Program Generation for Parallel Platforms

并行平台的程序生成

• 批准号: 0702386
• 负责人: Franz Franchetti
• 金额: --
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0702386&HistoricalAwards=false
• 关键词: Program; Generation; Parallel; Platforms

The clock speed of microprocessors has finally reached its practical limits. Future performance gains will only be obtained through various forms of parallelism such as integrating multiple CPU cores on one chip: The area of mainstream parallelism has started. This will pose an enormous burden on the developers of high performance libraries. Optimal code has to be carefully tuned to every specific platform including its memory hierarchy, special instruction sets, and the forms of parallelism it provides. This time-consuming process is repeated for every new platform released. It is time to ask the question: Can computers write these libraries for us?The goal of this research is to develop a program generation system that completelyautomates the implementation and optimization of a large class of performance-critical library functionality. This class will at least include linear transforms, a set of dense linear algebra problems, correlation, a set of decoders, and numerical integration. The program generation system will produce code that is optimized to a computer's memory hierarchy and that is parallelized, if required, for vector architectures, shared or distributed memory parallelism, or even streaming parallelism in graphics processing units (GPUs), or a for a combination of those. The performance of the generated code should be competitive with the best hand-written code available. "Program generation" means that the system takes as input only the problem specification. In other words, the computer itself writes highly optimized and, if desired, already parallelized source code. To achieve this, the knowledge about alternative algorithms and about algorithm optimization has to be formalized in a way that it can be done by the computer. In summary, the goal is to enable computers to write very fast libraries for well-understood numerical functionality and for a wide range of parallel platforms.

微处理器的时钟速度终于达到了实际限制。未来的绩效增长只能通过各种形式的并行性获得，例如将多个CPU核心集成在一个芯片上：主流平行性的区域已经开始。这将为高性能图书馆的开发商带来巨大的负担。最佳代码必须仔细调整到每个特定平台，包括其内存层次结构，特殊说明集以及所提供的并行性形式。为每个发布的新平台重复此耗时的过程。现在该提出一个问题了：计算机可以为我们编写这些图书馆吗？这项研究的目的是开发一个程序生成系统，该系统完全可以使大量绩效至关重要的库库功能的实现和优化。该类将至少包括线性变换，一组密集的线性代数问题，相关性，一组解码器和数值集成。程序生成系统将生成针对计算机内存层次结构进行优化的代码，并在需要的情况下（如果需要）（如果需要），对于矢量体系结构，共享或分布式存储器并行性，甚至是图形处理单元（GPU）中的流媒体并行性，或用于组合这些组合。生成的代码的性能应具有最佳的手写代码竞争性。 “程序生成”是指系统仅将问题规范作为输入。换句话说，计算机本身会编写高度优化的，如果需要的话，已经平行的源代码。为了实现这一目标，必须以计算机可以完成有关替代算法和算法优化的知识。总而言之，目标是使计算机能够为众所周知的数字功能和各种并行平台编写非常快速的库。