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

今天，两种趋势共同减缓了科学、工程和学术研究进展的步伐。首先，研究人员越来越依赖于计算来处理越来越大的数据集，并执行越来越多的计算密集型模拟。其次，单个处理器的速度不再像以前那样随着每一代计算机芯片的出现而增加。为了弥补这一点，处理器制造商已经采取行动，在每一代芯片上都包含更多的处理器或核心。为了在这些多核芯片上获得最佳性能，必须实施软件，使其能够并行执行，从而使用额外的处理器核心。不幸的是，使用当今的软件开发工具编写高效、显式并行的软件程序需要计算机科学方面的高级培训，即使有这样的培训，任务仍然非常困难、容易出错和耗时。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)，旨在将现代多核机器的性能承诺带给科学家和工程师，而不需要教这些用户如何编写显式并行程序的相关成本。在短期内，这项研究将为几个科学领域的研究人员提供直接和直接的好处，因为PI将把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获得性能的方式，提高他们的生产率，并加快整个科学的步伐。这项工作还将产生重大的教育影响，开发所有科学家和工程师都可以使用的关于显式和隐式并行计算主题的课件和教程材料。IPP的运作方式是允许用户编写普通的顺序程序，然后用扩展(或抽象)顺序程序行为集的逻辑规范来扩充它们。这种抽象能力将为并行化编译器提供比其他方式更积极地优化程序的灵活性。事实上，它将使以前无法实现的有效并行化技术成为可能。语言设计和编译器实现将伴随着形式化的语义分析，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，并指导静态分析和程序缺陷检测算法的创建。此外，由于现有程序和语言可以被视为(退化的)隐式并行，因此保留了数十年在人类专业知识、语言、编译器、方法、工具和应用程序方面的投资。特别是，可以从缓慢的顺序版本升级旧的遗留程序或库，而不需要彻底检查整个系统架构，而只需添加一些辅助规范。编译器技术将帮助指导科学家和工程师完成这一过程，进一步简化任务。从概念上讲，IPP恢复了重要的抽象层，使程序员可以编写易于理解的高级代码，而不是根据特定并行机的特定需求构建的低级代码。

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.