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

专业化和新技术的到来是推动异构系统的关键力量。异构已经被广泛使用，公共云提供的实例在计算能力和存储方面都是异构的。该项目确定了以下力量，这些力量将使系统异构，而不仅仅是计算和存储，使编程和编译复杂化，而不仅仅是我们今天面临的挑战。该项目开发了Arion，一个基于几个统一思想的异构平台上编译程序的系统。Arion系统将在实际相关的工作负载上进行评估，从计算机视觉和虚拟现实到图形计算、机器学习和流处理。研究人员将与工业界的合作伙伴合作，将研究成果转化为产品，该项目开发的工具和软件将作为开源发布。这个项目的研究依赖于四个统一的想法。第一个要点探讨了调度和类型系统将程序的规范从它的实现策略中分离出来，支持性能可移植性，因为人们可以在不改变程序的情况下选择它的并行性、局部性和硬件映射。第二个要点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工件，如调度、资源和内存一致性模型。这允许自动合成这些工件。第三个推力使用资源模型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决策之前使用模型评估性能来做出高级决策。最后，研究人员将使用正式的方法将程序提升到我们的dsl中，并验证和合成程序，提供高度的自动化。验证器和合成器从dsl的描述中自动生成。

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.