Add Snow Intermediate Representation (IR) Code Generation

[cowboy8625]

Description:

Currently, Snow compiles directly to machine code or relies on external backends like LLVM. To enhance flexibility and enable optimizations, this issue proposes the addition of a Snow Intermediate Representation (IR) that can serve as an intermediate step in the compilation process. The Snow IR can then be efficiently handed off to LLVM for further code generation and optimization.

Proposed Solution:

1. Design Snow Intermediate Representation (IR):

   • Create a human-readable, text-based IR language that is expressive enough to capture Snow language constructs.
   • Define an IR format that can represent Snow's functions, data structures, and control flow.

2. IR Code Generation:

   • Modify the Snow compiler to generate Snow IR code as an intermediate representation.
   • The Snow IR should be generated after the parsing and semantic analysis phases and before the final code generation.

3. Integration with LLVM:

   • Develop a mechanism to pass the Snow IR to the LLVM compiler infrastructure.
   • Use LLVM's IR capabilities to optimize and generate machine code from the Snow IR.

4. Example Snow IR Code:

   • The Snow IR should be designed to represent the Snow language effectively. -Static Single-Assignment (SSA) form is a popular intermediate representation used by compilers. In Snow's context, the Snow IR in SSA form might look like this:
   • Consider a simple Snow function that adds two integers:

function add(a: Int, b: Int) -> Int {
    entry:
        %1 = parameter a
        %2 = parameter b
        %3 = add i32 %1, %2
        %4 = return i32 %3
}

In this example, we have an add function that takes two integers, a and b, and returns their sum. The IR is in SSA form with distinct identifiers for each use of a variable, which simplifies many aspects of analysis and optimization.

Here's a breakdown of the SSA-form IR code:

• %1 and %2 represent the incoming parameters a and b.
• %3 is the result of the addition of %1 and %2.
• %4 represents the return value of the function, which is %3.

The use of SSA form provides several benefits, such as simplifying variable scoping, aiding in dead code elimination, and enabling more straightforward analysis and optimization passes. However, this is a simplified example, and Snow's actual SSA-form IR would be more comprehensive, capturing the language's full range of features and constructs.

5. Testing and Validation:
   • Thoroughly test the IR generation and the LLVM integration using a suite of Snow programs.
   • Ensure that the Snow IR correctly represents the semantics of Snow programs.

Benefits:

1. Improved Code Quality: Separating the IR generation from LLVM interaction allows Snow to perform more language-specific optimizations before handing off to LLVM.

2. Flexibility: Introducing a Snow IR provides flexibility to explore different backends or optimizations in the future.

3. Debugging: Debugging Snow code will become more accessible, as developers can inspect the intermediate representation.

4. Community Contributions: By adding an intermediate step, the Snow compiler becomes more approachable to contributors who might not be familiar with LLVM or low-level code generation.

Dependencies:

The development team will need in-depth knowledge of LLVM and compiler design, as well as experience in designing intermediate representations.

Tasks:

1. Design Snow IR Syntax: Define the syntax and structure of the Snow IR language.
2. IR Code Generation: Implement the Snow IR generation in the compiler.
3. LLVM Integration: Create a mechanism to hand off the Snow IR to LLVM.
4. Testing and Validation: Develop a comprehensive test suite and validate the entire compilation process.

Expected Timeframe:

The implementation of Snow IR and its integration with LLVM is expected to take several months, depending on the complexity of the task.

Additional Information:

This enhancement will significantly enhance the Snow compiler's capabilities and maintainability. It's a valuable step toward creating a powerful and flexible programming language.

Contributor Guidance:

Contributors interested in working on this feature should have experience with LLVM, compiler construction, and intermediate representation design. Collaborative development and feedback from the Snow community are encouraged.

