🆁🅸🅽.x ≠ π*r² by Hillel.App::Mi6 by Alexey Melezhik.
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Roman Baumer (Freenode: rba #raku or ##raku-infra) / 2020-09-24T15:21:26Votemaster Will Coleda has published the results of the first Raku Steering Council election. Thanks to everybody who has voted! The elected council members are (in alphabetical order of their last name):
Congratulations! Yours truly assumes that after an initial meeting, the Council will come with a statement on how to proceed with the future of the Raku Programming Language.
JJ Merelo has announced the results of the more general Raku User Survey that has been running in the past weeks: the raw CSV, and preliminary PDF. Kudos to JJ Merelo for taking care of this yet another year!
Daniel Sockwell dives into the Unicode internals of the Raku Programming Language and finds out in more detail that Raku is pretty unique in that respect. In A deep dive into Raku’s Unicode support (/r/rakulang comments).
Vadim Belman elaborates on the Proxy Container in the Advanced Raku for Beginners series: in other words, how you can override the FETCH and STORE methods on containers.
Andrew Shitov wrote three episodes in the Pearls of Raku series this week:
-rw thingsWeekly Challenge #79 is available for your perusal. A full review of the Raku solutions of Challenge #77 (including a video run-through) was done by Andrew Shitov.
Most of the core developments have been happening in the rakuast branch. The 2020.09 Rakudo Compiler Release has been postponed to iron out some configuration issues. Meanwhile, in the main branch:
P6opaque object in MoarVM.Nums.This week’s new Pull Requests:
copy_to should call MVM_gc_write_barrier with the *new* key’s addressCapture.rakuPerl6::SysConfig depends on NQPs HLL::SysConfig.truncatePlease check them out and leave any comments that you may have!
Finally crossed the 1500 question mark on StackOverflow. Keep those questions coming! Meanwhile:
enums augmentable? by Ben Davies.has keyword mean before method in a class definition? by jja.🆁🅸🅽.x ≠ π*r² by Hillel.App::Mi6 by Alexey Melezhik.The suspense was killing! Finally the election results are in. Yours truly is happy to have been selected by more 75% of the voters. It is good to know that so many people think you’re doing a good thing for the Raku Programming Language. Thank you! I congratulate the other elected members and look forward to work constructively with them!
Finally, again and again, please don’t forget to stay healthy and to stay safe. Next week there will be more news about Raku. Until then!
Want to quickly learn about the fundamentals of Raku with a book? Raku Fundamentals by Moritz Lenz has just arrived on the physical bookshelves as well as on the virtual ones. Formerly known as “Perl 6 Fundamentals”, the second edition has been completely updated and has a chapter on Cro web services added. Be sure to leave a review when you have become the owner of a copy!
Out of the blue, a very nice set of introductory videos into the Raku Programming Language have appeared on the interwebs. Kudos to Alex Merced for making these, and William Michels for the tip!
You have until midnight UTC on 20 September 2020 to cast your vote in the first official Raku Steering Council election. Fourteen candidates to fill 7 positions: and here they are in alphabetical order of their last name (follow the link to find out why they would like to be on the RSC):
Please follow the instructions on how to cast your ballot!
Daniel Sockwell elaborates about how the difference between pod and pod6, is like the difference between JSON and Javascript objects. In Peas in a Pod6 (/r/rakulang comments).
L’Alabameñu has started a project to translate Raku’s error messages into various natural languages other than English. The associated module is not (yet) in the module ecosystem, but feels interesting enough to start mentioning already 
Wenzel P. P. Peppmeyer wrote about releasing on Github, and Andrew Shitov revisited weekly challenges of the past with an interesting range of alternate programming languages.
For quite a few months now, Joseph Brenner has been running a weekly Raku Study Group in San Francisco. Sadly, yours truly had not noticed that these events have been online, so you don’t actually have to travel to San Francisco to be able to attend. So be sure to checkout the upcoming events for details on the next meeting!
Weekly Challenge #78 is available for your perusal, and Andrew Shitov was quick to follow that up with their solutions and found time to do a full review of the Raku solutions of Challenge #76.
Most of the core developments have been happening in branches on MoarVM and Rakudo, specifically in the rakuast branch. Meanwhile, in the main branch:
This week’s new Pull Requests:
--rakudo-home Configure.pl parameterPlease check them out and leave any comments that you may have!
Enum question by Mohammad S Anwar.ddt as a Raku function by Alexey Melezhik.
.$_ by * by Ralph Mellor.A new book, some new videos, new modules, new blog posts and many people talking about Raku. A quiet week again, indeed :-). Please, don’t forget to stay healthy and to stay safe. Check again next week for more news about the Raku Programming Language!
After month and a half full of many events, I finally got time to complete one more article for the Advanced Raku For Beginners series.
Frankly, I’m not happy about it. It feels to me that my not the perfect English is gotten even worse; not all topics and quirks are covered. But at least I’m getting back in these waters. So, just let it be. I’m warming up. And hope to advance into another subject soon.
In my last post I lamented the lack of testing metadata. Just a few days later it got in my way when I played with creating releases on github. My normal workflow on github is to commit changes and push them to trigger travis. When travis is fine I bump the version field in META6.json so the ecosystem and zef can pick up the changes. And there is a hidden trap. If anybody clones the repo via zef just before I bump the version, there will be a mismatch between code and version. CPAN doesn’t got that problem because there is always a static tarball per version. With releases we can get the same on github.
It’s a fairly straight forward process.
This is so simple that I immediately automated that stuff with META6::bin so I can mess it up. (Not released yet, see below.)
The result is an URL like so: https://github.com/gfldex/raku-release-test/archive/raku-release-test-0.0.19.tar.gz. When we feed that to zef it will check if the version is not already installed and then proceed to test and install.
And there is a catch. Even though zef is fine with the URL, Test::META will complain because it doesn’t end in .git and fail the test. This in turn will stop zef from installing the module. We added that check to make sure zef always gets a proper link to a clone-able repo for modules hosted on github. This assumption is clearly wrong and needs fixing. I will send a PR soon.
Having releases on github (other gitish repo-hosting sites will have similar facilities or will get them) can get us one step closer to a proper RPAN. Once I got my first module into the ecosystem this way I will provide an update here.
Just a reminder to anybody passing by this blog that the election to Raku Steering Council is going on now and will be taking place until Sep 20. More details can be found in the voting form and the original announcement.
I have cast my ballot.
The coming two weeks will allow all Rakoons to vote in the first official Raku Steering Council election. Fourteen candidates to fill 7 positions: and here they are in alphabetical order of their first name:
Please follow the instructions on how to cast your ballot: you have until midnight UTC on 20 September 2020 to cast your vote!
Daniel Sockwell delves further into Raku in an inspiring blog post: Weaving Raku: semiliterate programming in a beautiful language. Taking the Raku Programming Language to as yet unexplored corners of its capabilities (/r/rakulang comments).
The August report of the Raku Development Grant of Jonathan Worthington was published: not a lot happened on it in August, but September has more time available. In related news, Makoto Nozaki announced the proper launch of the Raku Development Fund.
Wenzel P. P. Peppmeyer wrote about finding out which modules are added / updated in the ecosystem.
After careful consideration, yours truly has decided to no longer list the Raku solutions of the Weekly Challenge in the Rakudo Weekly News. Turns out that the WordPress editor gets very confused about changing URLs in existing, but copied posts. Last week had several wrong links in the overview, and nobody noticed or took the trouble to inform yours truly. Clearly, this is not a very heavily used feature of the Rakudo Weekly News.
Whenever there is a weekly review of Raku solutions, these will be mentioned! Therefore, please check out Andrew Shitov‘s review of Raku solutions to Weekly Challenge #75.
gist method on hashes by adding a cmp candidate for comparing Code objects.Allomorph class from which all allomorphic objects inherit (IntStr, NumStr, RatStr and ComplexStr), which makes dispatching for these types a lot simpler, and should better allow for custom allomorphic classes in the future.parse-names function.This week’s new Pull Requests:
Please check them out and leave any comments that you may have!
mi6 as a function by Alexey Melezhik.@*ARGS form? by William Michels.gather / take by Ralph Mellor.A bit of a quiet week, which in many ways feels like the calm before the storm. It still bears repeating: don’t forget to stay healthy and to stay safe. Please check again next week for more news about the Raku Programming Language!
I didn’t know so I asked her.
15:25 <gfldex> How do you gather info for "Updated Raku Modules"?
17:40 <lizmat> https://twitter.com/raku_cpan_new
17:59 <gfldex> thx
23:06 <lizmat> well volunteered :-)That’s what you get for being nosey. So off I went into the land of mostly undocumented infrastructure.
The objective is simple. Generate two lists of modules where the first contains all modules that are newly added to the ecosystem and the second got all updated modules. For both the timespan of interest is Monday of this week until Monday of last week. Currently we got two collections of META-files. Our ecosystem and CPAN. The latter does not know about META6 and that sucks. But we will manage. Conveniently both lists are provided by ugexe at github. Since there are commits we can travel back in time and get a view of the ecosystem from when we need it. To do so we first need to get a list of commits.
sub github-get-remote-commits($owner, $repo, :$since, :$until) is export(:GIT) {
my $page = 1;
my @response;
loop {
my $commits-url = $since && $until ?? „https://api.github.com/repos/$owner/$repo/commits?since=$since&until=$until&per_page=100&page=$page“ !! „https://api.github.com/repos/$owner/$repo/commits“;
my $curl = Proc::Async::Timeout.new('curl', '--silent', '-X', 'GET', $commits-url);
my $github-response;
$curl.stdout.tap: { $github-response ~= .Str };
await my $p = $curl.start: :$timeout;
@response.append: from-json($github-response);
last unless from-json($github-response)[0].<commit>;
$page++;
}
if @response.flat.grep(*.<message>) && @response.flat.hash.<message>.starts-with('API rate limit exceeded') {
dd @response.flat;
die „github hourly rate limit hit.“;
}
@response.flat
}
my @ecosystems-commits = github-get-remote-commits(‚ugexe‘, ‚Perl6-ecosystems‘, :since($old), :until($young));Now we can get a whole bunch of ex-json which was compiled of the META6.json and *.meta files. Both file formats are not compatible. The auth field of a CPAN module will differ from the auth of the upstream META6.json, there is no authors field and no URL to the upstream repo. Not pretty but fixable because tar is awesome.
my @meta6;
px«curl -s $source-url» |» px<tar -xz -O --no-wildcards-match-slash --wildcards */META6.json> |» @meta6;
my $meta6 = @meta6.join.chomp.&from-json;(Well, GNU tar is awesome. BSD tar doesn’t sport --no-wildcards-match-slash and there is one module with two META6.json-files. I think I can get around this with a 2 pass run.)
This works nicely for all but one module. For some reason a Perl 5 module sneaked into the list of Raku modules on CPAN. It’s all just parsed JSON so we can filter those out.
my @ecosystems = fetch-ecosystem(:commit($youngest-commit)).grep(*.<perl>.?starts-with('6'));Some modules don’t contain an auth field, some got an empty name. Others don’t got the authors field set. We don’t enforce proper meta data even though it’s very easy to add quality control. Just use Test::META in your tests. Here is an example.
I can’t let lizmat down though and github knows almost all authors.
sub github-realname(Str:D $handle) {
my @github-response;
my $url = 'https://api.github.com/users:' ~ $handle;
px«curl -s -X GET $url» |» @github-response;
@github-response.join.&from-json.<name>
}If there is more then one author they wont show up with this hack. I can’t win them all. I’m not the only one who suffers here. On modules.raku.org at least one module shows up twice with the same author. My guess is that happens when a module is published both in our ecosystem and on CPAN. I don’t know what zef does if you try to nail a module down by author and version with such ambiguity.
I added basic html support and am now able to give you a preview of next weeks new modules.
If your module is in the list and your name looks funny, you may want to have a look into the META6.json of you project.
Yesterday we had a discussion about where to publish modules. I will not use CPAN with the wrong language. Don’t get me wrong. I like CPAN. You can tie an aircraft carrier to it and it wont move. But it’s a Comprehensive Perl Archive Network. It’s no wonder it doesn’t like our metadata.
Kudos to tony-o for taking on a sizeable task. I hope my lamentation is helpful in this regard.
The script can be found here. I plan to turn it into a more general module to query the ecosystem. Given I spend the better part of a week on a 246 lines file the module might take a while.
Less than a week to go until the candidacy period for the first election of the Raku Steering Council ends (at midnight UTC on 6 September 2020, to be precise). So far, ten people have announced their candidacy, which is great to see! Yours truly feels that, to make the Raku Steering Council truly reflect the Raku userbase, there should be more women, more younger people and more people who do not have English as their first language. If you feel you belong to these groups, and you want to be a part of the future of Raku, please consider adding your candidacy! If you have any questions about the process, please feel free to open an issue!
Daniel Sockwell explains how they have fallen in love with the Raku Programming Language in an extensive blog post about testing and conditional compilation. It’s really all about applying Rust’s approach to organizing unit tests to Raku. And how the DOC phaser can be appropriated to achieve that goal. An inspiring piece of work that will surely have its influence on the development of Raku (/r/rakulang, /r/rust comments).
Alexander Kiryuhin informs us that there is a new release of Comma Complete, the full featured IDE for Raku. Please note that by buying a copy of Comma Complete, you will also be helping the development of the Comma Community edition, and will help with implementing of the Roadmap.
Sadly, the Raku ecosystem grant proposal did not make it in the July 2020 round of The Perl Foundation grants. Suggestions for an improved grant proposal were made (/r/rakulang comments).
Alexey Melezhik has launched a proposal to wrap existing command-line tools into Raku functions.
Gábor Szabo takes another, deeper look at the Raku REPL. Wenzel P. P. Peppmeyer wrote about tripping over variables. And another nice blog post by Andrew Shitov in the Pearls of Raku series: Issue 9: toss a coin, topic vs temporary variables (/r/rakulang comments).
The entries for Challenge 75 that have Raku solutions:
Andrew Shitov reviewed all of the Raku solutions of Challenge #74 with a video version for Task #1 and Task #2. The Weekly Challenge #76 is up for your perusal!
This week saw the merging of the new hash implementation work by Nicholas Clark, which could make your program more than 10% faster! In other core developments:
raku -V, a problem that was discovered shortly after the 2020.08.1 compiler release, causing a 2020.08.2 release.moar --dump functionality ignore dependency information added by Rakudo, thus making it work on Rakudo bytecode out of the box. They also worked on various aspects of memory allocation in MoarVM.SetHash.set / SetHash.unset methods.This week’s new Pull Requests:
MVM_file_isexecutable() when being rootfetch_delete_key and a regression test.MONKEY-SEE-NO-EVAL export from TestCool.EVAL methodPlease check them out and leave any comments that you may have!
zef test . returning a different result than running some tests with raku -Ilib by JJ Merelo.q{} removing spaces in a map by Mimosinnet.given / when? by Unsigned 128-bit Coffee.Seq whitespace sensitivity? by William Michels.lines :$nl-in question by ToddAndMargo.lines and :chomp question by ToddAndMargo.A week with exciting developments, a love story, and a new Comma Complete release! And thanks to Wenzel P.P. Peppmeyer, a complete list of updated Raku modules!
Yours truly keeps repeating: don’t forget to stay healthy and to stay safe. Please check again next week for more news about the Raku Programming Language!
A special edition of the Rakudo Weekly News to inform you all of an exciting development in the world of the Raku Programming Language.
The implementation of hashes in MoarVM has been replaced! Nicholas Clark wrote a new hash implementation for MoarVM in the past months, which uses less memory and less CPU.
MoarVM had been using the open source uthash library since July 2012. At the time it was the “Go To” hashing library for C – full of features and flexible – so it was the logical choice. Over the years it has been modified to remove the features not needed by MoarVM, and hence the associated code and overhead. But the basic design was necessarily unchanged – it remained the “classic” approach to hash tables – “collision chaining”. The internal structure is an array of linked lists – just like Perl has done, since Perl 1.
This structure is simple to implement, but ultimately dates back to an era when CPUs didn’t even have caches, so it doesn’t fare too well now that minimising cache misses is the key to performance.
The basic problem that hash tables need to solve, is that they need a fast way to map from arbitrary keys not known in advance into something that is efficient for a CPU to manipulate – integers. You’d like to store each key (and its associated value data) in an array, but also you can’t make the array infinitely big, so you always end up with a trade off – sometimes more than one key maps to the same integer, and so you need to code to handle this.
