So! A while ago I worked on adding -ftime-trace support for Clang.
That landed and shipped in Clang 9.0 in
September of 2019, so ＼(＾O＾)／ Looks like it will also be coming to Sony development tools soon (see SN Systems
blog post).
All that is good, but it works on one compiled file at a time. If you know which source files are problematic in your whole build,
then great. But what if you don’t, and just want to see things like “which headers are most expensive to include across whole codebase”?
Basically it grabs *.json files produced by -ftime-trace from your whole build, smashes them together and does
some analysis across all of them. For headers being included, templates being instantiated, functions code-generated, etc.
And then prints the slowest things, like this:
Aaanyway. Maybe that will be useful for someone. Issue reports and pull requests welcome, and here’s the github repo
again: github.com/aras-p/ClangBuildAnalyzer
So! Turns out my “two years in a build team” post was almost
the end of my time there :) I’ve started a new thing & new work area, and am wrapping up some of my leftover
build team work as we speak. But! I wanted to write about one particular aspect of this build system work,
which took almost three years in total.
Three. Years.
That’s a really long time, and that’s how long it took for us to switch from “the build system we had previously” to
“the build system we have now”. Turns out, replacing a system in an ever-moving product is really, really, REALLY hard.
Sometimes I see that whenever people dream up some New Fancier Better system, they think that making this new system is the
where most of the work will go into. In my experience (in build system, but also in a handful of other occasions),
in addition to developing the new things, you also have to cover these:
While you will be busy doing new stuff, how will you keep up with changes to the old stuff? In a build system, people will
keep on adding new files, libraries to be built, will tweak compiler flags, change preprocessor defines, update SDK/compiler
versions and so on. Same with any other area – the old system is “live”, being used and being changed over time; maybe data
for the old system is still being produced by someone out there. How will you transition all that?
How will the new system be rolled out, in a way that everything keeps on working, all the time?
We have hundreds of developers on this codebase, a lot of automated processes running (builds, tests, packaging etc.),
and if everyone loses even a day of work due to some mess-up, that’s a massive cost. Really risky changes
have to be rolled out incrementally somehow, and only rolled out to “everyone” when all the large issues are found
and fixed.
So here’s a story in how we did it! I don’t know if the chosen approach is good or bad; it seems to have worked out fine.
2016 May, “Jam with C#” project at Hackweek.
At Unity Hackweek 2016, one of the projects was
“what if instead of Jam syntax to describe the build, we had C#?”. There’s a short video of it here:
We used a Jam-based build system called JamPlus
to build everything inside Unity since about 2010. Overall the whole setup looks like this:
There is an actual “build engine”, the jam.exe itself. This piece knows how to parse *.jam files that describe
the build, find which things need to be updated in order to build something, and execute these builds commands
in parallel where possible.
“JamPlus” is a bunch of rules written on top of that, in a combination of Jam and Lua languages. These are helper utilities,
like “finding a C++ compiler” and describing basic structure of a C/C++ program, etc. JamPlus can also generate IDE project
files for Visual Studio, Xcode and so on.
And then we have a bunch of our own *.jam files, that describe pieces and platforms of Unity itself. From simple things like
“this is a list of C++ files to compile, and here are the compiler flags”, to more involved things that are mostly about generating
code at build time.
Jam language syntax is very compact, but also “interesting” – for example, it needs whitespace between all tokens;
and yes that means space before every semicolon, or otherwise a lot of confusing messages you will get. Here’s
a random example I found:
So at this hackweek, what they did was embed C# (via Mono) directly into jam.exe, and make it be able
to run C# code to describe everything there is to build, instead of parsing a Jam language file. They also
wrote a converted from Jam language into C# language. If that sounds a bit crazy, that’s because it is, but
eh, who here has not embedded C# into a piece of software written in 1993?
