Introduction
Most Linux kernel tutorials focus on writing kernel modules directly in C.
Most compiler tutorials focus on parsers, ASTs, and code generation.
What happens when we combine both?
This project explores a simple but powerful idea:
Generate Linux kernel modules from an F# Domain Specific Language (DSL).
Instead of manually writing kernel C code, we define memory allocation behavior using a higher-level representation and generate kernel code automatically.
The project serves as a miniature introduction to:
- Linux Kernel Development
- Domain Specific Languages (DSL)
- Intermediate Representations (IR)
- Code Generation
- Compiler Architecture
- Memory Management
- Systems Programming
The Core Idea
Traditional workflow:
Developer
↓
C Source
↓
Kernel Module
DSL-driven workflow:
Developer
↓
F# DSL
↓
IR
↓
Code Generator
↓
Kernel C Source
↓
Kernel Module
The F# layer becomes a tiny compiler.
The generated C becomes the executable artifact.
Project Architecture
The current project follows this architecture:
┌──────────────────────────────┐
│ F# DSL Layer │
└──────────────┬───────────────┘
│
▼
┌──────────────────────────────┐
│ Intermediate IR │
└──────────────┬───────────────┘
│
▼
┌──────────────────────────────┐
│ F# Code Generator │
└──────────────┬───────────────┘
│
▼
┌──────────────────────────────┐
│ kmalloc_demo.c │
└──────────────┬───────────────┘
│
▼
┌──────────────────────────────┐
│ Linux Kernel Module │
└──────────────────────────────┘
The generated C source is not handwritten.
It is produced by the DSL compiler.
Why Build a DSL?
Many systems eventually introduce abstraction layers.
Examples:
| Domain |
Abstraction |
| SQL Databases |
SQL |
| Kubernetes |
YAML |
| Terraform |
HCL |
| Build Systems |
Makefiles |
| This Project |
F# DSL |
The goal is not to eliminate C.
The goal is to generate repetitive C safely and consistently.
Current Generator
The current implementation is intentionally simple.
F# Script
↓
String Generation
↓
Kernel C Source
Example:
emitAllocation "buffer" 128
Generated output:
ptr = kmalloc(128, GFP_KERNEL);
This is sufficient to demonstrate the full pipeline.
Understanding the Compiler Pipeline
A mature compiler generally contains multiple stages.
DSL
↓
Lexer
↓
Parser
↓
AST
↓
Semantic Analysis
↓
IR
↓
Optimization
↓
Code Generation
↓
Output
The current project implements only a subset.
However, the architecture naturally evolves toward a complete compiler.
Future Architecture
A more advanced version would look like:
Frontend DSL
↓
Parser
↓
AST
↓
Semantic Analyzer
↓
Kernel IR
↓
Verification Passes
↓
Optimization Passes
↓
Backend Code Generator
↓
Linux Kernel Module
This mirrors architectures used in:
- LLVM
- GCC
- Rust Compiler
- .NET Compiler Platform
What Is an IR?
IR stands for Intermediate Representation.
Think of it as a neutral language between the frontend and backend.
Instead of generating C directly:
DSL
↓
C
introduce:
DSL
↓
IR
↓
C
Benefits:
| Benefit |
Description |
| Decoupling |
Frontend independent from backend |
| Verification |
Easier rule checking |
| Optimization |
Easier transformations |
| Portability |
Multiple backends possible |
Example Kernel IR
A memory allocation request might become:
AllocateBuffer
Name = logs
Size = 128
Flags = GFP_KERNEL
The backend then generates:
ptr = kmalloc(128, GFP_KERNEL);
The IR acts as a stable contract.
Verification Passes
One interesting future enhancement is verification.
Before generating C code, the compiler could validate:
Allocation Size > 0
Allocation Size < MAX_LIMIT
No Duplicate Names
Valid GFP Flags
Example:
logs 128
cache 4096
temp 256
Verification succeeds.
But:
logs -10
would fail.
Why Verification Matters
Kernel bugs are expensive.
Potential issues include:
| Bug Type |
Impact |
| Invalid Allocation |
Crash |
| Memory Leak |
Resource Loss |
| Use-After-Free |
Security Risk |
| Overflow |
Corruption |
Verification allows problems to be detected before code generation.
Compile-Time DSL vs Runtime DSL
This distinction is important.
Compile-Time DSL
DSL
↓
Code Generation
↓
Compiled Binary
Advantages:
- Fast
- Zero Runtime Cost
- Compiler Optimized
Examples:
- C Macros
- X-Macros
- Template Systems
Runtime DSL
String Input
↓
Runtime Parser
↓
Execution
Advantages:
Disadvantages:
- Parsing Cost
- Runtime Complexity
Hot Path Considerations
Kernel code frequently runs in performance-critical paths.
Not every DSL approach is suitable.
| Method |
Hot Path Safe |
| Macros |
Yes |
| Inline Functions |
Yes |
| Static Structures |
Yes |
| Runtime Parsing |
No |
| String DSL |
No |
Compile-time generation is generally preferred for kernel workloads.
F# vs C: Which Layer Owns What?
A useful mental model:
| Layer |
Responsibility |
| F# |
Specification |
| IR |
Representation |
| Generator |
Translation |
| C |
Execution |
| Kernel |
Runtime |
This separation keeps each layer focused.
Build Workflow
Generate kernel source:
dotnet fsi kernel_ir.fsx
Build module:
make
Sign module:
sudo /usr/src/linux-headers-$(uname -r)/scripts/sign-file \
sha256 \
~/kernel_keys/MOK.key \
~/kernel_keys/MOK.crt \
kmalloc_demo.ko
Load module:
sudo insmod kmalloc_demo.ko
View logs:
dmesg | tail
Unload:
sudo rmmod kmalloc_demo
DSL vs Control Plane
This project is primarily a compiler/code-generation experiment.
That differs from a Control Plane architecture.
Compiler approach:
F# DSL
↓
Generated C
↓
Kernel Module
Control-plane approach:
F#
↓
Configuration
↓
Existing Kernel Module
The distinction is subtle but important.
One generates code.
The other controls behavior.
Key Takeaways
- DSLs can generate Linux kernel code.
- F# is acting as a tiny compiler.
- IR provides a stable abstraction layer.
- Verification can catch errors before code generation.
- Compile-time generation is ideal for performance-critical code.
- Kernel development and compiler design share many architectural ideas.
Conclusion
This project started as a simple experiment involving kmalloc() and F# scripting.
It quickly evolved into a miniature compiler architecture:
DSL
↓
IR
↓
Code Generator
↓
Kernel C Source
↓
Linux Kernel Module
The most interesting lesson is that compiler techniques are not limited to programming languages.
The same ideas can be applied to kernel development, infrastructure systems, networking platforms, and embedded software.
A small DSL today can become a sophisticated code-generation platform tomorrow.
References
The Linux kernel documentation describes kmalloc() as the standard allocation mechanism for kernel objects smaller than a page and outlines common GFP allocation flags.
Research into Linux kernel memory safety highlights how memory-management mistakes remain a major source of vulnerabilities in kernel software.
Repository
GitHub Repository:
https://github.com/aj333git/linux_kernel_kmalloc_f
