The Hitchhiker's Guide to

FASTER BUILDS

by Viktor Kirilov (2019 edition)

Me, myself and I

  • my name is Viktor Kirilov - from Bulgaria
  • 7+ years of professional C++ in the games / VFX / DB industries
  • worked only on open source for 3 years (2016-2019)
  • creator of doctest - the fastest C++ testing framework
  •  
  •  
  •  
  •  
  •             !!! not wasting time !!!

Passionate about

C++ is known for...

  • ▲ performance
  • ▲ expressiveness
  • ▲ being multi-paradigm
  • ▼ complexity
  • ▼ metaprogramming could be         much more powerful and simple
  • ▼ no standard build system             and package management
  • ▼ L0oo0oOoNG COMPILE TIMES

Major C++ issue - compile times

This presentation

Introduction: WHAT and WHY

precompiled headers

unity builds

hardware

build systems

caching

distributed builds

diagnostics

static vs dynamic linking

linkers

PIMPL

forward declarations

includes

templates

binary bloat

annotations

modules

parallel builds

toolchains

tooling

symbol visibility

compilers

Slides: https://slides.com/onqtam/faster_builds

memes

HOW

Reality

  • some projects take (tens of) hours to compile - WebKit: a big offender 
  • both minimal and full rebuilds suffer more and more in time
  • all projects start small - build times increase inevitably

Opera 32 bit on Windows

in 3 years 6 months:

  • 15 minutes to 64+
  • + incorporated PCH !!!

A unique problem to C++

Actually the folks at Rust sympathize with us :D

Why reduce build times

  • idle time is costly - lets do the math:
    • assuming a 80k $ annual salary
    • 1 hour daily for waiting (out of 8 hours: 12.5%)
    • 10k $ per developer annually
    • value to company > at least x3 the salary
    • 100 developers ==> 3 million $ annually
  • the unquantifiable effects of long build times
    • discourage refactoring and experimentation
    • lead to mental context switches (expensive)
      • thinking != other work
  • any time spent reducing build times is worthwhile !!!

The steps in building C++ code

  • preprocessor: source file => translation unit
    • includes
    • macros, conditional compilation (ifdef)
  • compiler: translation unit => object file
    • parsing, tokens, AST
    • optimizations
    • code generation
  • linker: object files & libraries => executable
  • Absolutely fantastic explanation:

Why C++ compilation takes so long

  • backwards compatibility with C: a killer feature ==> adoption!!!
    • inherits the archaic textual include approach
      • originally for sharing just declarations & structs between TUs
  • language is complex (ADL, overloads, templates, etc.)
    • multiple phases of translation - each depending on the previous
    • not context independent
    • new standards complicate things further
  • complex zero-cost abstractions end up in headers
  • even the preprocessor can become a bottleneck
  • sub-optimal tools and build system setups
  • bad project structure - component and header dependencies
    • touch a header - rebuild everything

C vs C++ and different standards

compiled as time increase
C 5 sec baseline
C++98 5.6 sec +12%
C++11 5.6 sec +12%
C++17 5.8 sec +14%
compiled as time increase
C++98 8.4 sec baseline
C++11 9.8 sec +17%
C++17 10.8 sec +29%

C codebase (impl)

https://github.com/vurtun/nuklear

C++ codebase (impl + tests) (ver 1.2.8)

https://github.com/onqtam/doctest

  • Environment: GCC 7.2 on Windows, only compilation: "-c", in Debug: "-O0"
  • optimizer doesn't care about language/standard: -O2 ==> same diff in seconds
  • front-end and optimizer speed varies a lot, but linkers keep getting faster
  • newer standards might be even heavier depending on the code
    • ​more constexpr, metaclasses, concepts - further pressure on compilers
    • constexpr - more complex compiler, but faster compile time algorithms

That meme again...