Reference Links:

Link to LLVM - LLVM project website for reference and documentation. Link to InkWell - crate used for llvm

[cowboy8625]

max x y
  : Int -> Int -> Int
  = if x > y then x else y

min x y
 : Int -> Int -> Int
 = if x < y then x else y

clamp low high input
  : Int -> Int -> Int -> Int
  = max low (min input high)

main : IO = print (clamp 1 10 5)

IR CODE OUTPUT:

function max(x: Int, y: Int) -> Int {
    entry:
        %1 = parameter x
        %2 = parameter y
        %3 = compare i32 %1, %2
        br i1 %3, label %max_true, label %max_false

    max_true:
        %4 = return i32 %1

    max_false:
        %5 = return i32 %2
}

function min(x: Int, y: Int) -> Int {
    entry:
        %1 = parameter x
        %2 = parameter y
        %3 = compare i32 %1, %2
        br i1 %3, label %min_true, label %min_false

    min_true:
        %4 = return i32 %1

    min_false:
        %5 = return i32 %2
}

function clamp(low: Int, high: Int, input: Int) -> Int {
    entry:
        %1 = parameter low
        %2 = parameter high
        %3 = parameter input
        %4 = call i32 @max(i32 %1, i32 %3)
        %5 = call i32 @min(i32 %4, i32 %2)
        %6 = return i32 %5
}

function main() -> IO {
    entry:
        %1 = call i32 @clamp(i32 1, i32 10, i32 5)
        %2 = call void @print(i32 %1)
        %3 = return IO
}

In this Snow IR code, we have converted the max, min, and clamp functions and the main function into their corresponding SSA-form IR representations. The code includes basic block labels, parameter passing, comparisons, and function calls. This IR can serve as an intermediate representation that can be further optimized and translated into machine code or other target-specific code.

[cowboy8625]

1. Readability: A text-based IR allows developers to easily understand and debug the generated code. It's human-readable, which makes it accessible to both developers and anyone interested in understanding the compilation process.

2. Debugging: When there are issues with the generated code or when developers want to inspect how their Snow code is translated into low-level representations, a text-based IR makes it easier to identify problems and understand what's happening under the hood.

3. Education: It can serve as an educational resource for those interested in learning about compilers, code generation, and the intricacies of the Snow language. It could also be used for teaching purposes.

4. Portability: Text-based IR is platform-agnostic and can be easily shared and transferred across different systems without worrying about binary compatibility or endianness issues.

5. Development Tools: Tools like IR optimizers, profilers, and analyzers can benefit from a human-readable format for the IR. Developers can utilize these tools to analyze and enhance their Snow code.

6. Documentation: The text-based IR code can be a valuable part of the language's documentation. It can help developers understand how their Snow code is compiled, which can be crucial for optimizing performance.

To implement a text-based IR representation in Snow, the language might choose a format similar to LLVM's textual IR (LLVM IR). LLVM IR provides a convenient and widely-used example of a human-readable representation for low-level code.

Developers could use Snow's compiler or other tools to generate or inspect this text-based IR, making it an integral part of the Snow development process.

Here's a simplified example of what the Snow text-based IR for the provided Snow code could look like:

function max(x: Int, y: Int) -> Int {
    entry:
        %1 = parameter x
        %2 = parameter y
        %3 = compare i32 %1, %2
        br i1 %3, label %max_true, label %max_false

    max_true:
        %4 = return i32 %1

    max_false:
        %5 = return i32 %2
}

function min(x: Int, y: Int) -> Int {
    entry:
        %1 = parameter x
        %2 = parameter y
        %3 = compare i32 %1, %2
        br i1 %3, label %min_true, label %min_false

    min_true:
        %4 = return i32 %1

    min_false:
        %5 = return i32 %2
}

function clamp(low: Int, high: Int, input: Int) -> Int {
    entry:
        %1 = parameter low
        %2 = parameter high
        %3 = parameter input
        %4 = call i32 @max(i32 %1, i32 %3)
        %5 = call i32 @min(i32 %4, i32 %2)
        %6 = return i32 %5
}

function main() -> IO {
    entry:
        %1 = call i32 @clamp(i32 1, i32 10, i32 5)
        %2 = call void @print(i32 %1)
        %3 = return IO
}

This text-based representation can be saved in files, shared, and manipulated, providing transparency and control over the compilation process in Snow.