SI2-SSI: Accelerating the Pace of Research through Implicitly Parallel Programming

SI2-SSI：通过隐式并行编程加快研究步伐

• 批准号: 1047879
• 负责人: David August
• 金额: $ 174.02万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1047879&HistoricalAwards=false
• 关键词: SI2; SSI; Accelerating; Pace; Research

Today, two trends conspire to slow down the pace of science, engineering, and academic research progress in general. First, researchers increasingly rely on computation to process ever larger data sets and to perform ever more computationally-intensive simulations. Second, individual processor speeds are no longer increasing with every computer chip generation as they once were. To compensate, processor manufacturers have moved to including more processors, or cores, on a chip with each generation. To obtain peak performance on these multicore chips, software must be implemented so that it can execute in parallel and thereby use the additional processor cores. Unfortunately, writing efficient, explicitly parallel software programs using today's software-development tools takes advanced training in computer science, and even with such training, the task remains extremely difficult, error-prone, and time consuming. This project will create a new high-level programming platform, called Implicit Parallel Programming (IPP), designed to bring the performance promises of modern multicore machines to scientists and engineers without the costs associated with having to teach these users how to write explicitly parallel programs. In the short term, this research will provide direct and immediate benefit to researchers in several areas of science as the PIs will pair computer science graduate students with non-computer science graduate students to study, analyze, and develop high-value scientific applications. In the long term, this research has the potential to fundamentally change the way scientists obtain performance from parallel machines, improve their productivity, and accelerate the overall pace of science. This work will also have major educational impact by developing courseware and tutorial materials, useable by all scientists and engineers, on the topics of explicit and implicit parallel computing.IPP will operate by allowing users to write ordinary sequential programs and then to augment them with logical specifications that expand (or abstract) the set of sequential program behaviors. This capacity for abstraction will provide parallelizing compilers with the flexibility to more aggressively optimize programs than would otherwise be possible. In fact, it will enable effective parallelization techniques where they were impossible before. The language design and compiler implementation will be accompanied by formal semantic analysis that will be used to judge the correctness of compiler transformations, provide a foundation for about reasoning programs, and guide the creation of static analysis and program defect detection algorithms. Moreover since existing programs and languages can be viewed as (degenerately) implicitly parallel, decades of investment in human expertise, languages, compilers, methods, tools, and applications is preserved. In particular, it will be possible to upgrade old legacy programs or libraries from slow sequential versions without overhauling the entire system architecture, but merely by adding a few auxiliary specifications. Compiler technology will help guide scientists and engineers through this process, further simplifying the task. Conceptually, IPP restores an important layer of abstraction, freeing programmers to write high-level code, designed to be easy to understand, rather than low-level code, architected according to the specific demands of a particular parallel machine.

如今，两种趋势共同降低了科学，工程和学术研究进步的速度。 首先，研究人员越来越依靠计算来处理越来越大的数据集并执行更多计算密集的模拟。 其次，单个处理器的速度不再像以前一样随着每次计算机芯片的生成而增加。 为了补偿，处理器制造商已将每一代人的芯片置于更多的处理器或核心。 为了在这些多层芯片上获得峰值性能，必须实现软件，以便可以并行执行，从而使用其他处理器内核。不幸的是，使用当今的软件开发工具编写有效的，明确的并行软件程序，也需要计算机科学领域的高级培训，即使经过此类培训，该任务仍然非常困难，容易出错且耗时。 该项目将创建一个新的高级编程平台，称为隐式并行编程（IPP），旨在将现代多功能机器的绩效承诺带给科学家和工程师，而无需与必须教这些用户如何显式编写明确并行程序相关的成本。 在短期内，这项研究将为科学的多个领域的研究人员提供直接和直接的好处，因为PIS将计算机科学研究生与非计算机科学研究生配对，以研究，分析和开发高价值的科学应用。 从长远来看，这项研究有可能从根本上改变科学家从并行机器获得绩效，提高其生产率并加速科学的整体速度。 这项工作还将通过开发所有科学家和工程师，对明确和隐式并行计算主题使用的课程和教程材料来产生重大的教育影响。IPP将通过允许用户编写普通的顺序程序，然后通过扩展（或摘要）顺序程序行为的逻辑规范来增强它们。 这种抽象的能力将为并行的编译器提供比其他可能的灵活性优化程序的灵活性。 实际上，它将实现有效的并行化技术，而这些技术以前是不可能的。 语言设计和编译器实施将伴随正式的语义分析，该分析将用于判断编译器转换的正确性，为推理程序提供基础，并指导创建静态分析和程序缺陷检测算法。 此外，由于现有的程序和语言可以被视为（堕落）隐式平行，因此保留了数十年的人类专业知识，语言，编译器，方法，工具和应用程序的投资。 特别是，可以从缓慢的顺序版本中升级旧的旧程序或库，而不会大修整个系统体系结构，而仅通过添加一些辅助规范。 编译器技术将帮助指导科学家和工程师完成此过程，从而进一步简化任务。 从概念上讲，IPP恢复了一个重要的抽象层，使程序员释放了编写高级代码，该代码旨在易于理解，而不是根据特定并行机器的特定要求进行构建的低级代码。