Why includes suck

  • textual - not symbolic
    • parsed results cannot be cached - macros...
  • implementation dependencies are leaked/dragged
    • privately used types by value need the definition
  • the same code gets compiled many times
    • the same headers end up used in many sources
    • M headers with N source files ==> M x N build cost
  • templates
    • re-instantiated for the same types
    • require more code to be in headers
  • inline functions in headers (this includes templates)
    • more work for the compiler/linker

Standard includes - after the preprocessor

Header GCC 7 - size lines of code MSVC 2017 - size lines of code
cstdlib 43 kb 1k 158 kb 11k
cstdio 60 kb 1k 251 kb 12k
iosfwd 80 kb 1.7k 482 kb 23k
chrono 190 kb 6.6k 841 kb 31k
variant 282 kb 10k 1.1 mb 43k
vector 320 kb 13k 950 kb 45k
algorithm 446 kb 16k 880 kb 41k
string 500 kb 17k 1.1 mb 52k
optional 660 kb 22k 967 kb 37k
tuple 700 kb 23k 857 kb 33k
map 700 kb 24k 980 kb 46k
iostream 750 kb 26k 1.1 mb 52k
memory 852 kb 29k 1.0 mb 40k
random 1.1 mb 37k 1.4 mb 67k
functional 1.2 mb 42k 1.4 mb 58k
regex 1.7 mb 64k 1.5 mb 71k
ALL OF THEM 2.6mb 95k 2.3mb 98k

Standard includes - time

measurements of compile time for different headers:
"C++ Headers are Expensive!"

future 1.12 s
experimental/filesystem 0.612 s
filesystem 0.594 s
regex 0.426 s
string 0.4 s
future 1.57 s
filesystem 0.92 s
atomic 0.702 s
regex 0.698 s
locale 0.641 s

MSVC 

Clang

Boost includes - after the preprocessor

Header GCC 7 - size lines of code MSVC 2017 - size lines of code
hana 857 kb 24k 1.5 mb 69k
optional 1.6 mb 50k 2.2 mb 90k
variant 2 mb 65k 2.5 mb 124k
function 2 mb 68k 2.6 mb 118k
format 2.3 mb 75k 3.2 mb 158k
signals2 3.7 mb 120k 4.7 mb 250k
thread 5.8 mb 188k 4.8 mb 304k
asio 5.9 mb 194k 7.6 mb 513k
wave 6.5 mb 213k 6.7 mb 454k
spirit 6.6 mb 207k 7.8 mb 563k
geometry 9.6 mb 295k 9.8 mb 448k
ALL OF THEM 18 mb 560k 16 mb 975k
  • environment: C++17, GCC 7, VS 2017, Boost version: 1.66
  • 100 .cpp files including geometry ==> 1 GB of source code!
  • not meant to discredit Boost - this is a C++ issue
  • LOC for MSVC is big because #ifdef-ed parts take up lines

The evolution of C++

  • hot debate - mainly sparked by people in the gamedev industry
    • "Modern" C++ Lamentations - reddit thread 1, thread 2
      • Example using ranges VS plain C++
        • 2.92 seconds vs 0.07 seconds of compile time
        • debug performance terrible
      • some spot-on comments
    • zero-cost abstractions in headers aren't "zero-cost" in debug
      • debug builds end up too slow in practice
    • build speed is important - even in Release
  • library features VS language features - no simple answer

Insert title of slide

What can we do about all of this...

Any great talk starts with... Disclaimers!

  • not really...
  • haven't personally tested all of these tools/techniques
  • sometimes defer to other more specialized resources
  • no exact measurements for most of tips/techniques
    • hard to reflect the real world or other projects
    • some techniques are in conflict with each other
    • reason yourself what to try and measure
    • also too much work :D
  • many tools use compile_commands.json
    • can be generated by CMake, ninja, Build EAR (BEAR)

Debug vs Release - the most obvious

  • compilers do a lot less in debug
    • differences can be huge (2-3 ... 20 times)
  • -O0, -O1, -O2, -O3, -Os - different results!
  • a runtime compromise: enable *some* optimizations
    • example - inlining: /Ob1 for MSVC
      • might improve link times
      • some game studios use it

Unreachable (dead) code

The C++ philosophy: "don't pay for what you don't use"

might also uncover bugs

& improve runtime!

  • unused sources/libraries
    • look for such after dead code removal
    • often a problem with makefiles and old cruft
    • MSVC warning LNK4221: This object file does not define any previously undefined public symbols
  • compile each source file just once
    • scan the build logs for such instances
    • commonly used sources => static libraries

Other low hanging fruit

Finding unnecessary includes

find_program(iwyu_path NAMES include-what-you-use iwyu)
set_property(TARGET hello PROPERTY CXX_INCLUDE_WHAT_YOU_USE ${iwyu_path})

Declarations vs definitions - functions

// interface.h

int foo(); // fwd decl - definition is in a source file somewhere

inline int bar() { // needs to be inline if the body is in a header
    return 42;
}

struct my_type {
    int method { return 666; } // implicitly inline - can get inlined!
};

template <typename T> // a template - "usually" has to be in a header
T identity(T in) { return in; } // implicitly inline

  • move function definitions out of headers

    • builds will be faster

    • use link time optimizations for inlining (or unity builds!)