Collision chaining is the “obvious” way – a memory efficient way to store a variable number of keys that all need to sit at the same index in the array. Operations need to walk all keys at the same index linearly, but keep the list small (grow the array as needed) and the performance is still good.
However the downside is that the keys you need to walk linearly are not adjacent in memory, meaning you likely have cache misses. You could replace the linked lists with arrays (for more complexity, and higher delete costs), but you still have one pointer hop and potential cache miss.
With the increasing importance of staying CPU cache-friendly, a different approach to collision resolution is now better: open address hashing.
This is roughly equivalent to using a larger array than collision chaining, and eliminating the linked lists. While this reduces the collisions, it doesn’t eliminate them, so you need some other strategy to handle them. Usually this involves a strategy for storing the key/value pair in an array index close to the correct location, and searching all locations where the key might be until either it is found or the possible locations are exhausted.
Currently one of the most efficient approaches to this is “Robin Hood” hashing. On updates, it moves entries around to minimise the distance of all keys to their ideal locations, not just the key added or deleted. Usefully, this increases the chance of CPU cache hits during lookups.
Regarding hash iterators: one fun “gotcha” is that the Raku Programming Language (as well as Perl) both specify that it is acceptable to delete the key at the current hash iterator position without problems. Fortunately it was possible to adapt the insertion and iteration strategies to permit this without needing any extra complications or overhead.
All of this is now available in the main branch of the Rakudo compiler. If you have hash-intensive applications, please try it out with this (as yet unreleased) version of Rakudo. And let us know of your findings. Reports so far indicate 10% to 15% improvements in core setting compilation, as well as in roast testing. But of course, Your Mileage May Vary!
I was wondering where lizmat gets the info for changed modules from. She kindly answered with a link. I learned that updates to modules only show up, when we put them on CPAN. Since most modules are hosted on github, changing a module there does not mean that the world will be informed. I believe a better way to do that would be to fetch the ecosystems (we got two) once a week and check if version in any META6.json has changed.
Anyway, the reason I started this post is the documentation for FixedInt. It reads:
One major caveat to be aware of when using this module. The class instance may not be instantiated in a $ sigiled variable.
Raku $ sigiled scalar variables do not implement a STORE method, but instead do direct assignment; and there doesn’t seem to be any easy way to override that behaviour.
An implication of that is that classes that do implement a STORE method can not be held in a $ sigiled variable. (Well, they can, they just won’t work correctly. The first time you try to store a new value, the entire class instance will be overwritten and disappear.)
That is not true.
class Changing {
has $!var handles <Str gist raku> is default(Nil);
method STORE(\v) { note 'storing'; $!var = v }
method FETCH { note 'fetching'; $!var }
}
constant term:<$a> := Changing.new;
$a = 42;
put $a;
# OUTPUT: storing
42The problem here is that the docs talk about variables while Raku don’t got any. It got containers with mutable content and values which are immutable. The language also got symbols that we can actually point at in source code. (Values we can point at in source code are called literals.) In the example above I created a symbol that looks like a variable but is a “reference” to a value of type Changing. The assignment operator can not be overloaded so we can protect immutable values. We can implement the method STORE instead. In fact we must, because there is no container in-between the symbol $a and the instance of Changing. (We get X::Assignment::RO if we try to assign without a STORE.) Since Rakudo does not recognise Changing as a container, it will refuse to call FETCH.
Thundergnat wrote a neat module with very little effort. Quite useful to do calculations with integers of fixed bit size.
my \fixedint = FixedInt.new(:8bit);
say fixedint; # 0
say fixedint -= 12; # 244
say fixedint.signed; # -12
say fixedint.bin; # 0b11110100
say fixedint.hex; # 0xF4He achieved all that in just 36 lines of code. The trick is to force the user to bind and thus avoid the creation of a container while using STORE and FETCH to change the object in place. I doubt this is thread safe. Also the user of the module loses the ability to use some forms of quote interpolation and data dumper functions/modules will have less to display.
my \i = Int.new(10);
my $i = Int.new(10);
dd i;
dd $i;
# OUTPUT: 10
Int $i = 10We don’t have to define many operators to make custom types work because of plenty of meta-programming that is done in CORE. Many of those constructs assume immutable values. Autothreading is planned and will make the use of ». “interesting”. Thundergnat did not specify a language version for his module. The module itself is not hard to make safe. But – acutally BUT – this will change the interface for the user.
The flexibility of the language bites us here. Even thought the docs explain the difference between different sigils nobody is forced to read it. Also, nobody is forced to stick use v6.d at the beginning of a module. Please do so or the compiler wont be able to help you in the future. While naming immutable constructs quite often, the docs don’t explain why we use them. Concurrency and thus threading is very easy to add to a program. Testing it is hard.
I don’t have a solution to those problems but I’m pretty sure we need one or they will haunt us the next 100 years.
STDERR is often (ab)used for printing debug or status information. This can create clutter which in turn hides the important stuff. I want to print the essential stuff in exceptions in red unless a dynvar or environment variable is set.
class Explode is Exception {
method message {
put "$*dynvar is bad‼";
}
}
sub e() {
await start {
Explode.new.throw;
}
CATCH { default { put .message } }
}
my $*dynvar = 'foo';
e();
# OUTPUT: foo is bad‼We can access a dynvar inside the method of an exception from within an exception handler. In Shell::Piping error handling is a bit more involved. The biggest issue is fail because the enclosed exception is thrown by some routine in CORE about two steps down the call tree seen from the implicit or explicit MAIN sub. The dynvar is simply not there at this point in time. Luckily instances of Exception tend not to be long lived so we can get away with capturing the state of a dynvar at object creation. A good place to do so is TWEAK.
sub infix:<///>(\a, \b) is raw {
my $dyn-name = a.VAR.name;
my $has-outer-dynvar = CALLER::CALLERS::{$dyn-name}:exists;
CALLER::{$dyn-name} = $has-outer-dynvar ?? CALLER::CALLERS::{$dyn-name} !! b
}
role Exception::Colored is Exception is export {
has $.color;
submethod TWEAK {
my $*colored-exceptions /// on;
$!color = $*colored-exceptions ~~ on && $env-color ?? 31 !! 0;
}
method RED($str) {
$*ERR.t ?? ("\e[" ~ $.color ~ 'm' ~ $str ~ "\e[0m") !! $str
}
}Now I can use $.RED in .message of any exception that is Exception::Colored.
To have a look at the full stack was very helpful to figure out why the dynvar wasn’t there in some cases. For such cases I have a context sensitive binding in my .vimrc.
nmap <F1> :w<CR>:!raku -I ./lib %<CR>
imap <F1> <esc>:w<CR>:!raku --ll-exception -I ./lib %<CR>In insert mode F1 will write the file and run Rakudo with an additional parameter. This results in a full stack trace.
foo failed
at SETTING::src/core.c/Exception.pm6:62 (/usr/local/src/rakudo/install/share/perl6/runtime/CORE.c.setting.moarvm:throw)
from SETTING::src/core.c/Failure.pm6:56 (/usr/local/src/rakudo/install/share/perl6/runtime/CORE.c.setting.moarvm:throw)
from SETTING::src/core.c/Failure.pm6:111 (/usr/local/src/rakudo/install/share/perl6/runtime/CORE.c.setting.moarvm:sink)
from /home/dex/tmp/tmp-2.raku:56 (<ephemeral file>:<unit>)
from /home/dex/tmp/tmp-2.raku:1 (<ephemeral file>:<unit-outer>)
from gen/moar/stage2/NQPHLL.nqp:1948 (/usr/local/src/rakudo/install/share/nqp/lib/NQPHLL.moarvm:eval)
from gen/moar/stage2/NQPHLL.nqp:2153 (/usr/local/src/rakudo/install/share/nqp/lib/NQPHLL.moarvm:evalfiles)
from gen/moar/stage2/NQPHLL.nqp:2113 (/usr/local/src/rakudo/install/share/nqp/lib/NQPHLL.moarvm:command_eval)
from gen/moar/Compiler.nqp:60 (/usr/local/src/rakudo/install/share/perl6/lib/Perl6/Compiler.moarvm:command_eval)
from gen/moar/stage2/NQPHLL.nqp:2038 (/usr/local/src/rakudo/install/share/nqp/lib/NQPHLL.moarvm:command_line)
from gen/moar/rakudo.nqp:116 (/usr/local/src/rakudo/install/share/perl6/runtime/perl6.moarvm:MAIN)
from gen/moar/rakudo.nqp:1 (/usr/local/src/rakudo/install/share/perl6/runtime/perl6.moarvm:<mainline>)
from <unknown>:1 (/usr/local/src/rakudo/install/share/perl6/runtime/perl6.moarvm:<main>)
from <unknown>:1 (/usr/local/src/rakudo/install/share/perl6/runtime/perl6.moarvm:<entry>)As you can see there are quite a few things called before your script will be executed. Luckily Rakudo is implementing Raku in Raku so we have a chance to see what is going on.
A little preface with an off-topic first. In the process of writing this post I was struck by the worst sysadmin’s nightmare: loss of servers followed by a bad backup. Until the very last moment I have had well-grounded fears of not finishing the post whatsoever. Luckily, I made a truce with life to get temporary respite. A conclusion? Don’t use bareos with ESXi. Or, probably, just don’t use bareos…
While picking up a RFC for my previous advent post I was totally focused on language-objects section. It took me a few passes to find the right one to cover. But in the meantime I realized that a very important topic is actually missing from the list. “Impossible!” – I said to myself and went onto another hunt later. Yet, neither search for “abstract class”, nor for “role” didn’t come up with any result. I was about to give up and make the conclusion that the idea came to life later, when the synopses were written or around so.
But, wait, what interface is mentioned as a topic of a OO-related RFC? Oh, that interface! As the request body states it:
Add a mechanism for declaring class interfaces with a further method for declaring that a class implements said interface.
At this point I realized once again that it is now a full 20 years behind us. That the text is from the times when many considered Java as the only right OO implementation! And indeed, by reading further we find the following statement, likely to be affected by some popular views of the time:
It’s now a compile time error if an interface file tries to do anything other than pre declare methods.
Reminds of something, isn’t it? And then, at the end of the RFC, we find another one:
Java is one language that springs to mind that uses interface polymorphism. Don’t let this put you off – if we must steal something from Java let’s steal something good.
Good? Good?!! Oh, my… Java’s attempt to solve problems of C++ multiple inheritance approach by simply denying it altogether is what drove me away from the language from the very beginning. I was fed up with Pascal controlling my writing style as far back as in early 90s!
Luckily, those involved in early Perl6 design must have shared my view to the problem (besides, Java itself has changed a lot since). So, we have roles now. What they have in common with abstract classes and the modern interfaces is that a role can define an interface to communicate with a class, and provide implementation of some role-specific behavior too. It can also do a little more than only that!
What makes roles different is the way a role is used in Raku OO model. A class doesn’t implement a role; nor it inherits from it as it would with abstract classes. Instead it does the role; or the other word I love to use for this: it consumes a role. Technically it means that roles are mixed into classes. The process can be figuratively described as if the compiler takes all methods and attributes contained by role’s type object and re-plants then onto the class. Something like:
role Foo {
has $.foo = 42;
method bar {
say "hello!"
}
}
class Bar does Foo { }
my $obj = Bar.new;
say $obj.foo; # 42
$obj.bar; # hello!How is it different from inheritance? Let’s change the class Bar a little:
class Baz {
method bar {
say "hello from Baz!"
}
}
class Bar does Foo is Baz {
method bar {
say "hello from Bar!";
nextsame
}
}
Bar.new.bar; # hello from Bar!
# hello from Baz!nextsame in this case re-dispatches a method call to the next method of the same name in the inheritance hierarchy. Simply put, it passes control over to the method Baz::bar, as one can see from the output we’ve received. And Foo::bar? It’s not there. When the compiler mixes the role into Bar it finds that the class does have a method named bar already. Thus the one from Foo is ignored. Since nextsame only considers classes in the inheritance hierarchy, Foo::bar is not invoked.
With another trick the difference from interface consumption can also be made clear:
class Bar {
method bar {
say "hello from Bar!"
}
}
my $obj = Bar.new;
$obj.bar; # hello from Bar!
$obj does Foo;
$obj.bar; # hello!In this example the role is mixed into an existing object, thanks to the dynamic nature of Raku which makes this possible. When a role is applied this way its content is enforced over the class content, similarly to a virus injecting its genetic material into a cell effectively overriding internal processes. This is why the second call to bar is dispatched to the Foo::bar method and Bar::bar is nowhere to be found on $obj this time.
To have this subject fully covered, let me show you some funny code example. The operator but used in it behaves like does except it doesn’t modify its LHS object; instead but creates and returns a new one:
my $s1 = "not empty means true";
my $s2 = $s1 but role { method Bool { False } };
say $s1 ?? "true" !! "false";
say $s2 ?? "true" !! "false";This snippet I’m leaving for you to try on your own because it’s time for my post to move onto another topic: role parameterization.
Consider the example:
role R[Str:D $desc] {
has Str:D $.description = $desc;
}
class Foo does R["some info"] { }
say Foo.new.description; # some infoOr more practical one:
role R[::T] {
has T $.val is rw;
}
class ContInt does R[Int] { }
ContInt.new.val = "oops!"; # "Type check failed..." exception is thrownThe latter example utilizes so called type capture where T is a generic type, the concept many of you are likely to know from other languages, which turns into a concrete type only when the role gets consumed and supplied with a parameter, as in class ContInt declaration.
The final iteration for parametrics I’m going to present today would be this more extensive example:
role Vect[::TX] {
has TX $.x;
method distance(Vect $v) { ($v.x - $.x).abs }
}
role Vect[::TX, ::TY] {
has TX $.x;
has TY $.y;
method distance(Vect $v) {
(($v.x - $.x)² + ($v.y - $.y)²).sqrt
}
}
class Foo1 does Vect[Rat] { }
class Foo2 does Vect[Int, Int] { }
my $foo1 = Foo1.new(:x(10.0));
my $foo2 = Foo2.new(:x(10), :y(5));
say $foo1; # Foo1.new(x => 10.0)
say $foo2; # Foo2.new(x => 10, y => 5)
say $foo2.distance(Foo2.new(:x(11), :y(4))); # 1.4142135623730951Hopefully, the code explains itself. Most certainly it nicely visualizes the long way made by the language designers since the initial RFC was made.
At the end I’d like to share a few interesting facts about Raku roles and their implementation by Rakudo.
use v6.e.PREVIEW; # 6.e is not released yet
role R { submethod TWEAK { say "R" } }
class Foo { submethod TWEAK { say "Foo" } }
class Bar is Foo does R { submethod TWEAK { say "Bar" } }
Bar.new; # Foo
# R
# Barrole R { say "Role" }
class Foo { say "Foo" }
# FooThen modify class Foo so that it consumes R:
class Foo does R { say "Foo" }
# Role
# FooThe difference in the output is explained by the fact that role body gets invoked when the role itself is mixed into a class. Try adding one more class consuming R alongside with Foo and see how the output changes. To make the distinction between class and role bodies even more clear, make your new class inherit from Foo. Even though is and does look alike they act very much different. 3. Square brackets in role declaration enclose a signature. As a matter of fact, it is the signature of role body subroutine! This makes a few very useful tricks possible:
# Limit role parameters to concrete numeric objects.
role R[Numeric:D ::T $default] {
has T $.value = $default;
}
class Foo[42.13] { };
say Foo.new.x; # 42.13Or even:
# Same as above but only allow specific values.
role R[Numeric:D ::T $default where * > 10] {
has T $.value = $default;
} Moreover, in case when few different parametric candidates are declared for a role, choosing the right one is a task of the same kind as choosing the right routine of a few multi candidates and based on matching signatures to the parameters passed. 4. Rakudo implements a role using four different role types! Let me demonstrate one aspect of this with the following snippet based on the example for the previous fact:
for Foo.^roles -> \consumed {
say R === consumed
}=== is a strict object identity operator. In our case we can consider it as a strict type equivalence operator which tells us if two types are actually exactly the same one.
And as I hope to have this subject covered later in a more extensive article, at this point I would make it a classical abrupt open ending by providing just the output of the above snippet as a hint:
False
Originally Submitted by Simon Cozens, RFC 28 on August 4, 2020, this RFC asked the community to make sure that whatever updates were made, that Perl 6 was still definitely recognizable as Perl. After 20 years of design, proofs-of-concept, implementations, two released language versions, we’ve ended up with something that is definitely Perlish, even if we’re no longer a Perl.
