A shared compilation stack for distributed-memory parallelism in stencil DSLs

This is a Plain English Papers summary of a research paper called A shared compilation stack for distributed-memory parallelism in stencil DSLs. If you like these kinds of analysis, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter.

Overview

• Domain-Specific Languages (DSLs) can increase programmer productivity and provide high performance for specific tasks
• DSLs allow scientists to express problems at a high level, providing rich details that optimizing compilers can use to target supercomputers
• However, the development and maintenance of DSLs can be costly
• The siloed design of DSL compilers limits the ability to benefit from shared infrastructure, creating uncertainties around longevity and adoption at scale
• The paper describes how the authors have tailored the MLIR compiler framework to the High Performance Computing (HPC) domain, bringing the same synergies that the machine learning community exploits across their DSLs

Plain English Explanation

Domain-Specific Languages (DSLs) are specialized programming languages designed for particular tasks or domains, like finance, biology, or physics. They allow experts in those fields to write code that is more natural and intuitive for their specific problem, rather than having to use a general-purpose programming language. This can make the experts more productive and lead to higher-performing programs.

However, creating and maintaining these specialized DSLs can be quite challenging and expensive. Each DSL needs its own compiler and toolchain, which can be a lot of work. Additionally, the different DSLs often don't share much infrastructure, which makes it harder for them to benefit from improvements or new features in the same way that general-purpose languages can.

In this paper, the researchers describe how they have adapted a widely-used compiler framework called MLIR to work well for high-performance computing (HPC) applications. MLIR was originally developed for the machine learning community, but the researchers have extended it to better support the kinds of domain-specific languages used in scientific computing and simulation.

By building on MLIR, the researchers were able to create new HPC-focused abstractions and share common components across several different DSL compilers used in the HPC world. This allows these specialized languages to benefit from the ongoing development and improvements to the underlying MLIR framework, without each one having to rebuild everything from scratch. The result is that the DSL users get the convenience and performance they need, while also being able to take advantage of a more robust and long-lasting compiler ecosystem.

Technical Explanation

The key innovation in this work is the adaptation of the MLIR compiler framework to the domain of high-performance computing (HPC). MLIR was originally developed by the machine learning community to provide a common infrastructure for their diverse set of domain-specific languages (e.g. TensorFlow, PyTorch).

The authors recognized that the HPC community faces similar challenges with the proliferation of specialized finite-difference stencil DSLs (e.g. Devito, PSyclone, Open Earth Compiler). Each of these DSLs has its own compiler and toolchain, which limits the ability to share common components and benefit from infrastructure improvements.

By adapting MLIR to the HPC domain, the authors were able to introduce new abstractions tailored for distributed stencil computations, a common pattern in scientific computing and simulation. This allowed them to build a shared compiler ecosystem that could generate high-performance executables from multiple distinct HPC stencil-DSL frontends.

The key technical contributions include:

• Extending the MLIR framework with HPC-specific abstractions for message passing and distributed stencil computations
• Demonstrating the ability to share common compiler components across three different HPC stencil-DSL compilers (Devito, PSyclone, Open Earth Compiler)
• Showing that the MLIR-based compilers can generate high-performance executables that are comparable to the original hand-tuned DSL compilers

Critical Analysis

The paper provides a compelling vision for how the HPC community can benefit from the synergies enabled by a shared compiler framework, similar to what the machine learning community has achieved with MLIR.

However, the paper does not delve into some of the potential challenges and limitations of this approach. For example, it's unclear how easy it will be to integrate existing, entrenched DSL compilers into the MLIR ecosystem, or how much effort will be required to maintain backwards compatibility as MLIR evolves.

Additionally, the performance evaluations in the paper are limited to a small set of benchmarks. More extensive testing across a wider range of HPC applications would be needed to fully validate the capabilities of the MLIR-based compilers.

It would also be helpful to understand how the MLIR-based approach compares to other efforts to create common infrastructure for HPC DSLs, such as the Telescoping Languages project. A more thorough comparison to related work in this area could provide helpful context.

Overall, the paper presents a promising direction for addressing the challenges of DSL proliferation in HPC, but further research and real-world deployment experience will be needed to fully assess the merits and limitations of this approach.

Conclusion

This paper describes an innovative approach to addressing the challenges faced by the high-performance computing (HPC) community in managing the proliferation of domain-specific languages (DSLs). By tailoring the widely-adopted MLIR compiler framework to the needs of HPC, the researchers have created a shared infrastructure that can benefit multiple distinct stencil-based DSL compilers.

This work has the potential to significantly improve the long-term sustainability and performance of specialized DSLs in HPC, by allowing them to leverage common components and ongoing improvements to the underlying MLIR ecosystem. If successful, this could lead to greater adoption of DSLs in scientific computing and simulation, empowering domain experts to more easily express their problems at a high level while still achieving high-performance execution.

The technical details and early results presented in the paper are promising, but further research and real-world deployment experience will be needed to fully validate the merits of this approach and address any remaining challenges or limitations. Nonetheless, this work represents an important step towards creating a more robust and collaborative compiler ecosystem for the diverse needs of the HPC community.

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