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LLVM Cookbook

Chapter 1. LLVM Design and Use

In this chapter, we will cover the following topics:

Understanding modular design
Cross-compiling Clang/LLVM
Converting a C source code to LLVM assembly
Converting IR to LLVM bitcode
Converting LLVM bitcode to target machine assembly
Converting LLVM bitcode back to LLVM assembly
Transforming LLVM IR
Linking LLVM bitcode
Executing LLVM bitcode
Using C frontend Clang
Using the GO frontend
Using DragonEgg

Understanding modular design

LLVM is designed as a set of libraries unlike other compilers such as GNU Compiler Collection (GCC). In this recipe, LLVM optimizer will be used to understand this design. As LLVM optimizer's design is library-based, it allows you to order the passes to be run in a specified order. Also, this design allows you to choose which optimization passes you can run—that is, there might be a few optimizations that might not be useful to the type of system you are designing, and only a few optimizations will be specific to the system. When looking at traditional compiler optimizers, they are built as a tightly interconnected mass of code, that is difficult to break down into small parts that you can understand and use easily. In LLVM, you need not know about how the whole system works to know about a specific optimizer. You can just pick one optimizer and use it without having to worry about other components attached to it.

Before we go ahead and look into this recipe, we must also know a little about LLVM assembly language. The LLVM code is represented in three forms: in memory compiler Intermediate Representation (IR), on disk bitcode representation, and as human readable assembly. LLVM is a Static Single Assignment (SSA)-based representation that provides type safety, low level operations, flexibility, and the capability to represent all the high-level languages cleanly. This representation is used throughout all the phases of LLVM compilation strategy. The LLVM representation aims to be a universal IR by being at a low enough level that high-level ideas may be cleanly mapped to it. Also, LLVM assembly language is well formed. If you have any doubts about understanding the LLVM assembly mentioned in this recipe, refer to the link provided in the See also section at the end of this recipe.

Getting ready

We must have installed the LLVM toolchain on our host machine. Specifically, we need the opt tool.

How to do it...

We will run two different optimizations on the same code, one-by-one, and see how it modifies the code according to the optimization we choose.

First of all, let us write a code we can input for these optimizations. Here we will write it into a file named testfile.ll:

$ cat testfile.ll
define i32 @test1(i32 %A) {
  %B = add i32 %A, 0
  ret i32 %B
}

define internal i32 @test(i32 %X, i32 %dead) {
  ret i32 %X
}

define i32 @caller() {
  %A = call i32 @test(i32 123, i32 456)
  ret i32 %A
}

Now, run the opt tool for one of the optimizations—that is, for combining the instruction:
```
$ opt –S –instcombine testfile.ll –o output1.ll
```

View the output to see how instcombine has worked:

$ cat output1.ll
; ModuleID = 'testfile.ll'

define i32 @test1(i32 %A) {
  ret i32 %A
}

define internal i32 @test(i32 %X, i32 %dead) {
  ret i32 %X
}

define i32 @caller() {
  %A = call i32 @test(i32 123, i32 456)
  ret i32 %A
}

Run the opt command for dead argument elimination optimization:
```
$ opt –S –deadargelim testfile.ll –o output2.ll
```

View the output, to see how deadargelim has worked:

$ cat output2.ll
; ModuleID = testfile.ll'

define i32 @test1(i32 %A) {
  %B = add i32 %A, 0
  ret i32 %B
}

define internal i32 @test(i32 %X) {
  ret i32 %X
}

define i32 @caller() {
  %A = call i32 @test(i32 123)
  ret i32 %A
}

How it works...

In the preceding example, we can see that, for the first command, the instcombine pass is run, which combines the instructions and hence optimizes %B = add i32 %A, 0; ret i32 %B to ret i32 %A without affecting the code.

In the second case, when the deadargelim pass is run, we can see that there is no modification in the first function, but the part of code that was not modified last time gets modified with the function arguments that are not used getting eliminated.

LLVM optimizer is the tool that provided the user with all the different passes in LLVM. These passes are all written in a similar style. For each of these passes, there is a compiled object file. Object files of different passes are archived into a library. The passes within the library are not strongly connected, and it is the LLVM PassManager that has the information about dependencies among the passes, which it resolves when a pass is executed. The following image shows how each pass can be linked to a specific object file within a specific library. In the following figure, the PassA references LLVMPasses.a for PassA.o, whereas the custom pass refers to a different library MyPasses.a for the MyPass.o object file.

Tip

Downloading the example code

You can download the example code files for all Packt books you have purchased from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

There's more...

Similar to the optimizer, the LLVM code generator also makes use of its modular design, splitting the code generation problem into individual passes: instruction selection, register allocation, scheduling, code layout optimization, and assembly emission. Also, there are many built-in passes that are run by default. It is up to the user to choose which passes to run.

