CAPA: Collaborative Research: ARION: Taming Heterogeneity with DSLs, Approximation, and Synthesis

CAPA：合作研究：ARION：通过 DSL、近似和综合来驯服异质性

• 批准号: 2217878
• 负责人: Jonathan Ragan-Kelley
• 金额: $ 30万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217878&HistoricalAwards=false
• 关键词: CAPA; Collaborative; Research; ARION; Taming

Specialization and the arrival of new technologies are key forces motivating heterogeneous systems. Heterogeneity is already in use widely, with public clouds offering instances that are heterogeneous in both compute capabilities and storage. This project identifies the following forces that will make systems heterogeneous beyond just compute and storage, complicating programming and compilation beyond the challenges that we face today. This project develops Arion, a system for compiling programs onto heterogeneous platforms based on several unifying ideas. The Arion system will be evaluated on practically relevant workloads ranging from computer vision and virtual reality, to graph computations, machine learning and stream processing. The investigators will work with partners in industry to transfer research results to products, and the tools and software developed by this project will be released as open source.The research in this project relies on four unifying ideas. The first thrust explores schedules and type systems separate a program's specification from its implementation strategy, enabling performance portability because one can select, without changing the program, its parallelism, locality, and hardware mapping. The second thrust uses domain-specific languages to describe not only programs but also artifacts used during compilation, such as schedules, resource-, and memory consistency models. This allows automatic synthesis of these artifacts. The third thrust uses resource models to bring scheduling and synthesis to large programs because the target program need not be scheduled or synthesized all at once. Instead, the compiler makes high-level decisions by estimating performance using a model before committing to low-level decisions. Finally, the investigators will use formal methods to lift programs into, and verify and synthesize programs in our DSLs, providing a high degree of automation. The verifiers and synthesizers are automatically generated from descriptions of DSLs.

专业化和新技术的到来是推动不同系统的关键力量。异构性已经得到了广泛的使用，公共云提供的实例在计算能力和存储方面都是异构性的。该项目确定了以下因素，它们将使系统的异构性超越计算和存储，使编程和编译变得复杂，超出了我们今天面临的挑战。这个项目开发了Arion，这是一个基于几个统一思想在不同平台上编译程序的系统。Arion系统将在从计算机视觉和虚拟现实到图形计算、机器学习和流处理等实际相关工作负载上进行评估。研究人员将与业界合作伙伴合作，将研究成果转化为产品，并将该项目开发的工具和软件作为开源发布。该项目的研究依赖于四个统一的理念。第一个重点是探索时间表和类型系统，将程序的规范与其实现策略分开，支持性能可移植性，因为人们可以在不更改程序的情况下选择其并行性、局部性和硬件映射。第二个重点是使用特定于领域的语言不仅描述程序，还描述编译过程中使用的构件，如调度、资源和内存一致性模型。这允许自动合成这些人工产物。第三个推力使用资源模型来为大型程序带来调度和综合，因为目标程序不需要一次全部调度或综合。相反，编译器通过在提交低级别决策之前使用模型估计性能来做出高级别决策。最后，调查人员将使用正式方法将程序提升到我们的DSL中，并在其中验证和综合程序，提供高度自动化。验证器和合成器是根据DSL的描述自动生成的。

项目成果

Gradient Descent: The Ultimate Optimizer

• 发表时间: 2019-09
• 作者: Kartik Chandra;Audrey Xie;Jonathan Ragan-Kelley;E. Meijer

Exocompilation for productive programming of hardware accelerators

• DOI: 10.1145/3519939.3523446
• 发表时间: 2022-06
• 期刊: Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation
• 作者: Yuka Ikarashi;G. Bernstein;Alex Reinking;Hasan Genç;Jonathan Ragan-Kelley

Searching for Fast Demosaicking Algorithms

• DOI: 10.1145/3508461
• 发表时间: 2022-03
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: Karima Ma;Michaël Gharbi;Andrew Adams;Shoaib Kamil;Tzu-Mao Li;Connelly Barnes;Jonathan Ragan-Kelley

Efficient automatic scheduling of imaging and vision pipelines for the GPU

• DOI: 10.1145/3485486
• 发表时间: 2020-12
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Luke Anderson;Andrew Adams;Karima Ma;Tzu-Mao Li;Tian Jin;Jonathan Ragan-Kelley

Designing Perceptual Puzzles by Differentiating Probabilistic Programs

• DOI: 10.1145/3528233.3530715
• 发表时间: 2022-04
• 期刊: ACM SIGGRAPH 2022 Conference Proceedings
• 作者: Kartik Chandra;Tzu-Mao Li;J. Tenenbaum;Jonathan Ragan-Kelley