And so all of *.jam files (our own build code, but also most of JamPlus rules) get turned into C# files, but
functionally nothing else changes. The auto-generated C# of course does not look much better; in fact at this
point it’s more verbose than original Jam code:
However with some cleanups and good IDEs (♥Rider) you can get to more legible
C# fairly quickly eventually:
static void ConvertFile(string cmd, NPath dest, NPath sourceInput, NPath generatedInput)
{
InvokeJamAction(cmd, dest, JamList(sourceInput, generatedInput));
// Tell Jam that the generated bison/flex file "includes" the original tempate grammar files,
// meaning it will include whatever regular C headers these include too, to detect needed rebuilds.
Includes(dest, sourceInput);
Depends(dest, JamList(sourceInput, generatedInput));
Needs(dest, BuildZips.Instance.FlexAndBison.ArtifactVersionFile);
}
2016, actual work starts
Hackweeks are a lot of fun, and one can get very impressive results by doing the most interesting parts of the project.
However, for actual production, “we’ll embed Mono into Jam, and write a language converter that kinda works” is not
nearly enough. It has to actually work, etc. etc.
Anyway, a couple months after hackweek experiment, our previous effort to move from Jam/JamPlus to
Gradle was canceled, and this new “Jam with C#” plan was greenlit.
It took until February of next year when this “Jam build engine, but build code is written in C#” was landed to everyone
developing Unity. How did we test it?
Had a separate branch that tracks mainline, where on the build farm it was doing two builds at once:
First, regular Jam build with the *.jam build code, and dumped whole Jam build graph structure,
Second one, with all the *.jam code automatically converted to C#, and dumped whole Jam build graph structure,
Checking that the two builds graphs were identical for each and every build target/platform that we have.
Had some developers at Unity opt-in to the new “Jam#” build code for a few months, to catch any possible issues.
Especially the ones that are not tested/covered by the build farm, e.g. “are Visual Studio project files still generated
just like before?”.
Before the final roll-out of “all .jam files are gone, .jam.cs files are in”, we also had a tool that would help anyone
who had a long-lived branch that they want to land to mainline. They might have changed build code in Jam language,
but after the C# roll-out their changes would have nowhere to merge! So there was a “give an old .jam file, we’ll get you
the converted C# file back” tooling for that case.
And so in 2017 February we rolled out removal of all the old *.jam files, and the (horrible looking) auto-converted
C# build code landed:
2017, starting to take advantage of this C# thing
Auto-converted-Jam-to-C# is arguably not much better. More verbose, actually kinda harder to read, but there
are some upsides. Statically typed programming language! Great IDEs and debuggers! You have more data types besides
“list of strings”! A lot of people inside Unity know C#, whereas “I know Jam” is not exactly common! And so on.
And so we started writing new C#-based APIs to express “how to build a program” rules, which we call “Bee”
(you’d have to ask @lucasmeijer about the name).
We were also rewriting IDE project files generation from the Lua-based one in JamPlus to, well, C#. My blog
posts from 2017 relating to Visual Studio project files (this one
or that one) might have been because I
was doing it at the time :)
Of course at this point all of our own build code still used the old JamPlus-but-now-C# APIs to express how things
need to be built. And we began taking all these pieces and converting them to use the new Bee build APIs:
This took much longer than initially expected, primarily because OMG you would not believe what a platform build
code might be doing. Why is this arcane compiler flag used here? No one remembers! But who knows what might break if you change it.
Why these files are being copied over there, ran through this strange tool, signed in triplicate, sent in, sent back,
queried, lost, found, subjected to public inquiry, lost again, and finally buried in soft peat for three months and
recycled as firelighters? Who knows! So there was a lot of that going on, inside each and every non-trivial build platform
and build target that we had.
We also did a lot of work in other build areas, be it improving UX (error messages, colors, …), developing a system for downloading
binary artifacts as part of the build, upgrading and packaging up compiler toolchains, experimenting with Ninja build
backend instead of Jam (more on that later), optimizing codebase build times in general, improving project files and so on.
2018, Jam switched to Tundra backend
After some experiments with Ninja backend, we settled upon Tundra
(our own fork) and replaced the Jam build backend with it.