At the time the RFCs were submitted, the thought was that this language would be the next Perl in line, Perl 6. As time went on before an official language release, Perl 5 development picked up again, and that team & community wanted to continue on its own path. A few months ago, Perl 6 officially changed its name to Raku – not to get away from our Perl legacy, but to free the Perl 5 community to continue on their path as well. It was a difficult path to get to Raku, but we are happy with the language we’re shipping, even if we do miss having the Perl name on the tin.
Let’s dig into some of the specifics Simon mentions in his RFC.
We’ve got a golden opportunity here to turn Perl into whatever on earth we like. Let’s not take it.
This was a fine line that we ended up crossing, even before the rename. Specific design decisions were changed, we started with a fresh implementation (more than once if you count Pugs & Parrot & Niecza …). We are Perlish, inspired by Perl, but Raku is definitely different.
Nobody wins if we bend the Perl language out of all recognition, because it won’t be Perl any more.
I argue that eventually, everyone won – we got a new and improved Perl 5 (and soon, a 7), and we got a brand new language in Raku. The path wasn’t clear 20 years ago, but we ended up in a good place.
Some things just don’t need heavy object orientation.
Raku’s OO is everywhere: but it isn’t required. While you can treat everything as an object:
| 3.sqrt.say; |
You can still use the familiar Perlish forms for most features. say sqrt 3;
Even native scalars (which don’t have the overhead of objects) let you treat them as OO if you want.
| my uint32 $x = 32; | |
| say $x; | |
| $x.^name.say; |
Even though $x here doesn’t start out as an object, by calling a meta-method on it, the compiler cheats on our behalf and outputs Int here, the closest class to our native int.
But we avoid going the extent of Java; for example, we don’t have to define a class with a main method in order to execute a program.
Strong typing does not equal legitimacy.
Similar to the OO approach, we don’t require typing, but allow you to gradually add it. You can start with an untyped scalar variable, but as you further develop your code, you can add a type to that declared variable, and to parameters to subs & methods. The types can be single classes, subsets, Junctions, where clauses with complicated logic: you can use as much or as little typing as you want. Raku’s multi routines (subs or methods with the same name but different arguments) give you a way to split up your code based on types that is then optimized by the compiler. But you can use as little or as much of it as you want.
Just because Perl has a map operator, this doesn’t make it a functional programming language.
I think Raku stayed true to this point – while there are functional elements, the polyglot approach (supporting multiple different paradigms) means that any one of them, including functional, doesn’t take over the language. But you can declare routines pure, allowing the compiler to constant fold calls to that routine when the args are known at compile time.
Perl is really hard for a machine to parse. … It’s meant to be easy for humans to understand.
Development of Raku definitely embraced this thought – “torture the implementators on behalf of the users”. This is one of the reasons it took us a while to get to here. But on that journey, we designed and developed new language parsing tools that we not only use to build and run Raku, but we expose to our users as well, allowing them to implement their own languages and “Slangs” on top of our compiler.
Finally, now that the Perl team is proposing a version jump to 7, I suspect the Perl community will raise similar concerns to those raised by Simon. Raku and Perl 7 have taken two different paths, but both will be recognizable to the Perl 5 RFC contributors from 20 years ago.
Yet another nice goodie from Damian, truly what you might expect from the interlocutor and explicator!
The fat comma operator, =>, was originally used to separate values – with a twist. It behave just like , operator did, but modified parsing to stringify left operand.
It saved you some quoting for strings and so this code for hash initialization:
| my %h = ( | |
| 'a', 1, | |
| 'b', 2, | |
| ); |
could be written as:
| my %h = ( | |
| a => 1, | |
| b => 2, | |
| ); |
Here, bare a and b are parsed correctly, without a need to quote them into strings. However, the usual hash assignment semantics is still the same: pairs of values are processed one by one, and given that => is just a “left-side stringifying” comma operator, interestingly enough the code above is equivalent to this piece:
| my %h = ( a => 1 => b => 2 => ); |
The proposal suggested changing the meaning of this “special” operator to become a constructor of a new data type, Pair.
A Pair is constructed from a key and a value:
| my @pairs = a => 42, 1 => 2; | |
| say @pairs[0]; # a => 42 | |
| say @pairs[1]; # 1 => 2; | |
| say @pairs[1].key.^name; # Int, not a Str |
The @pairs list contains just 2 values here, not 4, one is conveniently stringified for us and the second just uses bare Int literal as a key.
It turns out, introducing Pair is not only a convenient data type to operate on, but this change offers new opportunities for… subroutines.
Raku has first class support of signatures, both for the sake of the “first travel class” pun here and for the matter of it, yes, actually having Signature, Parameter and Capture as first-class objects, which allows for surprising solutions. It is not a surprise it supports named parameters with plenty of syntax for it. And Pair class has blended in quite naturally.
If a Pair is passed to a subroutine with a named parameter where keys match, it works just so, otherwise you have a “full” Pair, and if you want to insist, a bit of syntax can help you here:
| sub foo($pos, :$named) { | |
| say "$pos.gist(), $named.gist()"; | |
| } | |
| foo(42); # 42, (Any) | |
| try foo(named => 42); # Oops, no positionals were passed! | |
| foo((named => 42)); # named => 42, (Any) | |
| foo((named => 42), named => 42); # named => 42, 42 |
As we can see, designing a language is interesting: a change made in one part can have consequences in some other part, which might seem quite unrelated, and you better hope your choices will work out well when connected together. Thanks to Damian and all the people who worked on Raku design, for putting in an amazing amount of efforts into it!
And last, but not the least: what happened with the => train we saw? Well, now it does what you mean if you mean what it does:
| my %a = a => 1 => b => 2; | |
| say %a.raku; # {:a(1 => :b(2))} |
And yes, this is a key a pointing to a value of Pair of 1 pointing to a value of Pair of b pointing to value of 2, so at least the direction is nice this time. Good luck and keep your directions!
Proposed on 7 September 2000, frozen on 20 September 2000, depends on RFC 159: True Polymorphic Objects proposed on 25 August 2000, frozen on 16 September 2000, also by Nathan Wiger and already blogged about earlier.
tie anyway?RFC 200 was about extending the tie functionality as offered by Perl.
This functionality in Perl allows one to inject program logic into the system’s handling of scalars, arrays and hashes, among other things. This is done by assigning the name of a package to a data-structure such as an array (aka tying). That package is then expected to provide a number of subroutines (e.g. FETCH and STORE) that will be called by the system to achieve certain effects on the given data-structure.
As such, it is used by some of Perl’s core modules, such as threads, and many modules on CPAN, such as Tie::File. The tie functionality of Perl still suffers from the problems mentioned in the RFC.
In Raku, everything is an object, or can be considered to be an object. Everything the system needs to do with an object, is done through its methods. In that sense, you could say that everything in Raku is a tied object. Fortunately, Rakudo (the most advanced implementation of the Raku Programming Language) can recognize when certain methods on an object are in fact the ones supplied by the system, and actually create short-cuts at compile time (e.g. when assigning to a variable that has a standard container: it won’t actually call a STORE method, but uses an internal subroutine to achieve the desired effect).
But apart from that, Rakudo has the capability of identifying hot code paths during execution of a program, and optimize these in real time.
Jonathan Worthington gave two very nice presentations about this process: How does deoptimization help us go faster from 2017, and a Performance Update from 2019.
Because everything in Raku is an object and access occurs through the methods of the classes of these objects, this allows the compiler and the runtime to have a much better grasp of what is actually going on in a program. Which in turn gives better optimization capabilities, even optimizing down to machine language level at some point.
And because everything is “tied” in Raku (looking at it using Perl-filtered glasses), injecting program logic into the system’s handling of arrays and hashes can be as simple as subclassing the system’s class and providing a special version of one of the standard methods as used by the system. Suppose you want to see in your program when an element is fetched from an array, one need only add a custom AT-POS method:
class VerboseFetcher is Array { # subclass core's Array class
method AT-POS($pos) { # method for fetching an element
say "fetching #$pos"; # tell the world
nextsame # provide standard functionality
}
}
my @a is VerboseFetcher = 1,2,3; # mark as special and initialize
say @a[1]; # fetching #12
The Raku documentation contains an overview of which methods need to be supplied to emulate an Array and to emulate a Hash. By the way, the whole lemma about accessing data structure elements by index or key is recommended reading for someone wanting to grok those aspects of the internals of Raku.
In a blog post about RFC 168 about making things less special, it was already mentioned that really nothing is special in Raku. And that (almost) all aspects of the language can by altered inside a lexical scope. So what the above example did to the Array class, can be done to any of Raku’s core classes, or any other classes that have been installed from the ecosystem, or that you have written yourself.
But it can be overwhelming to have to supply all of the logic needed to fully emulate an array or a hash. Especially when you first try to do this. Therefore the ecosystem actually has two modules with roles that help you with that:
Both modules only require you to implement 5 methods in a class that does these roles to get the full functionality of an array or a hash, completely customized to your liking.
In fact, the flexibility of the approach of Raku towards customizability of the language, actually allowed the implementation of Perl’s tie built-in function in Raku. So if you’re porting code from Perl to Raku, and the code in question uses tie, you can use this module as a quick intermediate solution.
Let’s look at the problems that were mentioned with tie in RFC 200:
Raku is completely extensible and pluggable in (almost) all aspects of its implementation. There is no limitation to which classes one can and one cannot extend.
All interfaces use methods in Raku, since everything is an object or can be considered as one. Use of classes and methods should be clear to any programmer using Raku.
In Raku, it is all the same speed during execution. And every customization profits from the same optimization features like every other piece of code in Raku. And will be, in the end, optimized down to machine code when possible.
In Raku, operators are multi-dispatch subroutines that allow additional candidates for custom classes to be added.
Typeglobs don’t exist in Raku. All interfacing in Raku is done by supplying additional methods (or subroutines in case of operators). No duplication of effort is needed, so no such problem.
One may argue that the kludgey syntax of Perl has been replaced by another kludgey syntax in Raku. That is probably in the eye of the beholder. Fact is that the syntax in Raku for injecting program logic, is not different from any other subclassing or role mixins one would otherwise do in Raku.
Nothing from RFC 159 actually was implemented in the way it was originally suggested. However, solutions to the problems mentioned have all been implemented in Raku.
While adding dynvars to Shell::Piping to reduce the risk of finger injury I made a typo lizmat kindly corrected. She suggested to use the defined-or operator to test if a given dynamic variable is declared.
($*FOO // '') eq 'yes'This is not equivalent to test if a dynvar was declared down the call tree. For that we need to check CALLERS.
say CALLERS::<$*colored-exceptions>:exists;
dd CALLERS::<$*colored-exceptions>;
# OUTPUT: False
# NilIn case the dynvar is declared we get a different result.
sub dyn-receiver {
say CALLERS::<$*colored-exceptions>:exists;
dd CALLERS::<$*colored-exceptions>;
}
my $*colored-exceptions;
dyn-receiver();
# OUTPUT : True
# Any $*colored-exceptions = AnyFor a module author that means we can have somebody sneak some undefined value into a dynvar we use, that has a type we don’t expect. Composebility is not the same thing as correctness. If we want do deal with this situation properly we need to check if the caller declared the dynvar and use a proper default value if they don’t.
class Switch {
has $.name;
method gist { $.name }
method Str { die('invalid coersion') }
method Bool { die('invalid coersion') }
}
constant on is export := Switch.new: :name<on>;
constant off is export := Switch.new: :name<off>;
sub dyn-receiver {
my $*colored-exceptions = CALLERS::<$*colored-exceptions>:exists
?? CALLERS::<$*colored-exceptions>
!! off
}In this example there are just two possible values but if there are more and they can be undefined we need to be more careful. However, this is quite a bit of typing. Can we use a deboilerplater here?
sub infix:<///>(\a, \b) is raw {
my $dyn-name = a.VAR.name;
my $has-outer-dynvar = CALLER::CALLERS::{$dyn-name}:exists;
CALLER::{$dyn-name} = $has-outer-dynvar ?? CALLER::CALLERS::{$dyn-name} !! b
}
sub c {
my $*colored-exceptions /// Int;
dd $*colored-exceptions;
}
sub d {
my $*colored-exceptions = Str;
c();
}
c();
d();
# OUTPUT: Int $*colored-exceptions = Int
Str $*colored-exceptions = StrThis operator takes two bound arguments. If we call it with a dynvar a contains the container that is the dynvar. We can query the name of that container and us it to check if down the call tree the dynvar was already declared. If so we use its value and assign it directly into the dynvar declared in c. Otherwise we assign b to the dynvar. In both cases we might return something naughty so we better do so raw.
Poking across the stack is risky. This could be done better with proper macros. I am quite sure we can do so after Christmas*.
*) For any value greater then Christmas last year.
Proposed on 25 August 2000, frozen on 16 September 2000
RFC159 introduces the concept of true polymorphic object.
Objects that can morph into numbers, strings, booleans and much more on-demand. As such, objects can be freely passed around and manipulated without having to care what they contain (or even that they’re objects).
When one looks at how 42, "foo", now work in Raku nowadays, one can only see that that vision has pretty much been implemented. Because most of the time, one doesn’t really care about the fact that 42 is really an Int object, "foo" is really a Str object and that now represents a new Instant object every time it is called. The only thing one cares about, is that they can be used in expressions:
say "foo" ~ "bar"; # foobar
say 42 + 666; # 708
say now - INIT now; # 0.0005243RFC159 lists a number of method names to be used to indicate how an object should behave under certain circumstances, with a fallback provided by the system if the class of the object does not provide that method. In most cases these methods did not make it into Raku, but some of them did with a different name:
| Name in RFC | Name in Raku | When |
|---|---|---|
| STRING | Str | Called in a string context |
| NUMBER | Numeric | Called in a numeric context |
| BOOLEAN | Bool | Called in a boolean context |
And some of them even retained their name:
| Name in RFC | When |
|---|---|
| BUILD | Called in object blessing |
| STORE | Called in an lvalue = context |
| FETCH | Called in an rvalue = context |
| DESTROY | Called in object destruction |
but with sometimes subtly different semantics from the RFC.
In the end, only a limited set of special methods was decided on for Raku. All of the other methods in RFC159 have been implemented by polymorphic operators that coerce when needed. For instance the proposed PLUS method has been implemented as an infix + operator that has a “default” candidate that coerces its operands to a number.
So, effectively, if you have an object of class Foo and you want that to act as a number, one only needs to add a Numeric method to that class. An expression such as:
my $foo = Foo.new;
say $foo + 42;is effectively executing:
say infix:<+>( $foo, 42 );and the infix:<+> candidate that takes Any objects, does:
return infix:<+>( $foo.Numeric, 42.Numeric );And if such a class Foo does not provide a Numeric method, then it will throw an exception.
In Raku, object destruction is non-deterministic. If an object is no longer in use, it will probably get garbage collected. The probable part is because Raku does not know a global destruction phase, unlike Perl. So when a program is done, it just does an exit (although that logic does honour any END blocks).
An object is marked “ready for removal” when it can no longer be “reached”. It then has its DESTROY method called when the garbage collection logic kicks in. Which can be any amount of time after it became unreachable.
If you need deterministic calling of the DESTROY method, you can use a LEAVE phaser. Or if that doesn’t allow you to scratch your itch, you can possibly use the FINALIZER module.
Conceptually, you can think of a container in Raku as an object with STORE and FETCH methods. Whenever you set a value in a container, it conceptually calls the STORE method. And whenever the value inside the container is needed, it conceptually calls the FETCH method. In pseudo-code:
my $foo = 42; # Scalar.new(:name<$foo>).STORE(42)But what if you want to control access to a scalar value, similar to Perl’s tie? Well, in Raku you can, with a special type of container class called Proxy. An example of its usage:
sub proxier($value? is copy) {
return-rw Proxy.new(
FETCH => method { $value },
STORE => method ($new) {
say "storing";
$value = $new
}
)
}
my $a := proxier(42);
say $a; # 42
$a = 666; # storing
say $a; # 666Subroutines return their result values de-containerized by default. There are basically two ways of making sure the actual container is returned: using return-rw (like in this example), or by marking the subroutine with the is rw trait.
Since FETCH only makes sense on scalar values, there is no support for FETCH on compound values, such as hashes and arrays, in Raku. I guess one could consider calling FETCH in such a case to be the Zen slice, but it was decided that that would just return the compound value itself.
