MLIR: A Compiler Infrastructure for the End of Moore's Law (2002.11054v2)

Abstract: This work presents MLIR, a novel approach to building reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain specific compilers, and aid in connecting existing compilers together. MLIR facilitates the design and implementation of code generators, translators and optimizers at different levels of abstraction and also across application domains, hardware targets and execution environments. The contribution of this work includes (1) discussion of MLIR as a research artifact, built for extension and evolution, and identifying the challenges and opportunities posed by this novel design point in design, semantics, optimization specification, system, and engineering. (2) evaluation of MLIR as a generalized infrastructure that reduces the cost of building compilers-describing diverse use-cases to show research and educational opportunities for future programming languages, compilers, execution environments, and computer architecture. The paper also presents the rationale for MLIR, its original design principles, structures and semantics.

MLIR: A Compiler Infrastructure for the End of Moore's Law

The paper "MLIR: A Compiler Infrastructure for the End of Moore's Law" presents the Multilevel Intermediate Representation (MLIR) as a versatile framework designed to address the growing challenges in compiler construction, particularly in the context of heterogeneous computing environments precipitated by the slowdown of Moore's Law. The work is primarily authored by Chris Lattner et al., involving contributions from institutions like Google and IISc.

Overview and Motivation

MLIR is introduced as a novel approach to constructing reusable and extensible compiler infrastructure, aiming to overcome software fragmentation and improve compilation efficiency across diverse hardware landscapes. The underlying motivation stems from the limitations of existing infrastructures like LLVM and JVM, which employ a "one size fits all" abstraction model that may not be optimal for all programming paradigms or hardware architectures. Additionally, domain-specific intermediate representations (IRs) often involve high engineering costs and result in redundant efforts across different languages and applications.

Core Contributions

MLIR's design is built on a few key principles:

1. Customizability and Extensibility: The paper emphasizes building minimal built-in features, allowing extensive customization at the operation, type, and dialect levels. This adaptability ensures that MLIR can accommodate future compiler and language needs.
2. Structured Control Flow and SSA: Leveraging Static Single Assignment (SSA) form, MLIR introduces nested regions to represent higher-level abstractions like loop trees, facilitating transformations that are difficult in flat control flow representations.
3. Progressive Lowering: MLIR aims to support stepwise lowering through multiple abstraction levels, enabling efficient transformations and analyses at the appropriate levels of detail.
4. Declarative Rewrite Rules: Through the Declarative Rewrite Rule (DRR) system, MLIR provides a framework for specifying transformations in a declarative manner, improving the modularity and reusability of compilation strategies.
5. Wide Range of Applications: Demonstrations in TensorFlow, polyhedral code generation, and domain-specific compilers affirm MLIR's applicability across different domains. Notably, the work on TensorFlow graphs illustrates MLIR's capability to optimize high-level dataflow representations for various hardware targets.

Implications and Future Directions

The implications of MLIR are multifaceted, impacting both practical compiler engineering and theoretical language design. By enabling higher abstraction levels and facilitating the interoperation of different dialects, MLIR promotes reuse and modularity in compiler infrastructure. This can potentially lead to more efficient, maintainable, and scalable compiler systems capable of adapting to rapidly evolving hardware demands.

The paper suggests that MLIR opens new research avenues in secure compilation, safety-critical systems, and further optimizations in high-performance computing (HPC) and ML contexts. Additionally, the conceptual shift towards extensibility in compiler design may catalyze new approaches in educational curricula regarding compiler construction and IR design.

Conclusion

MLIR represents a significant step in addressing the complex challenges posed by heterogeneous computing and the stagnation of traditional hardware scaling. By focusing on extensibility, modularity, and adaptability, MLIR provides a compelling framework applicable to a wide range of computational domains, setting the stage for the development of future compiler technologies. As the project and its community grow, further exploration will likely refine best practices in abstraction design, fostering advancements in both compiler infrastructure and language environments.