The change was fairly hard to verify that “it works exactly the same as before”, because Tundra does not work exactly the same
as Jam. One might think that all build systems are “have some rules, and a build dependency graph, and they
execute the build actions”, but it’s a bit more complexicated than that. There’s a nice paper from Microsoft Research,
“Build Systems à la Carte”, that categorizes
build systems by type of dependencies they support (static vs dynamic), scheduling approach, early cutoff support, etc.
Specifically, Tundra’s scheduler is different (I think it’s “restarting” one as per that paper, whereas Jam’s is “topological”).
In practice, at least in our codebase this means that Tundra very often rebuilds less things compared to Jam, especially when
things involve files generated during build time. Since the order of build steps and even the amount of them is different
between Tundra and Jam, we could not just build simple validation suite like “build everything with both, compare that
they did exact same steps”.
So what we did was rely on the automated builds/tests that we already had for the product itself, and also on volunteer developers
inside Unity to try out Tundra locally. Since 2018 January people could opt-in to Tundra by adding a tiny local environment change,
and report any & all findings. We started with a handful of people, and over coming months it grew to several dozen. In late May it
got turned on by default (still with ability to opt-out) for everyone on Mac, and next month everyone got Tundra switched on. Some time
later remains of jam.exe got actually removed.
2019, current state
Today in our main code repository, jam.exe is long gone, and almost all of remains of JamPlus-converted C# code are gone.
Compared to the build state three years ago, a lot of nice build related tools were built (some I wrote about in the
previous blog post), and in general I think various aspects of build performance,
reliability, UX, workflow have been improved.
As a side effect, we also have this fairly nice build system (“Bee”) that we can use to build things outside of our
main code repository! So that’s also used to build various external libraries that we use, in various plugins/packages,
and I think even things like Project Tiny
use it for building actual final game code.
So all that’s nice! But oh geez, that also took a lot of time. Hence the blog post title.
I switched to the “build system” team by having two extremely short chats:
…and then a lot more chats with graphics people and some others of course; but the above “hey could I join? yes!”
was kinda the whole of my “job interview” to the team.
And then I sent out a “goodbye graphics” email:
I actually had no idea for how long I’m leaving, so I wrote “a couple months”. Well look, it’s been two years already!
A bunch of people have asked me either “ok so what is it that you actually do?”, or alternatively “surely the build work
must be done by now?”. To which I don’t have an excellent answer. My own work is a combination of:
Switching from “old build” (JamPlus) to “new build”
(Bee/Tundra) while not disrupting
the work of everyone around us.
Speeding up builds by cleaning up dependencies, includes, removing code.
Upgrading various platforms to more recent compiler versions; usually this is not hard but e.g. VS2010 -> VS2015
was pretty painful due to needed rebuilds of all 3rd party static libraries.
Improving UX of various aspects of build process: cleaner logs, better diagnostic messages, more intuitive build command
line arguments.
Support for people who have any build issues.
Fixing out various build issues that are accidentally introduced due to one reason or another. In a live codebase, it’s not
like you can fix all issues and be done with it :)
Typical work weeks might look like this – this is from my own “week logs” doc, took a screenshot of two recent ones:
And “what I did during last year” summary looks like this. I’ve highlighted buildsystem-related work in there; the rest is
“everything else”:
It doesn’t feel like “I got a lot done”, but doesn’t feel terrible either.
Anyway! Most of build stuff is fairly typical, but during last year our team has build some pretty neat tools
that I wanted to write about. So here they are!
Neat Build Tools
In our main code repository the build entry point script is called jam (it does not use Jam anymore, but backwards compat
with what people are used to type…). In some of our new code repositories (for DOTS/ECS and
some packages) the build entry point would
be called bee; both have the same tools, the examples below will be using jam entry point.
How exactly X is built?
jam how substring-of-something finds the most relevant build step (e.g. object file compile, executable link, file copy, whatever)
and tells exactly how it is built. This is mostly useful to figure out exact compiler command line flags, and dependencies.
Why X got rebuilt?
If one is wondering why something gets rebuilt (recompiled, re-copied, relinked, etc.), jam why substring-of-something tells that:
Every build produces a “log of what got done and why” file, and the why tool looks at that to do the report.
Where time was spent during the build?