The STORE method on compound values however, allows for some interesting functionality. The STORE method is called whenever there is an initialization of the entire compound value. For instance:
@a = 1,2,3;basically executes:
@a := @a.STORE( (1,2,3) );But what if you don’t have an initialized @a yet? Then the STORE method is supposed to actually create a new object and initialize this with the given values. And the STORE method can tell, because then it also receives a INITIALIZE named argument with a True value. So when you write this:
my @b = 1,2,3;what basically gets executed is:
@b := Array.new.STORE( (1,2,3), :INITIALIZE );Now, if you realize that:
my @b;is actually short for:
my @b is Array;it’s only a small step to realize that you can create your own class with customized array logic, that can replace the standard Array logic with your own. Observe:
class Foo {
has @!array;
method STORE(@!array) {
say "STORED @!array[]";
self
}
}
my @b is Foo = 1,2,3; # STORED 1 2 3However, when you actually start using such an array, you are confronted with some weird results:
say @b[0]; # Foo.new
say @b[1]; # Index out of range. Is: 1, should be in 0..0Without getting into the reasons for these results, it should be clear that to completely mimic an Array, a lot more is needed. Fortunately, there are ecosystem modules available to help you with that: Array::Agnostic for arrays, and Hash::Agnostic for hashes.
The BUILD method also subtly changed its semantics. In Raku, method BUILD will be called as an object method and receive all of the parameters given to .new, after which it is fully responsible for initializing object attributes. This becomes more visible when you use the internal helper module BUILDPLAN. This module shows the actions that will be performed on an object of a class when built with the default .new method:
class Bar {
has $.score = 42;
}
use BUILDPLAN Bar;
# class Bar BUILDPLAN:
# 0: nqp::getattr(obj,Foo,'$!score') = :$score if possible
# 1: nqp::getattr(obj,Foo,'$!score') = 42 if not setThis is internals speak for: – assign the value of the optional named argument score to the $!score attribute – assign the value 42 to the $!score attribute if it was not set already
Now, if we add a BUILD method to the class, the buildplan changes:
class Bar {
has $.score = 42;
method BUILD() { }
}
use BUILDPLAN Bar;
# class Bar BUILDPLAN:
# 0: call obj.BUILD
# 1: nqp::getattr(obj,Foo,'$!score') = 42 if not setNote that there is no automatic attempt to take the value of the named argument score anymore. Which means that you need to do a lot of work in your custom BUILD method if you have many named arguments, and only one of them needs special handling. That’s why the TWEAK method was added:
class Bar {
has $.score = 42;
method TWEAK() { }
}
use BUILDPLAN Bar;
# class Bar BUILDPLAN:
# 0: nqp::getattr(obj,Foo,'$!score') = :$score if possible
# 1: nqp::getattr(obj,Foo,'$!score') = 42 if not set
# 2: call obj.TWEAKNote that the TWEAK method is called after all of the normal checks and initializations. This is in most cases much more useful.
Although the idea of true polymorphic objects has been implemented in Raku, it turned out quite different from originally envisioned. In hindsight, one can see why it was decided to be unpractical to try to support an ever increasing list of special methods for all objects. Instead, a choice was made to only implement a few key methods from the proposal, and for the others the approach of automatic coercions was taken.
I’m not the blogging kind of person and usually don’t post without a good reason. For a long while even a good reason wasn’t enough for me to write something. But things are changing, and this subject I should have mentioned earlier.
We’re currently in the process of forming The Raku Steering Council which is considered as a potentially effective governance model for the language and the community. It’s aimed at taking off load from the shoulders of Jonathan Worthington who currently bears the biggest responsibility for the vast majority of problems the community and the language development encounter.
The biggest advantages of the Council as I see them are:
Disclaimer: everything stated above is my personal view of the situation which is to be sumed up as: the damn good thing is happening!
To the point! The Council is not an elite closed club. Anybody can nominate himself! Just read the election announce.
BTW, the announce currently states the Aug 2 is the last date to nominate. This is about to change to Sep 6. Still, don’t procrastinate too much, let the community know about your nomination and yourself!
I’m publishing the next article from ARFB series. This time rather short one, much like a warm up prior to the main workout.
But I’d like to devote this post to another subject. It’s too small for an article yet still worth special note. It was again inspired by one more post from Wenzel P.P. Peppmeyer. Actually, I knew there going to be a new post from him when found an error report in Rakudo repository. And this is the subject of the report which made me write the post.
In the report Wenzel claims that the following code results in incorrect Rakudo behaviour:
class C { };
my \px = C.new;
sub postcircumfix:«< >»(C $c is raw) {
dd $c;
}
px<ls -l>;
And that either the operator redefinition must work or the error message he gets is less than awesome:
===SORRY!=== Error while compiling /home/dex/projects/raku/lib/raku-shell-piping/px.raku Missing required term after infix at /home/dex/projects/raku/lib/raku-shell-piping/px.raku:9 ------> px<ls -l>⏏; expecting any of: prefix term
Before I tell why things are happening as intended here, let me notice two
problems with the code itself which I copied over as-is since it doesn’t work
anyway. First, the postcircumfix sub must be a multi and in Wenzel’s post it
is done correctly. Second, it must receive two arguments: first is the object it
is applied to, second is what is enclosed into the angle brakets.
So, why won’t it work as one might expect? In Raku there is a class of syntax
constructs which look like operators but in fact they’re syntax sugars. There
may be different reasons why is it done so. For example, the assignment operator
= is done this way to achieve better performance. < > makes what is inclosed
inside of it a string or a list of strings. Because of this it belongs to the
same category, as quotes "", for example. Therefore, it can only be
implemented properly as a syntax construct. When we try to redefine it we break
the compiler’s parsing and instead of a postcircumfix it finds a pair of less
than and greater than operators. Because the latter doesn’t have rhs
statement hence the error we see.
And you know, it was really useful to make this post as I realized that closing of the tickat was preliminary and that such compiler behavior is still incorrect because the attempt to redefine the op should prbably not result in bad parsing.
A new article of Advanced Raku For Beginners series is published now. With a really surprising subject this time! It is about how we define Raku. Or, in other words: how one knows that his code is Raku? Why a compiler has the right to state that it is actually compiling Raku? And a few side concepts to provide grounds for the main topic.
It may seem to be a bit late. But keeping in mind that the article refers back to a few concepts from previous publications, it’s probably right the time for it!
Enjoy and don’t forget to correct me whenever necessary!
Good, that's the click-baity title out of the way. Sorry for taking such a long time to write again! There really has been everything going on.
To get back into blogging, I've decided to quickly write about a change I made some time ago already.
This change was for the "instrumented profiler", i.e. the one that will at run-time change all the code of the user's program, in order to measure execution times and count up calls and allocations.
In order to get everything right, the instrumented profiler keeps an entire call graph in memory. If you haven't seen something like it yet, imagine taking stack traces at every point in your program's life, and all these stack traces put together make all the paths in the tree that point at the root.
This means, among other things, that the same function can come up multiple times. With recursion, the same function can in fact come up a few hundred times "in a row". In general, if your call tree can become both deep and wide, you can end up with a whole truckload of nodes in your tree.
Is it a bad thing to have many nodes? Of course, it uses up memory. Only a single path on the tree is ever interesting at any one moment, though. Memory that's not read from or written to is not quite as "expensive". It never has to go into the CPU cache, and is even free to be swapped out to disk and/or compressed. But hold on, is this really actually the case?
It turns out that when you're compiling the Core Setting, which is a code file almost 2½ megabytes big with about 71½ thousand lines, and you're profiling during the parsing process, the tree gets enormous. At the same time, the parsing process slows to a crawl. What on earth is wrong here?
Well, looking at what MoarVM spends most of its time doing while the profiler runs gives you a good hint: It's spending almost all of its time going through the entirety of the tree for garbage collection purposes. Why would it do that, you ask? Well, in order to count allocated objects at every node, you have to match the count with the type you're allocating, and that means you need to hold on to a pointer to the type, and that in turn has to be kept up to date if anything moves (which the GC does to recently-born things) and to make sure types aren't considered unused and thrown out.
That's bad, right? Isn't there anything we can do? Well, we have to know at every node which counter belongs to which type, and we need to give all the types we have to the garbage collector to manage. But nothing forces us to have the types right next to the counter. And that's already the solution to the problem:
Holding on to all types is now the job of a little array kept just once per tree, and next to every counter there's just a little number that tells you where in the array to look.
This increases the cost of recording an allocation, as you'll now have to go to a separate memory location to match types to counters. On the other hand, the "little number" can be much smaller than before, and that saves memory in every node of the tree.
More importantly, the time cost of going through the profiler data is now independent of how big the tree is, since the individual nodes don't have to be looked at at all.
With a task as big as parsing the core setting, which is where almost every type, exception, operator, or sub lives, the difference is a factor of at least a thousand. Well, to be honest I didn't actually calculate the difference, but I'm sure it's somewhere between 100x faster and 10000x faster, and going from "ten microseconds per tree node" to "ten microseconds per tree" isn't a matter of a single factor increase, it's a complexity improvement from O(n) to O(1). As long as you can find a bigger tree, you can come up with a higher improvement factor. Very useful for writing that blog post you've always wanted to put at the center of a heated discussion about dishonest article titles!
Anyway, on testing my patch, esteemed colleague MasterDuke had this to say on IRC:
timotimo: hot damn, what did you do?!?! stage parse only took almost twice as long (i.e., 60s instead of the normal 37s) instead of the 930s last time i did the profile
(psst, don't check what 930 divided by 60 is, or else you'll expose my blog post title for the fraud that it is!)
Well, that's already all I had for this post. Thanks for your attention, stay safe, wear a mask (if by the time you're reading this the covid19 pandemic is still A Thing, or maybe something new has come up), and stay safe!
It was an emotional moment to see the keynote talk at TPRCiC from Sawyer X announcing that perl 7.00 === 5.32. Elation because of the ability of the hardcore perl community to finally break free of the frustrating perl6 roadblock. Pleasure in seeing how the risky decision to rename perl6 to raku has paid off and hopefully is beginning to defuse the tensions between the two rival communities. And Fear that improvements to perl7 will undermine the reasons for many to try out raku and may cannibalise raku usage. (Kudos to Sawyer to recognising that usage is an important design goal).
Then the left side of my brain kicked in. Raku took 15 years of total commitment of genius linguists to ingest 361 RFCs and then synthesise a new kind of programming language. If perl7 seeks the same level of completeness and perfection as raku, surely that will take the same amount of effort. And I do not see the perl community going for the same level of breaking changes that raku did. (OK maybe they could steal some stuff from raku to speed things up…)
And that brought me to Sadness. To reflect that perl Osborned sometime around 2005. That broke the community in two – let’s say the visionaries and the practical-cats. And it drove a mass emigration to Python. Ancient history.
So now we have two sister languages, and each will find a niche in the programming ecosystem via a process of Darwinism. They both inherit the traits (https://en.wikipedia.org/wiki/Perl#Design) that made perl great in the first place….
The design of Perl can be understood as a response to three broad trends in the computer industry: falling hardware costs, rising labor costs, and improvements in compiler technology. Many earlier computer languages, such as Fortran and C, aimed to make efficient use of expensive computer hardware. In contrast, Perl was designed so that computer programmers could write programs more quickly and easily.
Perl has many features that ease the task of the programmer at the expense of greater CPU and memory requirements. These include automatic memory management; dynamic typing; strings, lists, and hashes; regular expressions; introspection; and an eval() function. Perl follows the theory of “no built-in limits,” an idea similar to the Zero One Infinity rule.
Wall was trained as a linguist, and the design of Perl is very much informed by linguistic principles. Examples include Huffman coding(common constructions should be short), good end-weighting (the important information should come first), and a large collection of language primitives. Perl favours language constructs that are concise and natural for humans to write.
Perl’s syntax reflects the idea that “things that are different should look different.” For example, scalars, arrays, and hashes have different leading sigils. Array indices and hash keys use different kinds of braces. Strings and regular expressions have different standard delimiters. This approach can be contrasted with a language such as Lisp, where the same basic syntax, composed of simple and universal symbolic expressions, is used for all purposes.
Perl does not enforce any particular programming paradigm (procedural, object-oriented, functional, or others) or even require the programmer to choose among them.
But perl7 and raku serve distinct interests & needs:
| Thing… | perl7 | raku |
| compilation | static parser | one pass compiler |
| compile speed | super fast | relies on pre-c0mp |
| execution | interpreted | virtual machine |
| execution speed | super fast | relies on jit |
| module library | CPAN native | CPAN import |
| closures | yes | yes |
| OO philosophy | Cor not module | pervasive |
| OO inheritance | Roles + Is | Roles + Is + multiple |
| method invocation | -> | . |
| type checking | no | gradual |
| sigils | idiosyncratic | consistent |
| references | manual | automatic |
| unicode | feature guard | core |
| signatures | feature guard | core |
| lazy execution | nope | core |
| Junctions | nope | core |
| Rat math | nope | core |
| Sets & Mixes | nope | core |
| Complex math | nope | core |
| Grammars | nope | core |
| mutability | nope | core |
| concurrency | nope | core |
| variable scope | “notched” | cleaner |
| operators | C-like | cleaner (e.g. for ->) |
| switch | no | gather/when |
| regexen | classic | cleaner |
| eval | yes | shell |
| AST macros | huh? | … |
A long list and perhaps a little harsh on perl since many things may be got from CPAN – but when you use raku in anger, you do see the benefit if having a large core language. Only when I made this table, did I truly realise just what a comprehensive language raku is, and that perl will genuinely be the lean and mean option.
And, lest we forget our strengths:
When I first saw Python code, I thought that using indents to define the scope seemed like a good idea. However, there’s a huge downside. Deep nesting is permitted, but lines can get so wide that they wrap lines in the text editor. Long functions and long conditional actions may make it hard to match the start to the end. And I pity anyone who miscounts spaces and accidentally puts in three spaces instead of four somewhere — this can take hours to debug and track down. [Source: here]
Coming from Python, the Raku object model is recognizable, but brings a tad more structure:
What works for me, as a convenience seeker, is:
These are the things you want if you are writing in a more procedural or functional style and using class as a means to define a record type.
Here’s the rub…
When we describe OO, terms like “encapsulation” and “data hiding” often come up. The key idea here is that the state model inside the object – that is, the way it chooses to represent the data it needs in order to implement its behaviours (the methods) – is free to evolve, for example to handle new requirements. The more complex the object, the more liberating this becomes.
However, getters and setters are methods that have an implicit connection with the state. While we might claim we’re achieving data hiding because we’re calling a method, not accessing state directly, my experience is that we quickly end up at a place where outside code is making sequences of setter calls to achieve an operation – which is a form of the feature envy anti-pattern. And if we’re doing that, it’s pretty certain we’ll end up with logic outside of the object that does a mix of getter and setter operations to achieve an operation. Really, these operations should have been exposed as methods with a names that describes what is being achieved. This becomes even more important if we’re in a concurrent setting; a well-designed object is often fairly easy to protect at the method boundary.
(source jnthn https://stackoverflow.com/questions/59671027/mixing-private-and-public-attributes-and-accessors-in-raku)
Let’s fix that:
Now, I had to do a bit more lifting, but here’s what I got:
And, in contrast to Chapter 1:
I also discovered Larry’s First Law of Language Redesign: Everyone wants the colon
Apocalypse 1: The Ugly, the Bad, and the Good https://www.perl.com/pub/2001/04/02/wall.html/
I conclude that Larry’s decision was to confer the colon on the method syntax, subtly tilting the balance towards the strict model: by preferring $p.y: 3 over $p.y = 2.
Having hit rock bottom with an ‘I can’t understand my own code sufficiently enough to extend/maintain it’, I have been on a journey to review the perl5 Physics::Unit design and to use this to cut through my self made mess of raku Physics::Unit version 0.0.2.
Now I bring the perspective of a couple of years of regular raku coding to bear, so I am hoping that the bastard child of mature perl5 and raku version one will surpass both in the spirit of David Bowie’s “Pretty Things”.
