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https://github.com/arctic-marmoset/codegen
A repo for experimenting with AOT and JIT code generation, including IR transformations for optimisation.
https://github.com/arctic-marmoset/codegen
code-generation cpp cpp23 dataflow-analysis intermediate-representation optimizing-compilers
Last synced: about 1 month ago
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A repo for experimenting with AOT and JIT code generation, including IR transformations for optimisation.
- Host: GitHub
- URL: https://github.com/arctic-marmoset/codegen
- Owner: arctic-marmoset
- License: mit
- Created: 2022-08-14T07:29:00.000Z (over 2 years ago)
- Default Branch: trunk
- Last Pushed: 2022-08-16T07:20:24.000Z (over 2 years ago)
- Last Synced: 2024-04-22T06:21:12.930Z (10 months ago)
- Topics: code-generation, cpp, cpp23, dataflow-analysis, intermediate-representation, optimizing-compilers
- Language: C++
- Homepage:
- Size: 18.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Codegen
An experiment on AOT and JIT code generation, including IR transformations for
optimisation.
The goal of this project is not only to learn how to implement optimisations by
putting into practice data-flow analysis theory, but also to design and
structure the code as clearly as possible such that it is easy for others to
read, and thus learn from. This project currently focusses on the back-end, so
front-end operations such as lexing and parsing are omitted.
The project will take an incremental approach of first compiling a tiny subset
of the source language all the way down to the target language or machine code,
which at the moment is x64, then gradually increasing the size of the
subset—a method inspired by
Abdulaziz Ghuloum's [_An Incremental Approach to Compiler Construction_](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.88.170&rep=rep1&type=pdf),
and Nora Sandler's [_Writing a C Compiler_](https://norasandler.com/2017/11/29/Write-a-Compiler.html).
# Current Progress
The program is currently capable of transforming, _without optimisations_, an
AST representing a C-like compound statement containing multiple return
statements with optional return values (either add expressions of depth 1, or
unsigned 32-bit integers), to x64 assembly, then executing it dynamically. An
example is as follows:
Input AST:
```
compound_statement
|-return_statement
| `-additive_expression '+'
| |-integer_literal 3735928549U # 0xDEADBEE5
| `-integer_literal 10U # 0xA
|-return_statement
`-return_statement
`-integer_literal 0U # 0x0
```
IR:
```
0000 basic_block {
$0 = add 3735928549U, 10U
return $0
return ()
return 0U
}
```
Generated bytes:
```
00 01 02 03 04 05 06 07 08 09 0A 0B 0C 0D 0E 0F
0x00000000: B8 E5 BE AD DE 05 0A 00 00 00 C3 C3 B8 00 00 00 ................
0x00000010: 00 C3 ..
```
Disassembly:
```asm
0x00000000: mov eax, 0xDEADBEE5
0x00000005: add eax, 0xA
0x0000000A: ret
0x0000000B: ret
0x0000000C: mov eax, 0
0x00000011: ret
```
Execution:
```
function() = 0xDEADBEEF
```
# Building
| Requirement | Minimum Version |
|-----------------------------------------|-----------------|
| [CMake](https://cmake.org/) | 3.23 |
| [Ninja](https://ninja-build.org/) | - |
| [vcpkg](https://vcpkg.io/en/index.html) | - |
| C++ Compiler | C++23 support |
CMake and Ninja are available through your system package manager.
Although not advised, libraries that are used can be found in
[vcpkg.json](/vcpkg.json), and can alternatively be installed manually using
your system package manager as well.
Build using the following commands:
```
cmake --preset .
cmake --build ./build/
```
Where `` is one of the non-hidden presets in
[CMakePresets.json](/CMakePresets.json). The `/build` directory will contain the
executable.