Collaborative Research: CPA-CPL-T: An Effective Automatic Parallelization Framework for Multi-Core Architectures

合作研究：CPA-CPL-T：一种有效的多核架构自动并行化框架

• 批准号: 0811781
• 负责人: Ponnuswamy Sadayappan
• 金额: $ 50万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811781&HistoricalAwards=false
• 关键词: Collaborative; Research; CPA; CPL; Effective

The ubiquity of multi-core processors has brought parallel computing squarely into the mainstream. It is now essential to develop parallel implementations of a large number of existing sequential codes. The difficulty of programming these architectures to effectively tap the potential of multiple on-chip processing units is a significant challenge. Although there has been significant progress in compiler techniques towards automatic parallelization, the current state-of-practice leaves much to be desired. The pressing need for systematic, general, and effective theoretical foundations for such efforts is a major motivation for this project.This project will build on some very recent developments using polyhedral models showing great promise for developing effective automatic parallelization frameworks for multi-core architectures. With the polyhedral model, it is possible to reason about the correctness of complex loop transformations in a completely mathematical setting using powerful machinery from linear algebra and linear programming. This enables effective integrated transformation, and therefore can be the basis for developing a very powerful automatic parallelization framework that can target different multi-core platforms. The project will address a number of key issues that are very important in developing an automatic parallelization and data locality optimization framework that is effective over a range of user application codes: (i) model-driven search for determination of effective tile sizes and loop fusion choices; (ii) extended tiling approaches like overlapped/split tiles to enhance concurrency; (iii) automatic generation of parallel code for accelerators with multiple distinct address spaces; and (iv) development of an extensive benchmark suite for assessment of automatic parallelization systems.The developed software will be made publicly available.

多核处理器的普遍存在使平行计算直接进入主流。现在，开发大量现有顺序代码的并行实现至关重要。 编程这些架构以有效利用多个芯片加工单元的潜力的困难是一个重大挑战。尽管编译器技术方向自动并行化取得了重大进展，但当前的实践状态仍有很多不足之处。 对这种努力进行系统，一般和有效的理论基础的紧迫需求是该项目的主要动机。该项目将使用多面体模型的一些最新发展建立，这表明为开发有效的自动平行框架为多核体系结构提供了巨大的希望。使用多面体模型，可以使用线性代数和线性编程的功能强大的机械来理解完全数学设置中复杂环转换的正确性。 这可以实现有效的集成转换，因此可以成为开发非常强大的自动并行化框架的基础，该框架可以针对不同的多核平台。 该项目将解决许多关键问题，这些问题对于开发自动并行化和数据局部优化框架非常重要，该框架在一系列用户应用程序代码中有效：（i）模型驱动的搜索以确定有效的瓷砖尺寸和循环融合选择； （ii）诸如重叠/拆分瓷砖之类的延长瓷砖方法以增强并发性； （iii）具有多个不同地址空间的加速器的自动生成并行代码； （iv）开发一个广泛的基准套件，以评估自动并行化系统。将公开提供开发的软件。