One of the reasons I chose Physics::Units as a project was that, on the face of it, it seemed to have an aspect that could be approached by raku Grammars – helping me learn them. Here’s a sample of the perl5 version:
Yes – a recursive descent parser written from scratch in perl5 – pay dirt! There are 215 source code lines dedicated to the parse function. 5 more screens like this…
So I took out my newly sharpened raku tools and here’s my entire port:
Instead of ranging over 215 lines, raku has refined this down to a total of 58 lines (incl. the 11 blank ones I kept in for readability) – that’s a space saving of over 70%. Partly removal of parser boilerplate code, partly the raku Grammar constructs and partly an increased focus on the program logic as opposed to the mechanism.
For my coding style, this represents a greater than a two-thirds improvement – by getting the whole parser onto a single screen, I find that I can get the whole problem into my brain’s working memory and avoid burning cycles scrolling up and down to pin down butterflies bugs.
Attentive students will have noted that the Grammar / code integration provides a very natural paradigm for loading real-world data into an OO system, the UnitAction class starts with a stub object and populates ‘has’ attributes as it goes.
Oh and the raku code does a whole lot more such as support for unicode superscripts (up to +/-4), type assignment and type checking, offsets (such as 0 K = 273.15 °C), wider tolerance of user input and so on. Most importantly Real values are kept as Rats as much as possible which helps greatly for example, when round tripping 38.5 °C to °F and back it is still equals 38.5 °C!
One final remark – use Grammar::Tracer is a fantastic debugging tool for finding and fixing the subtle bugs that can come in and contributing to quickly getting to the optimum solution.
For anyone wondering where my occasional blog on raku has been for a couple of months – sorry. I have been busy wrestling with, and losing to, the first released version of my Physics::Measure module.
Of course, this is all a bit overshadowed by the name change from perl6 to raku. I was skeptical on this, but did not have a strong opinion either way. So kudos to the folks who thrashed this out and I am looking forward to a naissance. For now, I have decided to keep my nickname ‘p6steve’ – I enjoy the resonance between P6 and P–sics and that is my niche. No offence intended to either camp.
My stated aim (blogs passim) is to create a set of physical units that makes sense for high school education. To me, inspired by the perl5 Physics::Unit module, that means not just core SI units for science class, but also old style units like sea miles, furlongs/fortnight and so on for geography and even history. As I started to roll out v0.0.3 of raku Physics::Unit, I thought it would be worthwhile to track a real-world high school education resource, namely OpenStax CNX. As I came upon this passage, I had to take the firkin challenge on:
While there are numerous types of units that we are all familiar with, there are others that are much more obscure. For example, a firkin is a unit of volume that was once used to measure beer. One firkin equals about 34 liters. To learn more about nonstandard units, use a dictionary or encyclopedia to research different “weights and measures.” Take note of any unusual units, such as a barleycorn, that are not listed in the text. Think about how the unit is defined and state its relationship to SI units.
Disaster – I went back to the code for Physics::Unit and, blow me, could I figure out how to drop in a new Unit: the firkin??…. nope!! Why not? Well Physics::Unit v:0.0.3 was impenetrable even to me, the author. Statistically it has 638 lines of code alongside 380 lines of heredoc data. Practically, while it passes all the tests 100%, it is not a practical, maintainable code base.
How did we get here? Well I plead guilty to being an average perl5 coder who really loves the expressivity that Larry offers … but a newbie to raku. I wanted to take on Physics::Measure to learn raku. Finally, I have started to get raku – but it has taken me a couple of years to get to this point!
My best step now – bin the code. I have dumped my original effort, gone back to the original perl5 Physics::Unit module source and transposed it to raku. The result: 296 lines of tight code alongside the same 380 lines of heredoc – a reduction of 53%! And a new found respect for the design skill of my perl5 forbears.
I am aiming to release as v0.0.7 in April 2020.
By the way, you could replace … * with Inf or the unicode infinity symbol ∞ to make it more readable, i.e.
my @a = 1, 1, * + * … ∞;
— — or — —
my @a = 1, 1, * + * … Inf;
As I understand this, * + * … * means the following:
First— * + * sums the two previous elements in the list. … * tells this to do this an infinite number of times; i.e.
1, 1, (1 + 1)
1, 1, 2, (1 + 2)
1, 1, 2, 3, (2 + 3)
1, 1, 2, 3, 5, (3 + 5)
1, 1, 2, 3, 5, 8, (5 + 8), etc.
… three dots means that it does it lazy, i.e. that it does not generate an element before you call it. This can be good for large lists that are computationally heavy.
Hello everyone! In the last report I said that just a little bit of work on the heap snapshot portion of the UI should result in a useful tool.
Photo by Sticker Mule / Unsplash
Here's my report for the first useful pieces of the Heap Snapshot UI!
Last time you already saw the graphs showing how the number of instances of a given type or frame grow and shrink over the course of multiple snapshots, and how new snapshots can be requested from the UI.
The latter now looks a little bit different:
Each snapshot now has a little button for itself, they are in one line instead of each snapshot having its own line, and the progress bar has been replaced with a percentage and a little "spinner".
There are multiple ways to get started navigating the heap snapshot. Everything is reachable from the "Root" object (this is the norm for reachability-based garbage collection schemes). You can just click through from there and see what you can find.
Another way is to look at the Type & Frame Lists, which show every type or frame along with the number of instances that exist in the heap snapshot, and the total size taken up by those objects.
Clicking on a type, or the name or filename of a frame leads you to a list of all objects of that type, all frames with the given name, or all frames from the given file. They are grouped by size, and each object shows up as a little button with the ID:
Clicking any of these buttons leads you to the Explorer.
Here's a screenshot of the explorer to give you an idea of how the parts go together that I explain next:
The explorer is split into two identical panels, which allows you to compare two objects, or to explore in multiple directions from one given object.
There's an "Arrow to the Right" button on the left pane and an "Arrow to the Left" button on the right pane. These buttons make the other pane show the same object that the one pane currently shows.
On the left of each pane there's a "Path" display. Clicking the "Path" button in the explorer will calculate the shortest path to reach the object from the root. This is useful when you've got an object that you would expect to have already been deleted by the garbage collector, but for some reason is still around. The path can give the critical hint to figure out why it's still around. Maybe one phase of the program has ended, but something is still holding on to a cache that was put in as an optimization, and that still has your object in it? That cache in question would be on the path for your object.
The other half of each panel shows information about the object: Displayed at the very top is whether it is an object, a type object, an STable, or a frame.
Below that there is an input field where you can enter any ID belonging to a Collectable (the general term encompassing types, type objects, stables, and frames) to have a look.
The "Kind" field needs to have the number values replaced with human-readable text, but it's not the most interesting thing anyway.
The "Size" of the Collectable is split into two parts. One is the fixed size that every instance of the given type has. The other is any extra data an instance of this type may have attached to it, that's not a Collectable itself. This would be the case for arrays and hashes, as well as buffers and many "internal" objects.
Finally, the "References" field shows how many Collectables are referred to by the Collectable in question (outgoing references) and how many Collectables reference this object in question.
Below that there are two buttons, Path and Network. The former was explained further above, and the latter will get its own little section in this blog post.
Finally, the bottom of the panel is dedicated to a list of all references - outgoing or incoming - grouped by what the reference means, and what type it references.
In this example you see that the frame of the function display from elementary2d.p6 on line 87 references a couple of variables ($_, $tv, &inv), the frame that called this frame (step), an outer frame (MAIN), and a code object. The right pane shows the incoming references. For incoming references, the name of the reference isn't available (yet), but you can see that 7 different objects are holding a reference to this frame.
The newest part of the heap snapshot UI is the Network View. It allows the user to get a "bird's eye" view of many objects and their relations to each other.
Here's a screenshot of the network view in action:
The network view is split into two panes. The pane on the left lists all types present in the network currently. It allows you to give every type a different symbol, a different color, or optionally make it invisible. In addition, it shows how many of each type are currently in the network display.
The right pane shows the objects, sorted by how far they are from the root (object 0, the one in Layer 0, with the frog icon).
Each object has one three-piece button. On the left of the button is the icon representing the type, in the middle is the object ID for this particular object, and on the right is an icon for the "relation" this object has to the "selected" object:
This view was generated for object 46011 (in layer 4, with a hamburger as the icon). This object gets the little "map marker pin" icon to show that it's the "center" of the network. In layers for distances 3, 2, and 1 there is one object each with a little icon showing two map marker pins connected with a squiggly line. This means that the object is part of the shortest path to the root. The third kind of icon is an arrow pointing from the left into a square that's on the right. Those are objects that refer to the selected object.
There is also an icon that's the same but the arrow goes outwards from the square instead of inwards. Those are objects that are referenced by the selected object. However, there is currently no button to have every object referenced by the selected object put into the network view. This is one of the next steps I'll be working on.
Customizing the colors and visibility of different types can give you a view like this:
And here's a view with more objects in it:
Interesting observations from this image:
You have no doubt noticed that the buttons for collectables are very different between the network view and the type/frame lists and the explorer. The reason for that is that I only just started with the network view and wanted to display more info for each collectable (namely the icons to the left and right) and wanted them to look nicer. In the explorer there are sometimes thousands of objects in the reference list, and having big buttons like in the network view could be difficult to work with. There'll probably have to be a solution for that, or maybe it'll just work out fine in real-world use cases.
On the other hand, I want the colors and icons for types to be available everywhere, so that it's easier to spot common patterns across different views and to mark things you're interested in so they stand out in lists of many objects. I was also thinking of a "bookmark this object" feature for similar purposes.
Before most of that, the network viewer will have to become "navigable", i.e. clicking on an object should put it in the center, grab the path to the root, grab incoming references, etc.
There also need to be ways to handle references you're not (or no longer) interested in, especially when you come across an object that has thousands of them.
But until then, all of this should already be very useful!
Here's the section about the heap snapshot profiler from the original grant proposal:
Looking at the list, it seems like the majority of intended features are already available or will be very soon!
Until now the user had to download nodejs and npm along with a whole load of javascript libraries in order to compile and bundle the javascript code that powers the frontend of moarperf.
Fortunately, it was relatively easy to get travis-ci to do the work automatically and upload a package with the finished javascript code and the backend code to github.
You can now visit the releases page on github to grab a tarball with all the files you need! Just install all backend dependencies with zef install --deps-only . and run service.p6!
And with that I'm already done for this report!
It looks like the heap snapshot portion of the grant is quite a bit smaller than the profiler part, although a lot of work happened in moarvm rather than the UI. I'm glad to see rapid progress on this.
I hope you enjoyed this quick look at the newest pieces of moarperf!
- Timo
One of the most exciting parts of blogging about and hacking on perl6* is that there’s a community out there and there’s (always) more than one way to do it!
For Physics::Measure I have been working on a design for a ‘nano-slang’ that can provide a shortcut for the usual new Class declaration… quite long winded for my target audience of physics undergraduates and high school students.
#Instances the usual way
my Unit $u .=new(name => ‘m’, unitsof => ‘Distance’); #Unit m
my Distance $a .=new(value => 10, units => $u); #Distance 10 m
So, duly inspired by Rakudo perl6 going atomic 
applying unicode to some threading constructs, I started with the notion of the ‘libra’ operator 
as shorthand to declare and load Measure instances.
#Introducing the libra operator
my $b
$b
As you can see, the gap created between and ; is a slang zone that can consume strings and numeric literals. Here’s something a bit more elaborate:
#Normalization with the .norm method
my $p
$p .= norm; #Power 27 W
A few design ideas drew me in this direction:
unicode would be a good convention to warn coders that there is something substantive happening to the core raku language..
# Resistance
[‘Ω’, ‘Ohm:s’,], ’kg m^2 / A^2 s^3′,
[‘kilohm:s’,], ’kilo Ohm’,
[‘megohm:s’,], ’mega Ohm’,
HOWEVER!!
Others have proposed a much more direct approach to generate and combine Measure objects – by the use of a simple postfix syntax – thank you!
Something like:
say 500g; # –> Weight.new(grams => 500, prefix => “”)
say 2kg; # –> Weight.new(grams => 2000, prefix => “kg”)
Watch this space! Or even better zef Physics::Measure and give it a try…
~p6steve
* soon to be Rakudo?!
It has been a while since the last progress report, hasn't it?
Over the last few months I've been focusing on the MoarVM Heap Snapshot Profiler. The new format that I explained in the last post, "Intermediate Progress Report: Heap Snapshots", is available in the master branch of MoarVM, and it has learned a few new tricks, too.
The first thing I usually did when opening a Heap Snapshot in the heapanalyzer (the older command-line based one) was to select a Snapshot, ask for the summary, and then for the top objects by size, top objects by count, top frames by size, and/or top frames by count to see if anything immediately catches my eye. In order to make more sense of the results, I would repeat those commands for one or more other Snapshots.
Snapshot Heap Size Objects Type Objects STables Frames References
======== ================= ======= ============ ======= ====== ==========
0 46,229,818 bytes 331,212 686 687 1,285 1,146,426
25 63,471,658 bytes 475,587 995 996 2,832 1,889,612
50 82,407,275 bytes 625,958 1,320 1,321 6,176 2,741,066
75 97,860,712 bytes 754,075 1,415 1,416 6,967 3,436,141
100 113,398,840 bytes 883,405 1,507 1,508 7,837 4,187,184
Snapshot Heap Size Objects Type Objects STables Frames References
======== ================= ========= ============ ======= ====== ==========
125 130,799,241 bytes 1,028,928 1,631 1,632 9,254 5,036,284
150 145,781,617 bytes 1,155,887 1,684 1,685 9,774 5,809,084
175 162,018,588 bytes 1,293,439 1,791 1,792 10,887 6,602,449
Realizing that the most common use case should be simple to achieve, I first implemented a command summary all and later a command summary every 10 to get the heapanalyzer to give the summaries of multiple Snapshots at once, and to be able to get summaries (relatively) quickly even if there's multiple hundreds of snapshots in one file.
Sadly, this still requires the parser to go through the entire file to do the counting and adding up. That's obviously not optimal, even though this is an Embarrassingly Parallel task, and it can use every CPU core in the machine you have, it's still a whole lot of work just for the summary.
For this reason I decided to shift the responsibility for this task to MoarVM itself, to be done while the snapshot is taken. In order to record everything that goes into the Snapshot, MoarVM already differentiates between Object, Type Object, STable, and Frame, and it stores all references anyway. I figured it shouldn't have a performance impact to just add up the numbers and make them available in the file.
The result is that the summary table as shown further above is available only milliseconds after loading the heap snapshot file, rather than after an explicit request and sometimes a lengthy wait period.
The next step was to see if top objects by size and friends could be made faster in a similar way.
I decided that adding an optional "statistics collection" feature inside of MoarVM's heap snapshot profiler would be worthwhile. If it turns out that the performance impact of summing up sizes and counts on a per-type and per-frame basis makes capturing a snapshot too slow, it could be turned off.
> snapshot 50
Loading that snapshot. Carry on...
> top frames by size
Wait a moment, while I finish loading the snapshot...
Name Total Bytes
==================================== =============
finish_code_object (World.nqp:2532) 201,960 bytes
moarop_mapper (QAST.nqp:1764) 136,512 bytes
!protoregex (QRegex.nqp:1625) 71,760 bytes
new_type (Metamodel.nqp:1345) 40,704 bytes
statement (Perl6-Grammar.nqp:951) 35,640 bytes
termish (Perl6-Grammar.nqp:3641) 34,720 bytes
<anon> (Perl6-BOOTSTRAP.c.nqp:1382) 29,960 bytes
EXPR (Perl6-Grammar.nqp:3677) 27,200 bytes
<mainline> (Perl6-BOOTSTRAP.c.nqp:1) 26,496 bytes
<mainline> (NQPCORE.setting:1) 25,896 bytes
EXPR (NQPHLL.nqp:1186) 25,760 bytes
<anon> (<null>:1) 25,272 bytes
declarator (Perl6-Grammar.nqp:2189) 23,520 bytes
<anon> (<null>:1) 22,464 bytes
<anon> (<null>:1) 22,464 bytes
Showing the top objects or frame for a single snapshot is fairly straight-forward in the commandline based UI, but how would you display how a type or frame develops its value across many snapshots?
Instead of figuring out the best way to display this data in the commandline, I switched focus to the Moarperf Web Frontend. The most obvious way to display data like this is a Line Graph, I believe. So that's what we have now!
And of course you also get to see the data from each snapshot's Summary in graph format:
And now for the reason behind this blog post's Title.
Using Jonathan's module Concurrent::Progress (with a slight modification) I sprinkled the code to parse a snapshot with matching calls of .increment-target and .increment. The resulting progress reports (throttled to at most one per second) are then forwarded to the browser via the WebSocket connection that already delivers many other bits of data.
