Efficient Cross-Domain DSL Development for Exascale

针对百亿亿次计算的高效跨域 DSL 开发

• 批准号: EP/W007940/1
• 负责人: Tobias Grosser
• 金额: $ 73.54万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW007940%2F1
• 关键词: Efficient; Cross; Domain; DSL; Development

Developing scientific software, for example for climate modeling or medical research, is a highly challenging task. Domain scientists are often deeply involved in low-level programming details just to make their code run sufficiently fast. These tedious, but important, optimization steps significantly reduce the productivity of scientists.Domain specific languages (DSLs) revolutionize the productivity of domain scientists by enabling them to focus on scientific questions rather than making their code run fast. Sophisticated DSL compilers automatically generate high-performance code from domain-specific high-level problem descriptions.While there are individual successes, the existing landscape of DSLs is scattered and the reuse of software components in DSL compiler implementations is limited as traditionally DSL compilers are built in isolation. This results in high development costs of new DSLs and prevents many DSLs from ever achieving a level of maturity and sustainability that enables uptake by the scientific community.This project revolutionizes the design of DSL compiler implementations by leveraging the breadth and cross-industry support of the MLIR compiler and Python ecosystems. Python is the tool of choice for application developers in many domains, such as machine learning, data science, and - we believe - an important component of the future of High Performance Computing software. This project establishes MLIR as a common representation for code at multiple levels of abstraction in DSL compiler development. DSLs embedded in various host languages, including Python and Fortran, will be easily built on top of MLIR. Instead of building DSL compilers as isolated monolithic towers, our research will build a toolbox that enables developers to build DSLs using a rich ecosystem of shared intermediate representations IRs and optimizations.This project evaluates, drives, and demonstrates the DSL design toolbox to build the next generation of DSLs for Seismic and Climate Modelling as well as Medical imaging. These will share common software components and make them available for other DSLs. An extensive evaluation will show the scalability of DSL software towards exascale.Finally, this project investigates how future disruptors, including artificial intelligence, data science, and on-demand HPC-as-a-service, will shape and influence the next generations of high performance software. This project will work towards deeply integrating modern interactive data analytics and machine learning methods from the Python ecosystem with high-performance scientific code.

开发科学软件，例如用于气候建模或医学研究，是一项艰巨的任务。域科学家通常会深入参与低级编程细节，只是为了使他们的代码运行得足够快。这些乏味但重要的优化步骤可显​​着降低科学家的生产力。特定于域的语言（DSL）通过使他们能够专注于科学问题而不是使他们的代码快速运行，从而彻底改变了领域科学家的生产力。复杂的DSL编译器会自动从域特异性的高级问题描述中生成高性能代码。尽管有个人成功，但DSL的现有景观却分散了，并且在DSL编译器实现中，软件组件的重复使用是传统上是DSL Compilers In Insimation Insimation Nexlation Nexlion In Insimation in Insial in Insial Insimation。这导致了新的DSLS的高开发成本，并阻止许多DSL达到一定程度的成熟和可持续性，从而使科学界的吸收能力。该项目通过利用对MLIR Compiler和Python Ecosystems的广度和交叉印度支持来改变DSL编译器实施的设计。 Python是许多领域中的应用程序开发人员的选择工具，例如机器学习，数据科学和 - 我们相信 - 高性能计算软件未来的重要组成部分。该项目将MLIR作为DSL编译器开发中多个抽象的代码的共同表示。嵌入在包括Python和Fortran在内的各种主机语言中的DSL将很容易在MLIR顶部建造。 Instead of building DSL compilers as isolated monolithic towers, our research will build a toolbox that enables developers to build DSLs using a rich ecosystem of shared intermediate representations IRs and optimizations.This project evaluates, drives, and demonstrates the DSL design toolbox to build the next generation of DSLs for Seismic and Climate Modelling as well as Medical imaging.这些将共享通用的软件组件，并使它们可用于其他DSL。广泛的评估将显示DSL软件向Exascale的可伸缩性。在本文中，该项目调查了包括人工智能，数据科学和按需HPC-AS-AS-AS-Service在内的未来破坏者如何塑造和影响下一代高性能软件。该项目将致力于从Python生态系统中深入整合现代交互式数据分析和机器学习方法，并具有高性能科学法典。

项目成果

Fortran High-Level Synthesis: Reducing the Barriers to Accelerating HPC Codes on FPGAs

• DOI: 10.1109/fpl60245.2023.00010
• 发表时间: 2023-08
• 期刊: 2023 33rd International Conference on Field-Programmable Logic and Applications (FPL)
• 作者: Gabriel Rodriguez-Canal;Nick Brown;Timothy Dykes;Jessica R. Jones;U. Haus

Fortran performance optimisation and auto-parallelisation by leveraging MLIR-based domain specific abstractions in Flang

通过利用 Flang 中基于 MLIR 的领域特定抽象来优化 Fortran 性能和自动并行化

• DOI: 10.1145/3624062.3624167
• 发表时间: 2023
• 作者: Brown N

Stencil-HMLS: A multi-layered approach to the automatic optimisation of stencil codes on FPGA

Stencil-HMLS：一种在 FPGA 上自动优化模板代码的多层方法

• DOI: 10.1145/3624062.3624543
• 发表时间: 2023
• 作者: Rodriguez-Canal G

IRDL: An IR Definition Language for SSA Compilers

IRDL：SSA 编译器的 IR 定义语言

• 作者: Mathieu Fehr