XPS: FULL: DSD: Scalable High Performance with Halide and Simit Domain Specific Languages

标题：XPS: FULL: DSD：使用Halide和Simit领域特定语言的可扩展高性能今天，获得可扩展的并行性能需要专家的英雄编程。在这个建议中，我们正在开发一种基于领域特定语言（dsl）的方法，以简化程序员利用可伸缩并行性的能力所需的工作。这个建议的智力上的优点是表明dsl可以为程序员提供一种方法来利用可伸缩的性能，而不需要付出巨大的努力。拥有可扩展并行性能的简单路径将对气候建模和其他大规模科学模拟等领域具有更广泛的意义和重要性，使它们能够有效地利用大型机器和云。本建议旨在从根本上简化高性能DSL结构。首先，它将引入一个统一的转换框架，其中通过示例描述复杂的程序转换。使用合成技术，将提取和应用本地化重写规则的组合，简化实现，同时提供正确性保证。其次，通过扩展LLVM，构建统一的并行底层中间表示。有了新的并行后端，dsl只需要暴露算法的并行性，后端将完成所有架构特定的并行性映射到向量、非均匀内存访问（NUMA）、图形处理单元（GPU）和分布式并行组件。第三，开发统一的自动调优框架。前端转换的有效性取决于后端利用它们的能力。统一的自动调谐框架将通过将转换选择卸载到自动调谐器，从而完全消除这种复杂性，自动调谐器将使用复杂的机器学习技术来经验地选择产生最佳可扩展并行性能的转换。介绍的思想将通过两种重要的领域特定语言进行演示。Halide DSL用于图像处理管道，Simit DSL用于物理模拟。

Title: XPS: FULL: DSD: Scalable High Performance with Halide and Simit Domain Specific LanguagesToday, getting scalable parallel performance requires heroic programming by experts. In this proposal we are developing a methodology based on Domain Specific Languages (DSLs) to simplify the programmer effort required to harness the power of scalable parallelism. The intellectual merits of this proposal are to show that DSLs can provide a way for programmers to take advantage of scalable performance without a heroic effort. Having a simple path for scalable parallel performance will have a broader significance and importance on areas such as climate modeling and other simulations of large-scale science, by enabling them to efficiently utilize large-scale machines and the cloud.This proposal aims to radically simplify high performance DSL construction. First, it will introduce a unified transformation framework where complex program transformations are described by example. Using synthesis technology, combinations of localized rewriting rules will be extracted and applied, simplifying the implementation while providing correctness guarantees. Second, it will build a unified parallel low-level intermediate representation by extending LLVM. With the new parallel backend, DSLs only have to expose algorithmic parallelism and the backend will do all architecture-specific mapping of parallelism to vector, non-uniform memory access (NUMA), graphics processing unit (GPU) and distributed parallel components. Third, it will develop a unified auto-tuning framework. Effectiveness of frontend transformations depends on the ability of backends to exploit them. The unified auto-tuning framework will completely eliminate this complexity by offloading transformation selection to the auto-tuner which will use sophisticated machine learning techniques to empirically select transformations that yield the best scalable parallel performance. The ideas introduced will be demonstrated through two important domain-specific languages ? the Halide DSL for image processing pipelines and the Simit DSL for physical simulations.