The result can be seen in this tiny screencast:
The recording is rather choppy because the heapanalyzer code was using every last drop of performance out of my CPU while it was trying to capture my screen.
There's obviously a lot still missing from the heap snapshot analyzer frontend GUI, but I feel like this is a good start, and even provides useful features already. The graphs for the summary data are nicer to read than the table in the commandline UI, and it's only in this UI that you can get a graphical representation of the "highscore" lists.
I think a lot of the remaining features will already be useful after just the initial pieces are in place, so a little work should go a long way.
I didn't spend the whole time between the last progress report and now to work directly on the features shown here. Apart from Life Intervening™, I worked on fixing many frustrating bugs related to both of the profilers in MoarVM. I added a small subsystem I call VMEvents that allows user code to be notified when GC runs happen and other interesting bits from inside MoarVM itself. And of course I've been assisting other developers by answering questions and looking over their contributions. And of course there's the occasional video-game-development related experiment, for example with the GTK Live Coding Tool.
Finally, here's a nice little screencap of that same tool displaying a hilbert curve:
That's already everything I have for this time. A lot has (had to) happen behind the scenes to get to this point, but now there was finally something to look at (and touch, if you grab the source code and go through the needlessly complicated build process yourself).
Thank you for reading and I hope to see you in the next one!
- Timo
You’re right. I’ll let the article stand as it is and reflect my ignorance as it was when I wrote it :-)
This week’s Perl Weekly Challenge (#19) has two tasks. The first is to find all months with five weekends in the years from 1900 through 2019. The second is to program an implementation of word wrap using the greedy algorithm.
Both are pretty straight-forward tasks, and the solutions to them can (and should) be as well. This time, however, I’m also going to do the opposite and incrementally turn the easy solution into an unnecessarily complex one. Because in this particular case we can learn more by doing things the unnecessarily hard way. So this post will take a look at Dates and date manipulation in Perl 6, using PWC #19 task 1 as an example:
Write a script to display months from the year 1900 to 2019 where you find 5 weekends i.e. 5 Friday, 5 Saturday and 5 Sunday.
Let’s start by finding five-weekend months the easy way:
#!/usr/bin/env perl6
say join "\n", grep *.day-of-week == 5, map { Date.new: |$_, 1 }, do 1900..2019 X 1,3,5,7,8,10,12;The algorithm for figuring this out is simple. Given the prerequisite that there must be five occurrences of not only Saturday and Sunday but also Friday, you A) *must* have 31 days to cram five weekends into. And when you know that you’ll also see that B) the last day of the month MUST be a Sunday and C) the first day of the month MUST be a Friday (you don’t have to check for both; if A is true and B is true, C is automatically true too).
The code above implements B and employs a few tricks. You read it from right to left (unless you write it from left to right, like this… say do 1900..2019 X 1,3,5,7,8,10,12 ==> map { Date.new: |$_, 1 } ==> grep *.day-of-week == 5 ==> join “\n”; )
Using the X operator I create a cross product of all the years in the range 1900–2019 and the months 1, 3, 5, 7, 8, 10, 12 (31-day months). In return I get a sequence containing all year-month pairs of the period.
The map function iterates through the Seq. There it instantiates a Date object. A little song and dance is necessary: As Date.new takes three unnamed integer parameters, year, month and day, I have to do something to what I have — a Pair with year and month. I therefore use the | operator to “explode” the pair into two integer parameters for year and month.
You can always use this for calling a sub routine with fixed parameters, using an array with parameter values rather than having separate variables for each parameter. The code below exemplifies usage:
my @list = 1, 2, 3;
sub explode-parameters($one, $two, $three) {
…do something…
}#traditional call
explode-parameters(@list[0], @list[1], @list[2]);
# …or using |
explode-parameters(|@list);
Back to the business at hand — the .grep filters out the months where the 1st is a Friday, and those are our 5 weekend months. So the output of the one-liner above looks something like this:
...
1997-08-01
1998-05-01
1999-01-01
...
This is a solution as good as any, and if a solution was all we wanted, we could have stopped here. But using this task as an example I want to explore ways to utilise the Date class. Example: The one-liner above does the job, but strictly speaking it doesn’t output the months but the first day of those months. Correcting this is easy, because the Date class supports something called formatters and use the sprintf syntax. To do this you utilise the named parameter “formatter” when instantiating the object.
say join "\n", grep *.day-of-week == 5, map { Date.new: |$_, 1, formatter => { sprintf "%04d/%02d", .year, .month } }, do 1900..2019 X 1,3,5,7,8,10,12;Every time a routine pulls a stringified version of the date, the formatter object is invoked. In our case the output has been changed to…
...
1997/08
1998/05
1999/01
...
Formatters are powerful. Look into them.
Now to the overly complex solution. This is the unthinking programmer’s solution, as we don’t suppose anything. The program isn’t told that 5 weekend months only can occur on 31 day months. It doesn’t know that the 1st of such months must be a Friday. All it knows is that if the last day of the month is not Sunday, it figures out the date of the last Sunday (this is not very relevant when counting three-day weekends, but could be if you want to find Saturday+Sunday weekends, or only Sundays).
#!/usr/bin/env perl6
my $format-it = sub ($self) {
sprintf "%04d month %02d", .year, .month given $self;
}sub MAIN(Int :$from-year = 1900, Int :$to-year where * > $from-year = 2019, Int :$weekend-length where * ~~ 1..3 = 3) {
my $date-loop = Date.new($from-year, 1, 1, formatter => $format-it);
while ($date-loop.year <= $to-year) {
my $date = $date-loop.later(day => $date-loop.days-in-month);
$date = $date.truncated-to('week').pred if $date.day-of-week != 7;
my @weekend = do for 0..^$weekend-length -> $w {
$date.earlier(day => $w).weekday-of-month;
};
say $date-loop if ([+] @weekend) / @weekend == 5;
$date-loop = $date-loop.later(:1month);
}
}This code can solve the task both for three day weekends, but also for weekends consisting of Saturday + Sunday, as well as only Sundays. You control that with the command line parameter weekend-length=[1..3].
This code finds the last Sunday of each month and counts whether it has occured five times that month. It does the same for Saturday (if weekend-length=2) and Friday (if weekend-length=3). Like this:
my @weekend = do for 0..^$weekend-length -> $w {
$date.earlier(day => $w).weekday-of-month;
};The code then calculcates the average weekday-of-month for these three days like this:
say $date-loop if ([+] @weekend) / @weekend == 5;
This line uses the reduction operator [+] on the @weekend list to find the sum of all elements. That sum is divided by the number of elements. If the result is 5, then you have a five day weekend.
As for fun stuff to do with the Date object:
.later(day|month|year => Int) — adds the given number of time units to the current date. There’s also an earlier method for subtracting.
.days-in-months — tells you how many days there are in the current month of the Date object. The value may be 31, 30, 29 (february, leap year) or 28 (february).
.truncated-to(week|month|day|year) — rolls the date back to the first day of the week, month, day or year.
.weekday-of-month — figures out what day of week the current date is and calculates how many of that day there has been so far in that month.
Apart from this you’ll see that I added the formatter in a different way this time. This is probably cleaner looking and easier to maintain.
In the end this post maybe isn’t about dates and date manipulation at all, but rather is a call for all of us to use the documentation even more. It’s often I think that Perl 6 should have a function for x, y or z — .weekday-of-month is one such example — and the documentation tells me that it actually does!
It’s very easy to pick up Perl 6 and program it as you would have programmed Perl 5 or other languages you know well. But the documentation has lots of info of things you didn’t have before and that will make programming easier and more fun when you’ve learnt about them.
I guess you don’t need and excuse to delve into the docs, but if you do the Perl Weekly Challenge is an excellent excuse for spending time in the docs!
You have several options besides do. You could use parenthesises instead, like this:
say join "\n", grep *.day-of-week == 5, map { Date.new: |$_, 1 }, (1900..2019 X 1,3,5,7,8,10,12);In this case I just thought the code looked better without parenthesises, so I chose to use do instead. The docs has a couple of sentences about this option here.
BTW, I chose the form Date.new: blah — the colon variant — instead of Date.new(blah) also because I wanted to avoid parenthesises. This freedom to chose is the essence of Perl’s credo “There’s more than one way to do it” I guess.
Rant: This freedom of choice is the best thing with Perl 6, but it has a downside too. Code can be harder to understand if you’re not familiar with the variants another programmer has made. This is a part of the Perls’s reputation of being write-only languages.
It won’t happen, but personally I’d like to see someone analyse real-world Perl 6 code (public repositories on Github for instance) and figure what forms are used most. The analysis could have been used to clean house — figuring out what forms to keep and which should be removed. Perl 6 would become a smaller — and arguably easier — language while staying just as powerful as it is today.
This is slightly tangential to the Rakudo Perl 6 Performance Analysis Tooling grant from The Perl Foundation, but it does interact closely with it, so this is more or less a progress report.
The other day, Jonathan Worthington of Edument approached me with a little paid Job: Profiling very big codebases can be tedious, especially if you're interested in only one specific fraction of the program. That's why I got the assignment to make the profiler "configurable". On top of that, the Comma IDE will get native access to this functionality.
The actual request was just to allow specifying whether individual routines should be included in the profile via a configuration file. That would have been possible with just a simple text file that contains one filename/line number/routine name per line. However, I have been wanting something in MoarVM that allows much more involved configuration for many different parts of the VM, not just the profiler.
Obligatory cat photo.
That's why I quickly developed a small and simple "domain-specific language" for this and similar purposes.
The language had a few requirements:
There's also some things that aren't necessary:
While thinking about what exactly I should build – before I eventually settled on building a "programming language" for this task – I bounced back and forth between the simplest thing that could possibly work (for example, a text file with a list of file/line/name entries) and the most powerful thing that I can implement in a sensible timeframe (for example, allowing a full NQP script). A very important realization was that as long as I require the first line to identify what "version" of configuration program it is, I could completely throw away the current design and put something else instead, if the need ever arises. That allowed me to actually commit to a design that looked at least somewhat promising. And so I got started on what I call confprog.
Here's an example program. It doesn't do very much, but shows what it's about in general:
version = 1
entry profiler_static:
log = sf.name;
profile = sf.name eq "calculate-strawberries"
profile |= sf.cu.filename eq "CustomCode.pm6"
The entry decides which stage of profiling this program is applied to. In this case, the profiler_static means we're seeing a routine for the first time, before it is actually entered. That's why only the information every individual invocation of the frame in question shares is available via the variable sf, which stands for Static Frame. The Static Frame also allows access to the Compilation Unit (cu) that it was compiled into, which lets us find the filename.
The first line that actually does something assigns a value to the special variable log. This will output the name of the routine the program was invoked for.
The next line will turn on profiling only if the name of the routine is "calculate-strawberries". The line after that will also turn on profiling if the filename the routine is from is "CustomCode.pm6".
Apart from profiler_static, there are a couple more entry points available.
profiler_static, which runs the first time any routine is encountered, and stores the decision until the profile run is finished.profiler_dynamic will be evaluated every time a routine gets entered that would potentially be profiled otherwise (for example because a profiler_static program turned it on, or because there is no profiler_static program, which means that every routine is eligible for profiling).heapsnapshot will be run every time a GC run happens. It lets the user decide whether a GC run should result in a heap snapshot being taken or not, based on whether the run has been a minor or major collection, and arbitrary other factors, including time since start of the program, or time since the last heap snapshot was taken.spesh and jit I'm not entirely sure about yet. I want to allow turning the specializer or the jit off completely for certain routines, but I also want to offer control over individual optimizations in spesh (turning off inlining for one particular routine, preventing scalar replacement but only for Rat types, ...) and behaviour of the jit (use the template jit for everything except one specific piece of one particular routine, ...).The syntax is still subject to change, especially before the whole thing is actually in a released version of MoarVM.
There is a whole lot of other things I could imagine being of interest in the near or far future. One place I'm taking inspiration from is where "extended Berkeley Packet Filter" (eBPF for short) programs are being used in the linux kernel and related pieces of software:
Oversimplifying a bit, BPF was originally meant for tcpdump so that the kernel doesn't have to copy all data over to the userspace process so that the decision what is interesting or not can be made. Instead, the kernel receives a little piece of code in the special BPF language (or bytecode) and can calculate the decision before having to copy anything.
eBPF programs can now also be used as a complex ruleset for sandboxing processes (with "seccomp"), to decide how network packets are to be routed between sockets (that's probably for Software Defined Networks?), what operations a process may perform on a particular device, whether a trace point in the kernel or a user-space program should fire, and so on.
So what's the status of confprog? I've written a parser and compiler that feeds confprog "bytecode" (which is mostly the same as regular moarvm bytecode) to MoarVM. There's also a preliminary validator that ensures the program won't do anything weird, or crash, when run. It is much too lenient at the moment, though. Then there's an interpreter that actually runs the code. It can already take an initial value for the "decision output value" (great name, isn't it) and it will return whatever value the confprog has set when it runs. The heap snapshot profiler is currently the only part of MoarVM that will actually try to run a confprog, and it uses the value to decide whether to take a snapshot or not.
Next up on the list of things to work on:
Apart from improvements to the confprog programming language, the integration with MoarVM lacks almost everything, most importantly installing a confprog for the profiler to decide whether a frame should be profiled (which was the original purpose of this assignment).
After that, and after building a bit of GUI for Comma, the regular grant work can resume: Flame graphs are still not visible on the call graph explorer page, and heap snapshots can't be loaded into moarperf yet, either.
Thanks for sticking with me through this perhaps a little dry and technical post. I hope the next one will have a little more excitement! And if there's interest (which you can signal by sending me a message on irc, or posting on reddit, or reaching me via twitter @loltimo, on the Perl 6 discord server etc) I can also write a post on how exactly the compiler was made, and how you can build your own compiler with Perl 6 code. Until then, you can find Andrew Shitov's presentations about making tiny languages in Perl 6 on youtube.
I hope you have a wonderful day; see you in the next one!
- Timo
PS: I would like to give a special shout-out to Nadim Khemir for the wonderful Data::Dump::Tree module which made it much easier to see what my parser was doing. Here's some example output from another simple confprog program:
[6] @0
├ 0 = .Label .Node @1
│ ├ $.name = heapsnapshot.Str
│ ├ $.type = entrypoint.Str
│ ├ $.position is rw = Nil
│ └ @.children = [0] @2
├ 1 = .Op .Node @3
│ ├ $.op = =.Str
│ ├ $.type is rw = Nil
│ └ @.children = [2] @4
│ ├ 0 = .Var .Node @5
│ │ ├ $.name = log.Str
│ │ └ $.type = CPType String :stringy @6
│ └ 1 = String Value ("we're in") @7
├ 2 = .Op .Node @8
│ ├ $.op = =.Str
│ ├ $.type is rw = Nil
│ └ @.children = [2] @9
│ ├ 0 = .Var .Node @10
│ │ ├ $.name = log.Str
│ │ └ $.type = CPType String :stringy §6
│ └ 1 = .Op .Node @12
│ ├ $.op = getattr.Str
│ ├ $.type is rw = CPType String :stringy §6
│ └ @.children = [2] @14
│ ├ 0 = .Var .Node @15
│ │ ├ $.name = sf.Str
│ │ └ $.type = CPType MVMStaticFrame @16
│ └ 1 = name.Str
├ 3 = .Op .Node @17
│ ├ $.op = =.Str
│ ├ $.type is rw = Nil
│ └ @.children = [2] @18
│ ├ 0 = .Var .Node @19
│ │ ├ $.name = log.Str
│ │ └ $.type = CPType String :stringy §6
│ └ 1 = String Value ("i am the confprog and i say:") @21
├ 4 = .Op .Node @22
│ ├ $.op = =.Str
│ ├ $.type is rw = Nil
│ └ @.children = [2] @23
│ ├ 0 = .Var .Node @24
│ │ ├ $.name = log.Str
│ │ └ $.type = CPType String :stringy §6
│ └ 1 = String Value (" no heap snapshots for you my friend!") @26
└ 5 = .Op .Node @27
├ $.op = =.Str
├ $.type is rw = Nil
└ @.children = [2] @28
├ 0 = .Var .Node @29
│ ├ $.name = snapshot.Str
│ └ $.type = CPType Int :numeric :stringy @30
└ 1 = Int Value (0) @31
Notice how it shows the type of most things, like name.Str, as well as cross-references for things that appear multiple times, like the CPType String. Particularly useful is giving your own classes methods that specify exactly how they should be displayed by DDT. Love It!