I added Chrome Tracing profiler output support to both JamPlus and Tundra (see previous blog post),
and while that is all good and nice, sometimes what you want is just a “very quick summary”. Enter jam time-report. It shows
top slowest action “types”, and top 10 items within each type:
Of course if you want more detail, you can drag the profiler output file into chrome://tracing or speedscope.app
and browse it all:
What are the worst C/C++ header includes?
Since during the build Tundra scans source files for #include dependencies, we can use that to do some analysis / summary! jam include-report
shows various summaries of what might be worth untangling:
It is very similar to Header Hero that I used before for include optimization.
But I wanted something that would see actual includes instead of the approximation that Header Hero does, and
something that works on a Mac, and something that would be built-in in all our builds. So there!
This is all! I’ll get back to reviewing some pull requests now.
I saw a discussion today that lamented lack of “infinite projection” in Unity, so here’s
a quick post about that.
Unity today (at least in 2017/2018 versions) uses “reversed Z” projection, but does not use
“infinite projection”. This means that a Camera has an actual far clipping plane,
and beyond that distance nothing is rendered.
This is fine for almost all cases, except when you want to render custom “skybox-like”
geometry (a sky sphere or whatever); then you’d like to have it be “infinitely large”
and thus guaranteed to always be beyond any actual scene geometry.
You could wait for Unity to implement “infinite projection”, and/or write a custom
render pipeline to use your own infinite projection, or do a super small local change
just inside your “sky object” shader to make it effectively be infinite. Let’s check out
how to do the last bit!
“Infinite size” shader
To achieve an effectively “infinite size” (i.e. appears “behind any objects”) shader, all
we have to do is to move the vertices to be “on the far plane” in the vertex shader.
If o.vertex is a float4 with clip space position (e.g. computed by UnityObjectToClipPos),
then just do this:
#if defined(UNITY_REVERSED_Z)
// when using reversed-Z, make the Z be just a tiny
// bit above 0.0
o.vertex.z = 1.0e-9f;
#else
// when not using reversed-Z, make Z/W be just a tiny
// bit below 1.0
o.vertex.z = o.vertex.w - 1.0e-6f;
#endif
And here it is. Far plane of only 20, and a “sky sphere” object that is 100000 in size. No
clipping, renders behind scene geometry:
Here’s Unity 2018.2.15 project with the whole shader and a simple scene -
InfiniteSkyUnityProject.zip (350kb)
What’s all this Reversed Z and Infinite Projection anyway?
Summary is that reversing depth so that far plane is at zero, and near plane is at one,
and using a floating point format depth buffer, results in much better depth precision.
In Unity we have implemented support for this reversed Z a while ago (in Unity 5.5).
Today we don’t use infinite projection (yet?), so to achieve the infinite-like sky objects
you’d have to do a shader trick like above.
A while ago I found about a fairly useful Visual C++ flag that helps to investigate where the
compiler backend/optimizer spends time – /d2cgsummary, see
blog post.
Turns out, there is a flag that reports where the compiler frontend spends time – /d1reportTime.
I’ve been writing about lack of compiler time profiling/investigation tools, starting from that
same old post, up to the more recent ones
that focused on clang - one
and two.
All this recent discussion about C++ compilation times (among other issues…) led me
to finding this fresh project on github,
UECompileTimesVisualizer by
@phyronnaz. Ignore everything
about UE4 there (unless you work with it of course :)), the new thing for me was that it says:
Add the following arguments to the C/C++ Command Line option in your project settings: /Bt+ /d2cgsummary /d1reportTime
/Bt+ and /d2cgsummary are known, but /d1reportTime I haven’t heard about before. What is it?!
The only other place on the internet that talks about it – and it’s fairly impressive for anything to have just two mentions
on the internet – is a small comment in Microsoft
forums thread a couple months ago, and the only thing it says
is: “Can you also share the output from /d1reportTime?”
So what is /d1reportTime?
Passing /d1reportTime to the MSVC compiler (cl.exe) will make it print:
Which header files are included (hierarchically), with time taken for each,
Which classes are being parsed, with time taken for each,
Which functions are being parsed, with time taken for each.
