CSR-AES: Collaborative Research: Library Generators for Advanced Execution Systems

CSR-AES：协作研究：高级执行系统的库生成器

• 批准号: 0509432
• 负责人: David Padua
• 金额: --
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0509432&HistoricalAwards=false
• 关键词: CSR; AES; Collaborative; Research; Library

It is well-known that most applications do not exploit the full performance potential of computer hardware. A promising new approach for addressing some of these problems is provided by library generators. Although all library generators have much in common, there is currently little code reuse in their implementation. This project will develop an infrastructure, called Proteus to generate highly optimized libraries for four important problem domains: dense linear algebra, sorting, data mining, and network packet processing. The platforms of interst for these environments include sequential processors with deep memory hierarchies, multi-core systems that provide small-scale shared-memory parallelism, and custom network processors.Research advances on several fronts are required to make Proteus a reality: a simple domain language for linear algebra in the proposal and appropriate DSL's for data mining and network packet processing. Once domain-specific optimizations are done, the programs will be translated into a lower-level language called the X language, to be developed under the project, that will represent in a compact way the search space as well as the strategy for adapting the code to the hardware. Proteus will contain tools for gathering information about the target machine, for guiding the selection process either statically or dynamically, for implementing domain-independent optimizations of the X code, and for generating C code as the final output.The Proteus infrastructure will enable rapid prototyping of library generators for different domains. The libraries that will beproduced in the four different domains will be useful and interesting in their own right. In addition, tools will be developed for gathering information about the target machines and the computing environment, can be used by themselves in other projects.

众所周知，大多数应用程序没有充分利用计算机硬件的性能潜力。库生成器为解决其中一些问题提供了一种很有前途的新方法。尽管所有的库生成器都有很多共同之处，但目前在它们的实现中几乎没有代码重用。该项目将开发一个名为Proteus的基础设施，为四个重要的问题域生成高度优化的库：密集线性代数、排序、数据挖掘和网络数据包处理。这些环境的感兴趣的平台包括具有深度内存层次的顺序处理器、提供小规模共享内存并行性的多核系统和定制的网络处理器。要使Proteus成为现实，需要在几个方面取得进展：提案中的线性代数简单领域语言，以及用于数据挖掘和网络数据包处理的合适的DSL。一旦完成特定领域的优化，这些程序将被翻译成一种称为X语言的低级语言，将在该项目下开发，该语言将以紧凑的方式表示搜索空间以及使代码适应硬件的策略。Proteus将包含用于收集有关目标机器的信息的工具，用于静态或动态指导选择过程，用于实施X代码的域无关优化，以及用于生成作为最终输出的C代码。Proteus基础设施将使不同域的库生成器能够快速成型。将在四个不同领域产生的库本身将是有用和有趣的。此外，还将开发用于收集有关目标机器和计算环境的信息的工具，这些工具可供自己在其他项目中使用。