Hello dear readers,
this time I don't have something finished to show off, but nevertheless I can tell you all about the work I've recently started.
The very first post on this blog already had a little bit about the heap snapshot profiler. It was about introducing a binary format to the heap snapshot profiler so that snapshots can be written out immediately after they were taken and by moarvm itself rather than by NQP code. This also made it possible to load snapshot data faster: the files contain an index that allows a big chunk of data to be split up exactly in the middle and then read and decoded by two threads in parallel.
The new format also resulted in much smaller output files, and of course reduced memory usage of turning on the profiler while running perl6 code. However, since it still captures one heap snapshot every time the GC runs, every ordinary program that runs for longer than a few minutes will accumulate quite an amount of data. Heap snapshot files (which are actually collections of multiple snapshots) can very easily outgrow a gigabyte. There would have to be another change.
Photo by Waldemar Brandt / Unsplash
The new format already contained the simplest thing that you could call compression. Instead of simply writing every record to the file as it comes, records that have smaller numbers would be stored with a shorter representation. This saved a lot of space already, but not nearly as much as off-the-shelf compression techniques would.
There had to be another way to get compression than just coming up with my own compression scheme! Well, obviously I could have just implemented something that already exists. However, at the time I was discouraged by the specific requirements of the heap snapshot analyzer - the tool that reads the files to let the user interactively explore the data within it:
Normally, compression formats are not built to support easily seeking to any given spot in the uncompressed data. There was of course the possibility to compress each snapshot individually, but that would mean a whole snapshot could either only be read in with a single thread, or the compression would have to go through the whole blob and when the first splittable piece was decompressed, a thread could go off and parse it. I'm not entirely sure why I didn't go with that, perhaps I just didn't think of it back then. After all, it's already been more than a year, and my brain compresses data by forgetting stuff.
Anyway, recently I decided I'd try a regular compression format for the new moarvm heap snapshot file format. There's already a Perl 6 module named Compress::Zlib, which I first wanted to use. Writing the data out from moarvm was trivial once I linked it to zlib. Just replace fopen with gzopen, fwrite with gzwrite, fclose with gzclose and you're almost done! The compression ratio wasn't too shabby either.
When I mentioned this in the #moarvm channel on IRC, I was asked why I use zlib instead of zstd. After all, zstd usually (or always?) outperforms zlib in both compression/decompression speed and output size. The only answer I had for that was that I hadn't used the zstd C library yet, and there was not yet a Perl 6 module for it.
Figuring out zstd didn't go as smoothly as zlib, not by a long shot. But first I'd like to explain how I solved the issue of reading the file with multiple threads.
In the current binary format, there are areas for different kinds of objects that occur once per snapshot. Those are collectables and references. On top of that there are objects that are shared across snapshots: Strings that are referenced from all the other kinds of objects (for example filenames, or descriptions for references like "hashtable entry"), static frames (made up of a name like "push", an ID, a filename, and a line number), and types (made up of a repr name like VMArray, P6opaque, or CStruct and a type name like BagHash or Regex).
That resulted in a file format that has one object after the other in the given area. The heap snapshot analyzer itself then goes through these areas and splits the individual values apart, then shoves them off into a queue for another thread to actually store. Storage inside the heap analyzer consists of one array for each part of these objects. For example, there is one array of integers for all the descriptions and one array of integers for all the target collectables. The main benefit of that is not having to go through all the objects when the garbage collector runs.
The new format on the other hand puts every value of each attribute in one long string before continuing with the next attribute.
Here's how the data for static frames was laid out in the file in the previous version:
"sframes", count, name 1, uuid 1, file 1, line 1, name 2, uuid 2, file 2, line 2, name 3, uuid 3, file 3, line 3, … index data
The count at the beginning tells us how many entries we should be expecting. For collectables, types, reprs, and static frames this gives us the exact number of bytes to look for, too, since every entry has the same size. References on the other hand have a simple "compression" applied to them, which doesn't allow us to just figure out the total size by knowing the count. To offset this, the total size lives at the end in a place that can easily be found by the parser. Strings are also variable in length, but there's only a few hundred of them usually. References take up the most space in total; having four to five times as many references as there are collectables is pretty normal.
Here's how the same static frame data is laid out in the upcoming format:
"names", length, zstd(name 1, name 2, name 3, …), index data, "uuids", length, zstd(uuid 1, uuid 2, uuid 3, …), index data, "files", length, zstd(file 1, file 2, file 3, …), index data, "lines", length, zstd(line 1, line 2, line 3, …), index data
As you can see, the values for each attribute now live in the same space. Each attribute blob is compressed individually, each has a little piece of index data at the end and a length field at the start. The length field is actually supposed to hold the total size of the compressed blob, but if the heap snapshot is being output to a destination that doesn't support seeking (moving back in the file and overwriting an earlier piece of data) we'll just leave it zeroed out and rely on zstd's format being able to tell when one blob ends.
There are some benefits to this approach that I'd like to point out:
The last point, in fact, will let me put some extra fun into the files. First of all, I currently start the files with a "plain text comment" that explains what kind of file it is and how it's structured internally. That way, if someone stumbles upon this file in fifty years, they can get started finding out the contents right away!
On a perhaps more serious note, I'll put in summaries of each snapshot that MoarVM itself can just already generate while it's writing out the snapshot itself. Not only things like "total number of collectables", but also "top 25 types by size, top 25 types by count". That will actually make the heapanalyzer not need to touch the actual data until the user is interested in more specific data, like a "top 100" or "give me a thousand objects of type 'Hash'".
On top of that, why not allow the user to "edit" heap snapshots, put some comments in before sharing it to others, or maybe "bookmarking" specific objects?
All of these things will be easier to do with the new format - that's the hope at least!
I didn't actually have the patience to exhaustively measure all the details, but here's a rough ratio for comparison: One dataset I've got results in a 1.1 gigabytes big file with the current binary format, a 147 megabytes big file when using gzip and a 99 megabytes big file using zstd (at the maximum "regular" compression level - I haven't checked yet if the cpu usage isn't prohibitive for this, though).
It seems like this is a viable way forward! Allowing capture to run 10x as long is a nice thing for sure.
The profiler view itself in Moarperf isn't done yet, of course. I may not put in more than the simplest of features if I start on the web frontend for the heap analyzer itself.
On the other hand, there's another task that's been waiting: Packaging moarperf for simpler usage. Recently we got support for a relocatable perl6 binary merged into master. That should make it possible to create an AppImage of moarperf. A docker container should also be relatively easy to build.
We'll see what my actual next steps will be - or will have been I suppose - when I post the next update!
Thanks for reading and take care
- Timo
 |
| Both ADD and SUB refer to the same LOAD node |
 |
| The DO node is inserted for the LET operator. It ensures that the value of the LOAD node is computed before the reference in either branch |
This evening, I enjoyed the New Year’s fireworks display over the beautiful Prague skyline. Well, the bit of it that was between me and the fireworks, anyway. Rather than having its fireworks display at midnight, Prague puts it at 6pm on New Year’s Day. That makes it easy for families to go to, which is rather thoughtful. It’s also, for those of us with plans to dig back into work the following day, a nice end to the festive break.
So, tomorrow I’ll be digging back into work, which of late has involved a lot of Perl 6. Having spent so many years working on Perl 6 compiler and runtime design and implementation, it’s been fun to spend a good amount of 2018 using Perl 6 for commercial projects. I’m hopeful that will continue into 2019. Of course, I’ll be continuing to work on plenty of Perl 6 things that are for the good of all Perl 6 users too. In this post, I’d like to share some of the things I’m hoping to work on or achieve during 2019.
The MoarVM specializer learned plenty of new tricks this year, delivering some nice speedups for many Perl 6 programs. Many of my performance improvement hopes for 2019 center around escape analysis and optimizations stemming from it.
The idea is to analyze object allocations, and find pieces of the program where we can fully understand all of the references that exist to the object. The points at which we can cease to do that is where an object escapes. In the best cases, an object never escapes; in other cases, there are a number of reads and writes performed to its attributes up until its escape.
Armed with this, we can perform scalar replacement, which involves placing the attributes of the object into local registers up until the escape point, if any. As well as reducing memory operations, this means we can often prove significantly more program properties, allowing further optimization (such as getting rid of dynamic type checks). In some cases, we might never need to allocate the object at all; this should be a big win for Perl 6, which by its design creates lots of short-lived objects.
There will be various code-generation and static optimizer improvements to be done in Rakudo in support of this work also, which should result in its own set of speedups.
Expect to hear plenty about this in my posts here in the year ahead.
The current Rakudo startup time is quite high. I’d really like to see it fall to around half of what it currently is during 2019. I’ve got some concrete ideas on how that can be achieved, including changing the way we store and deserialize NFAs used by the parser, and perhaps also dealing with the way we currently handle method caches to have less startup impact.
Both of these should also decrease the base memory use, which is also a good bit higher than I wish.
Some folks – myself included – are developing increasingly large applications in Perl 6. For the current major project I’m working on, runtime performance is not an issue by now, but I certainly feel myself waiting a bit on compiles. Part of it is parse performance, and I’d like to look at that; in doing so, I’d also be able to speed up handling of all Perl 6 grammars.
I suspect there are some good wins to be had elsewhere in the compilation pipeline too, and the startup time improvements described above should also help, especially when we pre-compile deep dependency trees. I’d also like to look into if we can do some speculative parallel compilation.
In Perl 6.d, we got non-blocking await and react support, which greatly improved the scalability of Perl 6 concurrent and parallel programs. Now many thousands of outstanding tasks can be juggled across just a handful of threads (the exact number chosen according to demand and CPU count).
For Perl 6.e, which is still a good way off, I’d like to having something to offer in terms of making Perl 6 concurrent and parallel programming safer. While we have a number of higher-level constructs that eliminate various ways to make mistakes, it’s still possible to get into trouble and have races when using them.
So, I plan to spend some time this year quietly exploring and prototyping in this space. Obviously, I want something that fits in with the Perl 6 language design, and that catches real and interesting bugs – probably by making things that are liable to occasionally explode in weird ways instead reliably do so in helpful ways, such that they show up reliably in tests.
In the 16 months since I revealed it, Cro has become a popular choice for implementing HTTP APIs and web applications in Perl 6. It has also attracted code contributions from a couple of dozen contributors. This year, I aim to see Cro through to its 1.0 release. That will include (to save you following the roadmap link):
I founded Comma IDE in order to bring Perl 6 a powerful Integrated Development Environment. We’ve come a long way since the Minimum Viable Product we shipped back in June to the first subscribers to the Comma Supporter Program. In recent months, I’ve used Comma almost daily on my various Perl 6 projects, and by this point honestly wouldn’t want to be without it. Like Cro, I built Comma because it’s a product I wanted to use, which I think is a good place to be in when building any product.
In a few months time, we expect to start offering Comma Community and Comma Complete. The former will be free of charge, and the latter a commercial offering under a subscribe-for-updates model (just like how the supporter program has worked so far). My own Comma wishlist is lengthy enough to keep us busy for a lot more than the next year, and that’s before considering things Comma users are asking for. Expect plenty of exciting new features, as well as ongoing tweaks to make each release feel that little bit nicer to use.
This year will see me giving my first keynote at a European Perl Conference. I’m looking forward to being in Riga again; it’s a lovely city to wander around, and I remember having some pretty nice food there too. The keynote will focus on the concurrent and parallel aspects of Perl 6; thankfully, I’ve still a good six months to figure out exactly what angle I wish to take on that, having spoken on the topic many times before!
I also plan to submit a talk or two for the German Perl Workshop, and will probably find the Swiss Perl Workshop hard to resist attending once more. And, more locally, I’d like to try and track down other Perl folks here in Prague, and see if I can help some kind of Praha.pm to happen again.
I need to keep my travel down to sensible levels, but might be able to fit in the odd other bit of speaking during the year, if it’s not too far away.
While I want to spend most of my time building stuff rather than talking about it, I’m up for the occasional bit of teaching. I’m considering pitching a 1-day Perl 6 concurrency workshop to the Riga organizers. Then we’ll see if there’s enough folks interested in taking it. It’ll cost something, but probably much less than any other way of getting a day of teaching from me. :-)
Well, a good night’s sleep first. :-) But tomorrow, another year of fun begins. I’m looking forward to it, and to working alongside many wonderful folks in the Perl community.
I want to revive Carl Mäsak's Coding Contest as a crowd-sourced contest.
The contest will be in four phases:
For the first phase, development of tasks, I am looking for volunteers who come up with coding tasks collaboratively. Sadly, these volunteers, including myself, will be excluded from participating in the second phase.
I am looking for tasks that ...
This is non-trivial, so I'd like to have others to discuss things with, and to come up with some more tasks.
If you want to help with task creation, please send an email to moritz.lenz@gmail.com, stating your intentions to help, and your freenode IRC handle (optional).
There are other ways to help too:
In these cases you can use the same email address to contact me,
or use IRC (moritz on freenode) or twitter.
Recently, a Perl 6 object creation benchmark result did the rounds on social media. This Perl 6 code:
class Point {
has $.x;
has $.y;
}
my $total = 0;
for ^1_000_000 {
my $p = Point.new(x => 2, y => 3);
$total = $total + $p.x + $p.y;
}
say $total;
Now (on HEAD builds of Rakudo and MoarVM) runs faster than this roughly equivalent Perl 5 code:
use v5.10;
package Point;
sub new {
my ($class, %args) = @_;
bless \%args, $class;
}
sub x {
my $self = shift;
$self->{x}
}
sub y {
my $self = shift;
$self->{y}
}
package main;
my $total = 0;
for (1..1_000_000) {
my $p = Point->new(x => 2, y => 3);
$total = $total + $p->x + $p->y;
}
say $total;
(Aside: yes, I know there’s no shortage of libraries in Perl 5 that make OO nicer than this, though those I tried also made it slower.)
This is a fairly encouraging result: object creation, method calls, and attribute access are the operational bread and butter of OO programming, so it’s a pleasing sign that Perl 6 on Rakudo/MoarVM is getting increasingly speedy at them. In fact, it’s probably a bit better at them than this benchmark’s raw numbers show, since:
Int object; there’s two uses of + here, so that means two new Int allocations per iteration of the loop, which then have to be garbage collectedWhile dealing with Int values got faster recently, it’s still really making two Int objects every time around that loop and having to GC them later. An upcoming new set of analyses and optimizations will let us get rid of that cost too. And yes, startup will get faster with time also. In summary, while Rakudo/MoarVM is now winning that benchmark against Perl 5, there’s still lots more to be had. Which is a good job, since the equivalent Python and Ruby versions of that benchmark still run faster than the Perl 6 one.
In this post, I’ll look at the changes that allowed this benchmark to end up faster. None of the new work was particularly ground-breaking; in fact, it was mostly a case of doing small things to make better use of optimizations we already had.
Theoretically, the default new method in turn calls bless, passing the named arguments along. The bless method then creates an object instance, followed by calling BUILDALL. The BUILDALL method goes through the set of steps needed for constructing the object. In the case of a simple object like ours, that involves checking if an x and y named argument were passed, and if so assigning those values into the Scalar containers of the object attributes.
For those keeping count, that’s at least 3 method calls (new, bless, and BUILDALL).
However, there’s a cheat. If bless wasn’t overridden (which would be an odd thing to do anyway), then the default new could just call BUILDALL directly anyway. Therefore, new looks like this:
multi method new(*%attrinit) {
nqp::if(
nqp::eqaddr(
(my $bless := nqp::findmethod(self,'bless')),
nqp::findmethod(Mu,'bless')
),
nqp::create(self).BUILDALL(Empty, %attrinit),
$bless(|%attrinit)
)
}
The BUILDALL method was originally a little “interpreter” that went through a per-object build plan stating what needs to be done. However, for quite some time now we’ve instead compiled a per-class BUILDPLAN method.
To figure out how to speed this up, I took a look at how the specializer was handling the code. The answer: not so well. There were certainly some positive moments in there. Of note:
x and y accessor methods were being inlined+ operators were being inlinedHowever, the new method was getting only a “certain” specialization, which is usually only done for very heavily polymorphic code. That wasn’t the case here; this program clearly constructs overwhelmingly one type of object. So what was going on?
In order to produce an “observed type” specialization – the more powerful kind – it needs to have data on the types of all of the passed arguments. And for the named arguments, that was missing. But why?