Additionally, at end of each list, “top N” are listed too. List of classes/functions seems to contain
info about which templates (or nested classes? not sure yet) are being instantiated as well.
I have no idea which version of MSVC the flag has appeared in. VS2015 seems to not have it yet; and
VS2017 15.7 (compiler version 19.14.26428) already has it; it might have appeared in earlier VS2017 builds
but I haven’t checked.
Running it on anything non-trivial produces tons of output, e.g. I randomly ran it on a (fairly big)
Shader.cpp file we have in our codebase, and it produces 30 thousand lines of output. Shortened a lot:
Shader.cpp
Include Headers:
Count: 542
c:\trunk\runtime\shaders\shader.h: 0.311655s
c:\trunk\runtime\shaders\shadertags.h: 0.004842s
c:\trunk\runtime\shaders\shaderpropertysheet.h: 0.127829s
c:\trunk\runtime\math\color.h: 0.021522s
c:\trunk\runtime\serialize\serializeutility.h: 0.012031s
c:\trunk\runtime\serialize\serializationmetaflags.h: 0.005427s
c:\trunk\runtime\utilities\enumtraits.h: 0.004738s
c:\trunk\runtime\testing\testingforwarddecls.h: 0.000331s
c:\trunk\runtime\math\vector2.h: 0.003681s
<... a lot more skipped ...>
Top 24 (top-level only):
c:\trunk\runtime\graphics\scriptablerenderloop\scriptablerendercontext.h: 0.619189s
c:\trunk\runtime\graphics\scriptablerenderloop\scriptablebatchrenderer.h: 0.407027s
c:\trunk\runtime\shaders\shader.h: 0.311655s
c:\trunk\runtime\shaders\shadernameregistry.h: 0.263254s
<... a lot more skipped ...>
Total: 2.125453s
Class Definitions:
Count: 10381
ShaderTagID: 0.001146s
EnumTraits::internal::IsReflectableEnum<`template-type-parameter-1'>: 0.000057s
EnumTraits::internal::DefaultTraitsHelper<`template-type-parameter-1',1>: 0.000085s
EnumTraits::internal::DefaultTraitsHelper<`template-type-parameter-1',0>: 0.000064s
<... a lot more skipped ...>
Top 100 (top-level only):
std::_Vector_const_iterator<class std::_Vector_val<struct std::_Simple_types<struct ShaderLab::SerializedProperty> > >: 0.031536s
PersistentManager: 0.029135s
ModuleMetadata: 0.029010s
ShaderLab::SerializedSubProgram: 0.020047s
GlobalCallbacks: 0.019955s
<... a lot more skipped ...>
Total: 1.412664s
Function Definitions:
Count: 20544
ShaderTagID::ShaderTagID: 0.000368s
ShaderTagID::ShaderTagID: 0.000024s
operator ==: 0.000089s
operator !=: 0.000017s
operator <: 0.000021s
<... a lot more skipped ...>
Top 100 (top-level only):
std::operator ==: 0.031783s
ShaderLab::SerializedTagMap::Transfer: 0.014827s
math::matrixToQuat: 0.014715s
math::mul: 0.012467s
Texture::TextureIDMapInsert: 0.012006s
<... a lot more skipped ...>
Total: 2.658951s
At 30k lines it is not the most intuitive for manual use, but a lot of interesting information is there.
Particularly the “Top N” lists sound like good things to start with, if looking at the output manually.
Having reports like that is also useful to realize just how much work we’re asking the compiler to do.
The C++ snippet above is “as simple as it gets” when using “standard” C++ features. Just make a vector
of integers, and add a number to it. The compiler ends up including 74 headers, parsing over 800 classes and
almost 800 functions. Just to make a simple “push an integer into a vector” task.
Yes, I know about precompiled headers. And I know about upcoming C++ modules.
You can save on sending that email :)
Anyway, having that tooling is really good! Thanks Microsoft Visual C++ team!
Also, u/gratilup mentions in
this reddit thread
that MSVC team is working on ETW/WPA based tooling to visualize compilation times. Great!
