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

职务名称：光电子能谱：满：渠务署：使用Halide和Simit特定领域的并行处理器实现可扩展的高性能如今，要获得可扩展的并行性能，需要专家进行出色的编程。在本提案中，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扩展并行性所需的工作。这个建议的智力价值在于表明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。拥有一个简单的路径可扩展的并行性能将有更广泛的意义和重要性的领域，如气候建模和其他模拟的大规模科学，使他们能够有效地利用大型机器和云。该提案旨在从根本上简化高性能DSL建设。首先，它将介绍一个统一的转换框架，其中复杂的程序转换描述的例子。使用合成技术，本地化重写规则的组合将被提取和应用，简化实现，同时提供正确性保证。其次，它将通过扩展LLVM来构建统一的并行低级中间表示。使用新的并行后端，DSL只需公开算法并行性，后端将执行并行性到向量、非统一内存访问（NUMA）、图形处理单元（GPU）和分布式并行组件的所有特定于架构的映射。第三，它将开发一个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。统一的自动调优框架将通过将转换选择卸载到自动调优器来完全消除这种复杂性，自动调优器将使用复杂的机器学习技术来凭经验选择产生最佳可扩展并行性能的转换。 所介绍的思想将通过两种重要的领域特定语言来演示。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.