meta-Object Protocol.

More generally, Perl 6 is designed to smoothly interoperate with other languages and there are a number of modules aimed at facilitating the use of libs from other langs.

Make the Rakudo compiler generate faster code

To date, the focus for the compiler has been correctness, not how fast it generates code or how fast or lean the code it generates runs. But that's expected to change, eventually... You can talk to compiler devs on the freenode IRC channels #perl6 and #moarvm about what to expect. Better still, you can contribute yourself:

Still need more ideas?

Some known current Rakudo performance weaknesses not yet covered in this page include the use of gather/take, junctions, regexes and string handling in general.

If you think some topic needs more coverage on this page, please submit a PR or tell someone your idea. Thanks. :)

Not getting the results you need/want?

If you've tried everything on this page to no avail, please consider discussing things with a compiler dev on #perl6, so we can learn from your use-case and what you've found out about it so far.

Once a dev knows of your plight, allow enough time for an informed response (a few days or weeks, depending on the exact nature of your problem and potential solutions).

If that hasn't worked out, please consider filing an issue about your experience at our user experience repo before moving on.

Thanks. :)