  • some optimizations for templates exist - in later slides

Declarations vs definitions - types

// interface.h
#pragma once

struct foo; // fwd decl - don't #include "foo.h"!

#include "bar.h" // drag the definition of bar

struct my_type {
    foo f_1(foo obj); // fwd decl used by value in signature - OK
    
    foo* foo_ptr; // fwd decl used as pointer - OK
    bar member; // requires the definition of bar
};
// interface.cpp
#include "interface.h"

#include "foo.h" // drag the definition of foo

foo my_type::f_1(foo obj) { return obj; } // needs the definition of foo
  • used by value
    • including in class definitions (size for memory layout)
    • not in function declarations !!!

 

  • types are used by reference/pointer
    • pointer size is known by the compiler (4/8 bytes)
    • compiler doesn't need to know the size of types
  • types are used by value in function declarations
    • either as a return type or as an argument
    • this is less known !!!
    • function definition will still need the full type definition
    • call site also needs the full type definition

Declarations vs definitions - types

definition of types is necessary only when they are:

forward declarations are enough if:

On third-party libraries

  • making wrapper types/headers might be smart
    • example: platform specific stuff: <windows.h>
  • dedicated headers with forward declarations
    • instead of writing the same declarations manually
    • STL example: <iosfwd> for the I/O streams

PIMPL - pointer to implementation

  • AKA compilation firewall
  • holds a pointer to the implementation type
// widget.h
struct widget {
    widget();
    ~widget(); // cannot be defaulted here
    void foo();
private:
    struct impl; // just a fwd decl
    std::unique_ptr<impl> m_impl;
};
// widget.cpp
struct widget::impl {
   void foo() {}
};

widget::widget() : m_impl(std::make_unique<widget::impl>()) {}
widget::~widget() = default; // here it is possible
void widget::foo() { m_impl->foo(); }

PIMPL - pros & cons

  • breaks the interface/implementation dependency
    • less dragged headers ==> better compile times!
    • implementation data members no longer affect the size
  • helps with preserving ABI when changing the implementation
    • lots of APIs use it (example: Qt, Autodesk Maya)

 

  • requires allocation, also space overhead for pointer
    • can use allocators
  • extra indirection of calls - link time optimizations can help
  • more code

PROS:

CONS:

on const propagation, moves, copies and other details: http://en.cppreference.com/w/cpp/language/pimpl

Abstract interfaces & factories

// interface.h

struct IBase {
    virtual int do_stuff() = 0;
    
    virtual ~IBase() = default;
};

// factory functions
std::unique_ptr<IBase> make_der_1(int);
std::unique_ptr<IBase> make_der_2();
// interface.cpp
#include "interface.h"

// implementation 1
struct Derived_1 : public IBase {
    int data;
    
    Derived_1(int in) : data(in) {}
    int do_stuff() override { return data; }
};

// implementation 2
struct Derived_2 : public IBase {
    int do_stuff() override { return 42; }
};

// factory functions
std::unique_ptr<IBase> make_der_1(int in) {
    return std::make_unique<Derived_1>(in);
}
std::unique_ptr<IBase> make_der_2() {
    return std::make_unique<Derived_2>();
}

Implementations of derived classes are obtained through factory functions

Abstract interfaces & factories

  • breaks the interface/implementation dependency
  • helps with preserving ABI (careful with the virtual interface)

 

 

  • requires allocation, also space overhead for pointer
  • extra indirection of calls (virtual)
  • more code
  • users work with (smart) pointers instead of values
  • link time optimizations - harder than for PIMPL

PROS:

CONS:

Precompiled headers

  • put headers that are used in many of the sources
  • put headers that don't change often
  • do regular audits to reflect the latest project needs
  • often disregarded as a hack by many - HUGE mistake IMHO 
// precompiled.h
#pragma once

// system, runtime, STL
#include <cstdio>
#include <vector>
#include <map>

// third-party
#include <dynamix/dynamix.hpp>
#include <doctest.h>

// rarely changing project-specific
#include "utils/singleton.h"
#include "utils/transform.h"