Logging of passed argument types is done on the callee side, since:
The argument logging was done as the interpreter executed each parameter processing instruction. However, when there’s a slurpy, then it would just swallow up all the remaining arguments without logging type information. Thus the information about the argument types was missing and we ended up with a less powerful form of specialization.
Teaching the slurpy handling code about argument logging felt a bit involved, but then I realized there was a far simpler solution: log all of the things in the argument buffer at the point an unspecialized frame is being invoked. We’re already logging the entry to the call frame at that point anyway. This meant that all of the parameter handling instructions got a bit simpler too, since they no longer had logging to do.
Having new being specialized in a more significant way was an immediate win. Of note, this part:
nqp::eqaddr(
(my $bless := nqp::findmethod(self,'bless')),
nqp::findmethod(Mu,'bless')
),
Was quite interesting. Since we were now specializing on the type of self, then the findmethod could be resolved into a constant. The resolution of a method on the constant Mu was also a constant. Therefore, the result of the eqaddr (same memory address) comparison of two constants should also have been turned into a constant…except that wasn’t happening! This time, it was simple: we just hadn’t implemented folding of that yet. So, that was an easy win, and once done meant the optimizer could see that the if would always go a certain way and thus optimize the whole thing into the chosen branch. Thus the new method was specialized into something like:
multi method new(*%attrinit) {
nqp::create(self).BUILDALL(Empty, %attrinit),
}
Further, the create could be optimized into a “fast create” op, and the relatively small BUILDALL method could be inlined into new. Not bad.
At this point, things were much better, but still not quite there. I took a look at the BUILDALL method compilation, and realized that it could emit faster code.
The %attrinit is a Perl 6 Hash object, which is for the most part a wrapper around the lower-level VM hash object, which is the actual hash storage. We were, curiously, already pulling this lower-level hash out of the Hash object and using the nqp::existskey primitive to check if there was a value, but were not doing that for actually accessing the value. Instead, an .AT-KEY('x') method call was being done. While that was being handled fairly well – inlined and so forth – it also does its own bit of existence checking. I realized we could just use the nqp::atkey primitive here instead.
Later on, I also realized that we could do away with nqp::existskey and just use nqp::atkey. Since a VM-level null is something that never exists in Perl 6, we can safely use it as a sentinel to mean that there is no value. That got us down to a single hash lookup.
By this point, we were just about winning the benchmark, but only by a few percent. Were there any more easy pickings?
My next surprise was that the new method didn’t get inlined. It was within the size limit. There was nothing preventing it. What was going on?
Looking closer, it was even worse than that. Normally, when something is too big to be inlined, but we can work out what specialization will be called on the target, we do “specialization linking”, indicating which specialization of the code to call. That wasn’t happening. But why?
Some debugging later, I sheepishly fixed an off-by-one in the code that looks through a multi-dispatch cache to see if a particular candidate matches the set of argument types we have during optimization of a call instruction. This probably increased the performance of quite a few calls involving passing named arguments to multi-methods – and meant new was also inlined, putting us a good bit ahead on this benchmark.
The next round of performance improvements – both to this code and plenty more besides – will come from escape analysis, scalar replacement, and related optimizations. I’ve already started on that work, though expect it will keep me busy for quite a while. I will, however, be able to deliver it in stages, and am optimistic I’ll have the first stage of it ready to talk about – and maybe even merge – in a week or so.
Moreover, it’s very much the case that Perl 5 and Perl 6 make different trade-offs. To pick one concrete example, Perl 6 makes it easy to run code across multiple threads, and even uses multiple threads internally (for example, performing optimization and JIT compilation on a background thread). Which is great…except the only winning move in a game involving both threads and fork() is not to play. Sometimes one just can’t have their cake and eat it, and if you’re wanting a language that more directly gives you your POSIX, that’s probably always going to be a strength of Perl 5 over Perl 6.(Emphasis mine). You see, I had never realized that MoarVM couldn't implement fork(), but it's true. In POSIX systems, a fork()'d child process inherits the full memory space, as-is, from its parent process. But it inherits only the forking thread. This means that any operations performed by any other thread, including operations that might need to be protected by a mutex (e.g. malloc()), will be interrupted and unfinished (in the child process). This can be a problem. Or, in the words of the linux manual page on the subject:* The child process is created with a single thread—the one that
called fork(). The entire virtual address space of the parent is
replicated in the child, including the states of mutexes,
condition variables, and other pthreads objects; the use of
pthread_atfork(3) may be helpful for dealing with problems that
this can cause.
* After a fork() in a multithreaded program, the child can safely
call only async-signal-safe functions (see signal-safety(7)) until
such time as it calls execve(2).
perl6 -e 'use nqp; my $i = nqp::fork(); say $i;'nqp::fork() will throw an exception). The reason why is simple: while it is possible to adapt the system threads so that I can stop them on demand, user threads may be doing arbitrary work, hold arbitrary locks and may be blocked (possibly indefinitely) on a system call. So jnthn's comment at the start of this post still applies - threads and fork() don't work together.MoarVM’s optimizer can perform speculative optimization. It does this by gathering statistics as the program is interpreted, and then analyzing them to find out what types and callees typically show up at given points in the program. If it spots there is at least a 99% chance of a particular type showing up at a particular program point, then it will optimize the code ahead of that point as if that type would always show up.
Of course, statistics aren’t proofs. What about the 1% case? To handle this, a guard instruction is inserted. This cheaply checks if the type is the expected one, and if it isn’t, falls back to the interpreter. This process is known as deoptimization.
I just stated that a guard cheaply checks if the type is the expected one, but just how cheap is it really? There’s both direct and indirect costs.
The direct cost is that of the check. Here’s a (slightly simplified) version of the JIT compiler code that produces the machine code for a type guard.
/* Load object that we should guard */
| mov TMP1, WORK[obj];
/* Get type table we expect and compare it with the object's one */
MVMint16 spesh_idx = guard->ins->operands[2].lit_i16;
| get_spesh_slot TMP2, spesh_idx;
| cmp TMP2, OBJECT:TMP1->st;
| jne >1;
/* We're good, no need to deopt */
| jmp >2;
|1:
/* Call deoptimization handler */
| mov ARG1, TC;
| mov ARG2, guard->deopt_offset;
| mov ARG3, guard->deopt_target;
| callp &MVM_spesh_deopt_one_direct;
/* Jump out to the interpreter */
| jmp ->exit;
|2:
Where get_spesh_slot is a macro like this:
|.macro get_spesh_slot, reg, idx;
| mov reg, TC->cur_frame;
| mov reg, FRAME:reg->effective_spesh_slots;
| mov reg, OBJECTPTR:reg[idx];
|.endmacro
So, in the case that the guard matches, it’s 7 machine instructions (note: it’s actually a couple more because of something I omitted for simplicity). Thus there’s the cost of the time to execute them, plus the space they take in memory and, especially, the instruction cache. Further, one is a conditional jump. We’d expect it to be false most of the time, and so the CPU’s branch predictor should get a good hit rate – but branch predictor usage isn’t entirely free of charge either. Effectively, it’s not that bad, but it’s nice to save the cost if we can.
The indirect costs are much harder to quantify. In order to deoptimize, we need to have enough state to recreate the world as the interpreter expects it to be. I wrote on this topic not so long ago, for those who want to dive into the detail, but the essence of the problem is that we may have to retain some instructions and/or forgo some optimizations so that we are able to successfully deoptimize if needed. Thus, the presence of a guard constrains what optimizations we can perform in the code around it.
A guard instruction in MoarVM originally looked like:
sp_guard r(obj) sslot uint32
Where r(obj) is an object register to read containing the object to guard, the sslot is a spesh slot (an entry in a per-block constant table) containing the type we expect to see, and the uint32 indicates the target address after we deoptimize. Guards are inserted after instructions for which we had gathered statistics and determined there was a stable type. Things guarded include return values after a call, reads of object attributes, and reads of lexical variables.
This design has carried us a long way, however it has a major shortcoming. The program is represented in SSA form. Thus, an invoke followed by a guard might look something like:
invoke r6(5), r4(2)
sp_guard r6(5), sslot(42), litui32(64)
Where r6(5) has the return value written into it (and thus is a new SSA version of r6). We hold facts about a value (if it has a known type, if it has a known value, etc.) per SSA version. So the facts about r6(5) would be that it has a known type – the one that is asserted by the guard.
The invoke itself, however, might be optimized by performing inlining of the callee. In some cases, we might then know the type of result that the inlinee produces – either because there was a guard inside of the inlined code, or because we can actually prove the return type! However, since the facts about r6(5) were those produced by the guard, there was no way to talk about what we know of r6(5) before the guard and after the guard.
More awkwardly, while in the early days of the specializer we only ever put guards immediately after the instructions that read values, more recent additions might insert them at a distance (for example, in speculative call optimizations and around spesh plugins). In this case, we could not safely set facts on the guarded register, because those might lead to wrong optimizations being done prior to the guard.
Now a guard instruction looks like this:
sp_guard w(obj) r(obj) sslot uint32
Or, concretely:
invoke r6(5), r4(2)
sp_guard r6(6), r6(5), sslot(42), litui32(64)
That is to say, it introduces a new SSA version. This means that we get a way to talk about the value both before and after the guard instruction. Thus, if we perform an inlining and we know exactly what type it will return, then that type information will flow into the input – in our example, r6(5) – of the guard instruction. We can then notice that the property the guard wants to assert is already upheld, and replace the guard with a simple set (which may itself be eliminated by later optimizations).
This also solves the problem with guards inserted away from the original write of the value: we get a new SSA version beyond the guard point. This in turn leads to more opportunities to avoid repeated guards beyond that point.
Quite a lot of return value guards on common operations simply go away thanks to these changes. For example, in $a + $b, where $a and $b are Int, we will be able to inline the + operator, and we can statically see from its code that it will produce an Int. Thus, the guard on the return type in the caller of the operator can be eliminated. This saves the instructions associated with the guard, and potentially allows for further optimizations to take place since we know we’ll never deoptimize at that point.
MoarVM does lots of speculative optimization. This enables us to optimize in cases where we can’t prove a property of the program, but statistics tell us that it mostly behaves in a certain way. We make this safe by adding guards, and falling back to the general version of the code in cases where they fail.
However, guards have a cost. By changing our representation of them, so that we model the data coming into the guard and after the guard as two different SSA versions, we are able to eliminate many guard instructions. This not only reduces duplicate guards, but also allows for elimination of guards when the broader view afforded by inlining lets us prove properties that we weren’t previously able to.
In fact, upcoming work on escape analysis and scalar replacement will allow us to start seeing into currently opaque structures, such as Scalar containers. When we are able to do that, then we’ll be able to prove further program properties, leading to the elimination of yet more guards. Thus, this work is not only immediately useful, but also will help us better exploit upcoming optimizations.
My work on Perl 6 performance continues, thanks to a renewal of my grant from The Perl Foundation. I’m especially focusing on making common basic operations faster, the idea being that if those go faster than programs composed out of them also should. This appears to be working out well: I’ve not been directly trying to make the Text::CSV benchmark run faster, but that’s resulted from my work.
I’ll be writing a few posts in on various of the changes I’ve done. This one will take a look at some related optimizations around boxing, unboxing, and common mathematical operations on Int.
Boxing is taking a natively typed value and wrapping it into an object. Unboxing is the opposite: taking an object that wraps a native value and getting the native value out of it.
In Perl 6, these are extremely common. Num and Str are boxes around num and str respectively. Thus, unless dealing with natives explicitly, working with floating point numbers and strings will involve lots of box and unbox operations.
There’s nothing particularly special about Num and Str. They are normal objects with the P6opaque representation, meaning they can be mixed into. The only thing that makes them slightly special is that they have attributes marked as being a box target. This indicates the attribute out as being the one to write to or read from in a box or unbox operation.
Thus, a box operations is something like:
While unbox is:
A box is actually two operations: an allocation and a store. We know how to fast-path allocations and JIT them relatively compactly, however that wasn’t being done for box. So, step one was to decompose this higher-level op into the allocation and the write into the allocated object. The first step could then be optimized in the usual way allocations are.
In the unspecialized path, we first find out where to write the native value to, and then write it. However, when we’re producing a specialized version, we almost always know the type we’re boxing into. Therefore, the object offset to write to can be calculated once, and a very simple instruction to do a write at an offset into the object produced. This JITs extremely well.
There are a couple of other tricks. Binds into a P6opaque generally have to check that the object wasn’t mixed in to, however since we just allocated it then we know that can’t be the case and can skip that check. Also, a string is a garbage-collectable object, and when assigning one GC-able object into another one, we need to perform a write barrier for the sake of generational GC. However, since the object was just allocated, we know very well that it is in the nursery, and so the write barrier will never trigger. Thus, we can omit it.
Unboxing is easier to specialize: just calculate the offset, and emit a simpler instruction to load the value from there.
I’ve also started some early work (quite a long way from merge) on escape analysis, which will allow us to eliminate many box object allocations entirely. It’s a great deal easier to implement this if allocations, reads, and writes to an object have a uniform representation. By lowering box and unbox operations into these lower level operations, this eases the path to implementing escape analysis for them.
Some readers might have wondered why I talked about Num and Str as examples of boxed types, but not Int. It is one too – but there’s a twist. Actually, there’s two twists.
The first is that Int isn’t actually a wrapper around an int, but rather an arbitrary precision integer. When we first implemented Int, we had it always use a big integer library. Of course, this is slow, so later on we made it so any number fitting into a 32-bit range would be stored directly, and only allocate a big integer structure if it’s outside of this range.
Thus, boxing to a big integer means range-checking the value to box. If it fits into the 32-bit range, then we can write it directly, and set the flag indicating that it’s a small Int. Machine code to perform these steps is now spat out directly by the JIT, and we only fall back to a function call in the case where we need a big integer. Once again, the allocation itself is emitted in a more specialized way too, and the offset to write to is determined once at specialization time.
Unboxing is similar. Provided we’re specializing on the type of the object to unbox, then we calculate the offset at specialization time. Then, the JIT produces code to check if the small Int flag is set, and if so just reads and sign extends the value into a 64-bit register. Otherwise, it falls back to the function call to handle the real big integer case.
For boxing, however, there was a second twist: we have a boxed integer cache, so for small integers we don’t have to repeatedly allocate objects boxing them. So boxing an integer is actually:
When I first did these changes, I omitted the use of the box cache. It turns out, however, to have quite an impact in some programs: one benchmark I was looking at suffered quite a bit from the missing box cache, since it now had to do a lot more garbage collection runs.
So, I reinstated use of the cache, but this time with the JIT doing the range checks in the produced machine code and reading directly out of the cache in the case of a hit. Thus, in the cache hit case, we now don’t even make a single function call for the box operation.
One might wonder why we picked 32-bit integers as the limit for the small case of a big integer, and not 64-bit integers. There’s two reasons. The most immediate is that we can then use the 32 bits that would be the lower 32 of a 64-bit pointer to the big integer structure as our “this is a small integer” flag. This works reliably as pointers are always aligned to at least a 4-byte boundary, so a real pointer to a big integer structure would never have the lowest bits set. (And yes, on big-endian platforms, we swap the order of the flag and the value to account for that!)
The second reason is that there’s no portable way in C to detect if a calculation overflowed. However, out of the basic math operations, if we have two inputs that fit into a 32-bit integer, and we do them at 64-bit width, we know that the result can never overflow the 64-bit integer. Thus we can then range check the result and decide whether to store it back into the result object as 32-bit, or to store it as a big integer.
Since Int is immutable, all operations result in a newly allocated object. This allocation – you’ll spot a pattern by now – is open to being specialized. Once again, finding the boxed value to operate on can also be specialized, by calculating its offset into the input objects and result object. So far, so familiar.
However, there’s a further opportunity for improvement if we are JIT-compiling the operations to machine code: the CPU has flags for if the last operation overflowed, and we can get at them. Thus, for two small Int inputs, we can simply:
I’ve done this for addition, subtraction, and multiplication. Those looking for a MoarVM specializer/JIT task might like to consider doing it for some of the other operations. :-)
Boxing, unboxing, and math on Int all came with various indirections for the sake of generality (coping with mixins, subclassing, and things like IntStr). However, when we are producing type-specialized code, we can get rid of most of the indirections, resulting in being able to perform them faster. Further, when we JIT-compile the optimized result into machine code, we can take some further opportunities, reducing function calls into C code as well as taking advantage of access to the overflow flags.