SHF Small: A Compiler-Based Auto-Tuning Framework for Many-Core Code Generation

SHF Small：用于多核代码生成的基于编译器的自动调优框架

• 批准号: 1018881
• 负责人: Mary Hall
• 金额: $ 48.11万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018881&HistoricalAwards=false
• 关键词: SHF; Small; Compiler; Based; Auto

This project provides a compiler-based foundation for data-parallel optimization and code generation that can yield very high performance across a range of different multi-core and many-core architectures while increasing the productivity of application programmers. The project is targeting two distinct architectures, a conventional quad-core microprocessor and a graphics processing unit (GPU) with 240 cores, as well as a heterogeneous system that combines both. The technology represents a significant departure from the organization of contemporary compilers, automating as much of optimization as possible, but opening up the mapping process to provide savvy programmers the control they so often desire. The unique features of the system include: (1) transformation recipes as a programmer's or high-level compiler's interface to optimization and code generation; (2) a polyhedral framework to mathematically represent code structures and optimizations for robust code generation; and (3) auto-tuning technology to compactly describe and systematically evaluate a range of possibleimplementations of a computation. The most novel of these is the is the transformation recipe, which factors out the commonality of OpenMP, CUDA and OpenCL code generation, thus supporting the heterogeneous platform and facilitating late-binding decisions of which implementation to use. The software system from previous work is being released, and has accelerated both libraries and scientific applications. With the right transformation recipe for the auto-tuning system to evaluate, the resulting automatically-generated code yields high performance, in some cases competitive or even better than manually-coded implementations. The PIs plan to expand this capability to a broader set of applications and architectural features.

该项目为数据并行优化和代码生成提供了基于编译器的基础，该基础可以在一系列不同的多核和多核体系结构中产生非常高的性能，同时提高应用程序程序员的生产率。 该项目针对两个不同的体系结构，一个常规的四核微处理器和一个具有240个核心的图形处理单元（GPU），以及将两者结合的异质系统。 该技术与当代编译器的组织有很大的不同，自动化了尽可能多的优化，但是打开了映射过程，以提供精明的程序员他们经常希望的控制。 系统的独特功能包括：（1）作为程序员或高级编译器的界面进行优化和代码生成的转换配方； （2）用于数学代表代码结构和可靠代码生成的优化的多面体框架； （3）自动调整技术，以紧凑并系统地评估计算的一系列可能实施范围。 其中最新颖的是转换配方，它因OpenMP，Cuda和OpenCL代码生成的共同点而产生了影响，从而支持了异构平台并促进了使用哪种实施的后期结合决策。 先前工作的软件系统正在发布，并加速了库和科学应用程序。 通过适合自动调整系统进行评估的正确转换配方，由此产生的自动生成代码可产生高性能，在某些情况下，竞争性甚至比手动编码的实现更好。 PIS计划将此功能扩展到更广泛的应用程序和建筑特征。