Precompiled headers - usage

  • AKA: amalgamated, jumbo, lump, SCU
  • include all original source files in one (or a few) unity source file(s)

Unity builds

// unity file
#include "core.cpp"
#include "widget.cpp"
#include "gui.cpp"
  • headers get parsed only once if included from multiple sources
  • less compiler invocations
  • less instantiation of the same templates
  • HIGHLY underrated
  • NOT because of reduced disk I/O - filesystems cache aggressively
  • less linker work
  • used at Ubisoft (14+ years), Unreal, WebKit

The main benefit is compile times:

  • up to 10+ times faster (depends on modularity)
    • best gains: short sources + lots of includes
  • faster (and not slower) LTO (WPO/LTCG)
  • ODR (One Definition Rule) violations get caught
    • this one is huge - still no reliable tools
    • .
    • .
    •  
  • enforces code hygiene
    • include guards in headers
    • no statics and anon namespaces in headers
    • less copy/paste of types & inline functions

Unity builds - pros

// a.cpp
struct Foo {
    // implicit inline
    int method() { return 42; }
};
// b.cpp
struct Foo {
    // implicit inline
    int method() { return 666; }
};
  • might slow down some workflows
    • minimal rebuilds... WRONG! actually faster because of link time!
    • might interfere with parallel compilation
    • these ^^ can be solved with tuning/configuring
  • some C++ stops compiling as a unity (blog post)
    • clashes of symbols in anonymous namespaces
    • overload ambiguities
    • using namespaces in sources
    • preprocessor
  • miscompilation - rare (but possible)
    • successful compilation with a wrong result
    • preprocessor, overloads (also non-explicit 1 arg constructors)
    • good tests can help!

Unity builds - cons

 

  • can produce multiple unity source files
  • can exclude files (if problematic, or for incremental builds)

 

  • Meson (build system)
  • waf (build system)
  • RudeBuild (Visual Studio extension)
  • custom solution

Unity builds - how to maintain

these:

others:

A patch to clang that:

  • gives unique names to symbols in anonymous namespaces
  • undef's macros from top-level source files

 

==> minimal changes to Chromium required

==> parts of it get built 3 times faster !!!!

 

"JumboSupport: making unity builds easier in Clang"

http://lists.llvm.org/pipermail/cfe-dev/2018-April/057579.html

Unity builds - potential Clang support

Inlining annotations

explicitly disable inlining:

  • "don't pay for what you don't use"
  • explicitly delete the ones which you don't need from headers
    • especially for POD types
    • less work for the compiler/linker
  • some libraries even declare (default) ctors/dtors out of line (not in the header) for this exact purpose *cough* doctest

 

 

  • the gains will likely be miniscule ==> effort is questionable

Special member functions

type(); // in header
type::type() = default; // in cpp
  • linkers sometimes can fold identical functions - but more work
    • only if proven you aren't taking their addresses (required by the standard)
  • this way: less work for the compiler/linker
  • now it is syntactically easier to move function bodies out of the header
    • use link time optimizations for inlining (or unity builds!)

Templates - argument-independent code

template <typename T>
class my_vector {
    int m_size;
    // ...
public:
    int size() const { return m_size; }
    // ...
};

// my_vector<T>::size() is instantiated
// for types such as int, char, etc...
// even though it doesn't depend on T
class my_vector_base { // type independent
protected:             // code is moved out
    int m_size;
public:
    int size() const { return m_size; }
};

template <typename T> // type dependent
class my_vector : public my_vector_base {
    // ...
public:
    // ...
};

Templates - C++11 extern template

// interface.h
#include <vector>

template <typename T>
T identity(T in) { return in; }
// somewhere.cpp
#include "interface.h"

// do not instantiate these templates here
extern template int identity<int>(int);
extern template float identity<float>(float);

extern template class std::vector<int>;

// will not instantiate
auto g_int = identity(5);
auto g_float = identity(15.f);
std::vector<int> a;

// will instantiate for bool
auto g_unsigned = identity(true);
// somewhere_else.cpp
#include "interface.h"

// instantiation for float
template float identity<float>(float);
// instantiation for std::vector<int>
template class std::vector<int>;

// instantiation for identity<int>()
auto g_int_2 = identity(1);

the class/function has to be explicitly instantiated somewhere

for all types it is used with... or linker errors!

