SHF: Small: Specializing Compilers For High Performance Computing Through Coordinated Data and Algorithm Optimizations

SHF：小型：通过协调数据和算法优化实现高性能计算的专用编译器

• 批准号: 1421443
• 负责人: Qing Yi
• 金额: $ 47.76万
• 依托单位: University of Colorado at Colorado Springs
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421443&HistoricalAwards=false
• 关键词: SHF; Small; Specializing; Compilers; Performance

This research brings about a new methodology for developing compilers, where the data structure and algorithm implementations of software applications are independently normalized and categorized into commonly occurring patterns, compiler optimizations are made customizable components that can be flexibly composed, and all optimizations are closely coordinated and collectively specialized to attain a highest level of performance. The pattern-based specialization specifically targets a number of domains, e.g., dense/sparse matrix codes, stencil computations, and graph/machine learning algorithms, which are critical to scientific computing. A uniform annotation interface is provided for developers to concisely document the higher-level semantics of abstractions provided by varying domain-specific and parallel programming libraries, thereby allowing the development of specially customized library-aware compilers that can automatically coordinate the uses of library abstractions to maximize the overall performance of large scale multiprocessor applications. Automated optimization tuning support is provided to support the performance portability of applications on modern heterogeneous computing platforms.The deliverables of this research include a collection of specialized compiler optimizers, distributed open source online, with associated auto-tuning toolkits to target them for varying modern multi-core and GPU platforms, and with a graphical user interface for users to interactively invoke these optimizers. These optimizers, together with their interactive configuration interfaces, are expected to fundamentally change how high performance computing applications are developed, while providing computational specialists a toolset to automatically generate optimized library kernels without manually composing assembly codes.

该研究为开发编译器带来了一种新的方法，其中软件应用程序的数据结构和算法实现被独立规范化并分类为常见的模式，编译器优化是可定制的组件，可以灵活地组合，所有优化都是密切协调和共同专业化的，以达到最高水平的性能。基于模式的专门化专门针对许多领域，例如密集/稀疏矩阵代码、模板计算和图/机器学习算法，这些对科学计算至关重要。为开发人员提供了统一的注释接口，以便简洁地记录由不同领域特定的并行编程库提供的抽象的高级语义，从而允许开发专门定制的库感知编译器，这些编译器可以自动协调库抽象的使用，以最大限度地提高大规模多处理器应用程序的总体性能。提供了自动优化调优支持，以支持现代异构计算平台上应用程序的性能可移植性。这项研究的成果包括一系列专门的编译器优化器，分布在网上的开源，以及相关的自动调优工具包，以针对不同的现代多核和GPU平台，以及一个图形用户界面，供用户交互式地调用这些优化器。这些优化器，连同它们的交互式配置接口，有望从根本上改变高性能计算应用程序的开发方式，同时为计算专家提供一个工具集来自动生成优化的库内核，而无需手动编写汇编代码。

项目成果

Automating Non-Blocking Synchronization In Concurrent Data Abstractions

• DOI: 10.1109/ase.2019.00074
• 发表时间: 2019-11
• 期刊: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)
• 作者: Jiange Zhang;Qing Yi;D. Dechev