XPS: Full: FP: Collaborative Research: Sphinx: Combining Data and Instruction Level Parallelism through Demand Driven Execution of Imperative Programs

XPS：完整：FP：协作研究：Sphinx：通过命令式程序的需求驱动执行将数据和指令级并行性相结合

• 批准号: 1533828
• 负责人: Soner Onder
• 金额: $ 56万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533828&HistoricalAwards=false
• 关键词: XPS; Full; FP; Collaborative; Research

Title: XPS: Full: FP: Collaborative Research: Sphinx: Combining Data and Instruction Level Parallelism through Demand Driven Execution of Imperative ProgramsIt has become increasingly difficult to improve the performance of processors so that they can meet the demands of existing and emerging workloads. Recent emphasis has been towards enhancing the performance through the use of multi-core processors and Graphics Processing Units. However, these processors remain difficult to program and inflexible to adapt to dynamic changes in the available parallelism in a given program. Although the computer architecture and programming language community continues to innovate and make important gains towards better programmability and better designs, it remains that parallel programming is inherently costly and error prone, and automatic parallelization of programs is not always feasible or effective. The intellectual merits of this project are the development of a new program execution paradigm and the establishment of critical compiler and micro-architecture mechanisms so that one can design processors that can be easily programmed using existing programming languages and at the same time surpass the performance of existing parallel computers. The project's broader significance and importance are wide-spread: the deployment of such processors will push the limits of computation in every field of science and commerce.The execution paradigm under consideration is a previously unexplored execution model, the demand-driven execution of imperative programs (DDE). The DDE paradigm rests on a solid theoretical framework and promises to efficiently deliver very high-levels of fine-grain parallelism. This parallelism is extracted from a program written in an imperative language such as C, and it is realized by means of an effective compiler-architecture collaboration mechanism using a common, single-assignment form for the program representation. DDE processors can extract instruction-level parallelism much more efficiently than existing superscalar processors as the paradigm does not require dynamic dependency checking. Such processors can fetch, buffer, and execute many more instructions in parallel than current superscalar processors. Owing to its dependence-driven instruction fetching and execution, the paradigm leads to extremely scalable designs, as the communication is naturally localized and synchronization is inherent in the model. Conventional thread-level parallelism (TLP) is orthogonal to DDE, and thus DDE designs can exploit both ILP and TLP. DDE architectures thus represent promising building blocks for extreme-scale machines.

标题：XPS：Full：FP：Collaborative Research：Sphinx：通过需求驱动的命令性程序执行将数据和指令级并行结合在一起越来越难提高处理器的性能，使其能够满足现有和新兴工作负载的需求。最近的重点是通过使用多核处理器和图形处理单元来提高性能。然而，这些处理器仍然很难编程，并且不能灵活地适应给定程序中可用并行度的动态变化。尽管计算机体系结构和编程语言社区不断创新，并在更好的可编程性和更好的设计方面取得了重要进展，但并行编程本身就很昂贵，容易出错，程序的自动并行化并不总是可行或有效的。该项目的智力优势是开发了一种新的程序执行范例，建立了关键的编译器和微体系结构机制，以便能够设计出可以使用现有编程语言轻松编程的处理器，同时性能超过现有并行计算机。该项目更广泛的意义和重要性是广泛的：这种处理器的部署将推动计算在科学和商业的各个领域的极限。正在考虑的执行范例是一个以前没有探索过的执行模型，需求驱动的命令式程序执行(DDE)。DDE范例建立在坚实的理论框架之上，并有望有效地提供非常高级别的细粒度并行性。这种并行性是从用命令式语言(如C)编写的程序中提取出来的，它是通过一种有效的编译器-体系结构协作机制来实现的，该机制使用通用的单赋值形式来表示程序。DDE处理器可以比现有的超标量处理器更有效地提取指令级并行性，因为该范例不需要动态依赖检查。这种处理器可以并行地获取、缓冲和执行比当前超标量处理器多得多的指令。由于其依赖驱动的指令获取和执行，该范例导致了极具可伸缩性的设计，因为通信自然是本地化的，同步是模型中固有的。传统的线程级并行(TLP)与DDE是正交的，因此DDE设计可以同时利用ILP和TLP。因此，DDE体系结构代表了极具潜力的计算机构建块。