Templates - move functions out of headers

// interface.h

// declaration
template <typename T>
T identity(T in);

template <typename T>
struct answer {
    // declaration
    T calculate();
};
// interface.cpp
#include "interface.h"

// definition
template <typename T>
T identity(T in) { return in; }

// definition
template <typename T>
T answer<T>::calculate() {
    return T(42);
}

// explicit instantiations for int/float
template int identity<int>(int);
template float identity<float>(float);

// explicit instantiation for int
template struct answer<int>;

// user.cpp
#include "interface.h"

auto g_int = identity(666);
auto g_float = identity(0.541f);

auto g_answer = answer<int>().calculate();

The "Rule of Chiel":

  1. looking up a memoized type
  2. adding a parameter to an alias call
  3. adding a parameter to a type
  4. calling an alias
  5. instantiating a type
  6. instantiating a function template
  7. SFINAE

Templates - cost of operations

A fascinating talk:

code::dive 2017 – Odin Holmes – The fastest template metaprogramming in the West

  • use variadic templates sparingly
  • recursive templates are bad business
    • especially when not memoized
  • recursive template arguments are even worse
  • now that we have "constexpr if" we can use a lot less SFINAE
    • ​if constexpr might be preferrable even to tag dispatching

Templates - other notes

Templates - tools

Templates - other good talks

Templates - more resources

  • -ftime-report - breakdown of different compiler phases
  • -ftime-trace (Clang) - Chrome tracing flamegraph - by Aras Pranckevičius

 

Diagnostics - compilation

some functions trigger pathological cases (or bugs) in compilers/optimizers

Clang/GCC:

MSVC:

Diagnostics - binary bloat

PGO

Profile-guided optimization

  1. instrumented compilation
  2. training - gathering profile data
  3. recompilation with training data

PGO-built compilers (and linkers !!!)

10... 15... 20% faster when tailored for your codebase

GCC: "profiledbootstrap" and "bootstrap-lto" targets

  1. gets compiled with PGO instrumentation
  2. compiles itself with the instrumented binary
    • or your code
  3. compiles itself with the profile from the last build
    • also: LTO, -march=native

Clang can also be PGO-bootstrapped

CppCon 2014: Matt Hargett "Common-sense acceleration of your MLOC build"

Build systems

Make

MSBuild

FASTBuild

Ninja

Buck

Bazel

Scons

Autotools

Jam

Boost.Build

Qmake

CMake

tup

build2

shake

IncrediBuild

Meta build systems:

Gyp

Waf

Meson

Premake

Custom...

XCode

Please

Tundra

GN

Build systems

  • "g++ src1.cpp src2.cpp ..."  is not a build system​
  • dependency tracking - for minimal and parallel rebuilds
  • help managing scale
    • includes, flags, link libs
  • help with refactoring/restructuring
  • help with tool integration
  • not all are created equal
  • HUGE topic (trade-offs, approaches) - but lets focus just on speed
  • most old build systems have high overhead (MSBuild, make)
    • suboptimal dependency and change detection/scanning

Parallel builds

  • Ninja, FASTBuild, IncrediBuild: best in this regard
  • make: -jN (and through CMake: "cmake --build . -- -jN")
  • MSBuild (build system behind Visual Studio):
  • careful with unity builds and "long poles"
  • modern LTO/LTCG technology is parallelizable too

Meta build systems

  • one canonical description of the project
  • can generate build files for different build systems
    • hopefully including IDEs
    • that means +1 step to build your code
      • build files have to be updated occasionally
    • usually extensible - with different backends
  • CMake...
    • the (meta) build system we love to hate
    • sort-of the industry standard by now
    • use modern CMake (target-based) and new policies
    • generation phase can be a big bottleneck (thousands of targets)
    • https://github.com/cristianadam/cmake-checks-cache
    • might also build it with PGO

Diagnosing/monitoring parallelism

Make VC++ Compiles Fast Through Parallel Compilation - Bruce Dawson

Diagnosing/monitoring parallelism

IncrediBuild build monitor - article

Diagnosing/monitoring parallelism

Chrome's about:tracing (flame graph) - chrome://tracing/

Sort-of winners (in terms of speed)

  • FASTBuild
    • unity builds
    • precompiled headers
    • caching
    • distributed builds
    • best parallelization
    • generates project files
    • multiplatform
    • multi-compiler
    • tiny
    • might get a CMake backend
    • others...

