Welcome to the home page of the 46th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2025)! PLDI is the premier forum in the field of programming languages and programming systems research, covering the areas of design, implementation, theory, applications, and performance. PLDI 2025 will be held in-person at the Westin Josun Seoul in Seoul, South Korea. The main PLDI conference will be held Wednesday, 18 June through Friday, 20 June. Workshops and tutorials were held on Monday, 16 June and Tuesday, 17 June. PLDI 2025 Travel Guide Nuno Lopes has kindly writte ...

The following document provides an overview of the TT-MLIR project, with a focus on the technical specifications of an MLIR-based compiler stack. So what exactly is an MLIR-based compiler stack?

This paper has a really nice Intro, pay close attention to how they lay out the storyline.

An esoteric exploration on the target independence of compiler IRs.

The QBE compiler backend is designed to be a compact yet high-performance C embeddable backend that prioritizes correctness, simplicity, and user-friendliness. It compiles on various x64 operating systems and boasts features like IEEE floating point support, SSA-based intermediate language, and quick compilation times. While currently limited to x64 platforms, plans include ARM support and further enhancements. The backend has been successfully utilized in various projects, showcasing its adaptability and effectiveness in compiler development.

A new JIT engine for PHP is being developed, improving performance and simplifying development. The engine will be included in the next major PHP version, potentially PHP 9.0. The new JIT engine generates a single Intermediate Representation (IR), eliminating the need to support assembler code for different CPUs.

MLIR is a versatile compiler infrastructure designed to address software fragmentation and improve compilation for different hardware. It aims to reduce the cost of building domain-specific compilers and facilitate the connection of existing compilers. MLIR offers a standardized approach to code generation and optimization across various application domains and hardware targets.

The text is a guide titled "MLIR — Getting Started" by Math ∩ Programming available on www.jeremykun.com.

Learning LLVM IR can be beneficial for generalist working programmers to understand what their compiler is doing to create highly optimized code. LLVM IR is well-documented and can be treated as a slightly weird programming language. It is strongly typed and requires explicit type annotations. LLVM IR is a static single assignment form (SSA) IR and has properties that make optimizations simpler to write. It supports control flow operations, arithmetic instructions for different types, and memory operations. There are also LLVM intrinsics available for specific functions. However, some parts of LLVM's semantics, such as undefined behavior and pointer provenance, can be challenging to navigate.