Cross-compiling Clang/LLVM

By cross-compiling we mean building a binary on one platform (for example, x86) that will be run on another platform (for example, ARM). The machine on which we build the binary is called the host, and the machine on which the generated binary will run is called the target. The compiler that builds code for the same platform on which it is running (the host and target platforms are the same) is called a native assembler, whereas the compiler that builds code for a target platform different from the host platform is called a cross-compiler.

In this recipe, cross-compilation of LLVM for a platform different than the host platform will be shown, so that you can use the built binaries for the required target platform. Here, cross-compiling will be shown using an example where cross-compilation from host platform x86_64 for target platform ARM will be done. The binaries thus generated can be used on a platform with ARM architecture.

Getting ready

The following packages need to be installed on your system (host platform):

cmake
ninja-build (from backports in Ubuntu)
gcc-4.x-arm-linux-gnueabihf
gcc-4.x-multilib-arm-linux-gnueabihf
binutils-arm-linux-gnueabihf
libgcc1-armhf-cross
libsfgcc1-armhf-cross
libstdc++6-armhf-cross
libstdc++6-4.x-dev-armhf-cross
install llvm on your host platform

How to do it...

To compile for the ARM target from the host architecture, that is X86_64 here, you need to perform the following steps:

Add the following cmake flags to the normal cmake build for LLVM:

-DCMAKE_CROSSCOMPILING=True
-DCMAKE_INSTALL_PREFIX= path-where-you-want-the-toolchain(optional)
-DLLVM_TABLEGEN=<path-to-host-installed-llvm-toolchain-bin>/llvm-tblgen
-DCLANG_TABLEGEN=< path-to-host-installed-llvm-toolchain-bin >/clang-tblgen
-DLLVM_DEFAULT_TARGET_TRIPLE=arm-linux-gnueabihf
-DLLVM_TARGET_ARCH=ARM
-DLLVM_TARGETS_TO_BUILD=ARM
-DCMAKE_CXX_FLAGS='-target armv7a-linux-gnueabihf -mcpu=cortex-a9 -I/usr/arm-linux-gnueabihf/include/c++/4.x.x/arm-linux-gnueabihf/ -I/usr/arm-linux-gnueabihf/include/ -mfloat-abi=hard -ccc-gcc-name arm-linux-gnueabihf-gcc'

If using your platform compiler, run:
```
$ cmake -G Ninja <llvm-source-dir> <options above>
```
If using Clang as the cross-compiler, run:
```
$ CC='clang' CXX='clang++' cmake -G Ninja <source-dir> <options above>
```
If you have clang/Clang++ on the path, it should work fine.
To build LLVM, simply type:
```
$ ninja
```
After the LLVM/Clang has built successfully, install it with the following command:
```
$ ninja install
```

This will create a sysroot on the install-dir location if you have specified the DCMAKE_INSTALL_PREFIX options

How it works...

The cmake package builds the toolchain for the required platform by making the use of option flags passed to cmake, and the tblgen tools are used to translate the target description files into C++ code. Thus, by using it, the information about targets is obtained, for example—what instructions are available on the target, the number of registers, and so on.

Note

If Clang is used as the cross-compiler, there is a problem in the LLVM ARM backend that produces absolute relocations on position-independent code (PIC), so as a workaround, disable PIC at the moment.

The ARM libraries will not be available on the host system. So, either download a copy of them or build them on your system.

What you will learn

Introduction to LLVM modular design and LLVM tools Write a frontend for a language Add JIT support and use frontends for different languages Learn about the LLVM Pass infrastructure and the LLVM Pass Manager Create analyses and transform optimization passes Build a LLVM TOY backend from scratch Optimize the code at SelectionDAG level and allocate registers to variables

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Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

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DRM FREE - Read whenever, wherever and however you want

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LLVM Cookbook: Over 80 engaging recipes that will help you build a compiler frontend, optimizer, and code generator using LLVM

What do you get with eBook?

Product Details

LLVM Cookbook

Chapter 1. LLVM Design and Use

Introduction

Understanding modular design

Getting ready

How to do it...

How it works...

Tip

There's more...

See also

Cross-compiling Clang/LLVM

Getting ready

How to do it...

How it works...

Note

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LLVM Cookbook: Over 80 engaging recipes that will help you build a compiler frontend, optimizer, and code generator using LLVM

What do you get with eBook?

Product Details

Key benefits

What you will learn

What do you get with eBook?

Product Details

Packt Subscriptions

Table of Contents

Recommendations for you

Customer reviews

Filter reviews by

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Authors (2)

FAQs