haven't looked into these yet

  • buck << by Facebook
  • bazel << by Google
  • build2 << modules! :D
  • meson << hot contender
  • ...
  • Ninja - less features but crazy fast - intented to be generated by a meta-build system
    • GN (GYP replacement)
    • can generate ONLY ninja
  • IndrediBuild
  • tup

Possible build system improvements

when a dynamic library is linked

  • triggers relinking of other libraries that depend on it
  • EVEN WHEN THE ABI HASN'T CHANGED :(

 

"Pruning Dynamic Rebuilds With libabigail"

compare hashes of the ABI to determine if dependent

DSOs should be relinked

https://engineering.mongodb.com/post/pruning-dynamic-rebuilds-with-libabigail

Possible to implement in most build systems

Other possibilities: don't rebuild on whitespace only changes

Internal vs external linkage

use internal linkage for implementation details in source files

// file_1.cpp

// internal linkage
namespace { int ans = 42; }

// internal linkage
static int foo() { return ans; }

// external linkage
int bar() { return foo(); }
// file_2.cpp

// will get it from file_1.cpp
int bar();

// internal linkage
namespace { int ans = 666; }

// internal linkage
static int foo() { return bar(); }

... foo() + ans ==> "708"
  • eases the burden on the linker
  • easier to remove the function entirely if inlined
  • give unique names to not fight with unity builds
    • or nest inside of a named namespace!
  • don't expect huge gains from this
  • big discussion overall - but here we care about build times
  • much much faster - handles only the exported interface

Static vs dynamic linking

prefer dynamic (.dll, .so, .dylib) over static (.lib, .a)

  • Unix: all symbols exported by default
    • opposite to Windows - which got the default right!
  • UNIX: compile with -fvisibility=hidden
    • improves link time
    • annotate the required parts of the API for exporting
    • circumvents the GOT (Global Offset Table) for calls => profit
    • load times also improved (extreme templates case: 45 times)
    • reduces the size of your DSO by 5-20%
    • for details: https://gcc.gnu.org/wiki/Visibility
  • clang-cl on windows: /Zc:dllexportInlines- => 30% faster chrome build
  • alternative: -fvisibility-inlines-hidden
    • all inlined member functions are hidden
    • no source code modifications needed
    • careful with local statics in inline functions
      • can also explicitly annotate with default visibility

Dynamic linking - symbol visibility

Multiplatform symbol export annotations

#if defined _WIN32 || defined __CYGWIN__
#define SYMBOL_EXPORT __declspec(dllexport)
#define SYMBOL_IMPORT __declspec(dllimport)
#define SYMBOL_HIDDEN
#else
#define SYMBOL_EXPORT __attribute__((visibility("default")))
#define SYMBOL_IMPORT
#define SYMBOL_HIDDEN __attribute__((visibility("hidden")))
#endif

#ifdef DLL_EXPORTS // on this depends if we are exporting or importing
#define MY_API SYMBOL_EXPORT
#else
#define MY_API SYMBOL_IMPORT
#endif

MY_API void foo();

class MY_API MyClass {
   int c;
   SYMBOL_HIDDEN void privateMethod();  // only for use within this DSO
public:
   MyClass(int _c) : c(_c) { }
   static void foo(int a);
};

Linkers

  • gold (unix)
    • comes with binutils starting from version 2.19
    • -fuse-ld=gold
    • not threaded by default
      • configure with "--enable-threads"
      • use: "--threads", "--thread-count COUNT", "--preread-archive-symbols"
  • lld even faster than gold - https://lld.llvm.org/ - multithreaded by default
  • Unix: Split DWARF - article - building clang/llvm codebase
    • 10%+ faster full builds and 40%+ faster incremental builds
  • CMake - LINK_WHAT_YOU_USE
  • can also build the linker yourself and add -flto, -march=native (or even do a PGO build of the linker with your codebase)

Linkers - LTO and other flags

Compiler caches

"compiler wrapper" -

maps a hash of:

  • preprocessed source (TU)
  • compilation flags
    & compiler version

==> to an object file

  • First run (cold cache) is a bit slower than without the wrapper
  • Consecutive >> FULL << rebuilds
    are much faster (warm cache)

Compiler caches

Compiler caches

  • caches work best for full rebuilds - not minimal

Other links:

ccache and CMake:

 

 

 

find_program(CCACHE_PROGRAM ccache)
if(CCACHE_PROGRAM)
    set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE "${CCACHE_PROGRAM}")
endif()

project(SomeProject)

Distributed builds

  • take advantage of unused processing power of computers
  • prepend in front of compiler
    • ​just like caches: distcc gcc ...
  • caching + distributed builds = ❤
  • watch out: network bottlenecks
    • include directories - sort em!
    • bandwidth
export CCACHE_PREFIX=DISTCC

Hardware

Hardware

  • more cores
    • make sure you have enough RAM && parallel builds setup
  • RAM disks (file system drive in your RAM instead of ssd/hdd)
    • better than SSDs
    • don't expect huge gains though...
    • mount $TEMP as a RAM disk (%TEMP% for windows)
    • build output to RAM disk (most important - because of the link step)
    • also put the compiler there!
  • CppCon 2014: Matt Hargett "Common-sense acceleration of your MLOC build"
    • touches on network I/O issues and many other topics

What if the fundament is bad

Physical design

Dependency analysis

Dependency analysis

Boost.Variant - Boost dependencies and bcp - by Stephen Kelly

MODULES

Modules - history

Modules - motivation

  • compile times...!!! hopefully? no?
  • preprocessor includes led to:
    • leaked symbols & implementation details
    • name clashes
    • making tooling hard
    • workarounds (include guards)
    • include order matters
  • more DRY (don't repeat yourself)
    • forward declarations
    • code duplication in headers/sources

Modules - VS 2017 example

import std.core; // containers, algorithms, etc.
import M_1; // our module!

int main() { 
    std::vector<std::string> v = getStrings();
    std::copy(v.begin(), v.end(),
        std::ostream_iterator<std::string>(std::cout, "\n"));
}
import std.core; // containers, string

export module M_1; // named M_1

export std::vector<std::string> getStrings() {
    return {"Plato", "Descartes", "Bacon"};
}
cl.exe /experimental:module /EHsc /MD /std:c++latest /module:export            /c mod_1.cpp
cl.exe /experimental:module /EHsc /MD /std:c++latest /module:reference M_1.ifc /c main.cpp
cl.exe main.obj mod_1.obj

Modules - how they work

  • BMI (Binary Module Interface) files
    • consumed by importers
    • produced by compiling a MIU (Module Interface Unit)
    • (maybe) contains serialized AST (abstract syntax tree) with the exported parts of the parsed C++
  • unlike precompiled headers:
    • composability of multiple modules
    • explicit code annotations to define visible interfaces

Modules - obsolete techniques

  • PIMPL!!! (mostly)
    • unless you need to provide ABI stability of your APIs
  • forward declarations (mostly)
  • precompiled headers
  • unity builds (mostly)

Modules - the current state

  • big companies are invested in them (think BIG)
  • there are 3 implementations already (MSVC, Clang, GCC)
  • coming in C++20
  • when standardized and implemented in compilers:
    • do not expect astronomical gains in speed (for now)
    • build system support is hard
      • notably build2 supports modules!
    • slow adoption in existing software
      • updating toolchains
      • source code changes
        • third party dependencies
        • if it ain't "broke" don't fix it

More on modules

Many details: exported macros?!?! - resources:

Even more on modules

Next steps

  • consider runtime C++ compilation & hotswap
  • make source control checkouts faster
  • tests
    • doctest? please!!! it's great, trust me :)
    • ctest has -j N
  • Continuous integration
  • IDE integration of tools for better productivity
  • change language :| Nim is the anwser *cough*

Enforcing

Vague rules + no enforcement + path of least resistance

+ human nature → chaos

  • code reviews
  • automation
    • tooling
      • code hygiene metrics/diagnostics
      • clang-based
    • pre-commit hooks
    • CI checks
      • notifications

 

I'm free for consultant work!

You can hire me to fix your C++ build times.

Expect something along the lines of this:

https://onqtam.github.io/programming/2019-12-20-pch-unity-cmake-3-16/

Q&A

                               Other useful links:

The Hitchhiker's Guide to Faster Builds (2019 edition)

By Viktor Kirilov

Made with Slides.com

The Hitchhiker's Guide to Faster Builds (2019 edition)

Covers absolutely every possible tool/technique to improve compile and link times for C++ code (2019 edition)

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