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: 1533846
• 负责人: David Whalley
• 金额: $ 31.5万
• 依托单位: Florida State 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=1533846&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 we 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.

职务名称：XPS：全部：FP：合作研究：斯芬克斯：通过命令式程序的需求驱动执行将数据和指令级并行性结合起来提高处理器的性能以使其能够满足现有和新兴工作负载的需求已经变得越来越困难。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，这些处理器仍然难以编程，并且不灵活地适应给定程序中可用并行性的动态变化。尽管计算机体系结构和编程语言社区继续创新并朝着更好的可编程性和更好的设计取得重要进展，但并行编程本质上是昂贵的且容易出错，并且程序的自动并行化并不总是可行或有效的。这个项目的智力优势是开发一个新的程序执行范例和建立关键的编译器和微体系结构机制，使我们可以设计处理器，可以很容易地使用现有的编程语言编程，并在同一时间超过现有的并行计算机的性能。该项目的广泛意义和重要性是广泛传播的：这种处理器的部署将推动计算在科学和商业的每一个领域的极限。正在考虑的执行范式是一个以前未探索的执行模型，需求驱动的执行命令式程序（DDE）。DDE范例依赖于一个坚实的理论框架，并承诺有效地提供非常高级别的细粒度并行。这种并行性是从一个程序编写的命令式语言，如C，它是通过一个有效的编译器架构的协作机制，使用一个共同的，单一的分配形式的程序表示来实现。DDE处理器可以更有效地提取并行级比现有的超标量处理器的范例不需要动态依赖检查。这样的处理器可以获取，缓冲，并执行更多的指令比目前的超标量处理器并行。由于其依赖性驱动的指令获取和执行，该范例导致极其可扩展的设计，因为通信是自然本地化的，并且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架构代表了极端规模机器的有前途的构建模块。

项目成果

The Domestic Computer Science Graduate Students Are There, We Just Need to Recruit Them

国内计算机研究生已经有了，我们只需要招就可以了

• DOI: 10.1145/3448249
• 发表时间: 2021
• 期刊: Communications of the ACM
• 影响因子: 22.7
• 作者: Whalley, David;Yuan, Xin;Liu, Xiuwen
• 通讯作者: Liu, Xiuwen

Decreasing the Miss Rate and Eliminating the Performance Penalty of a Data Filter Cache

降低丢失率并消除数据过滤器缓存的性能损失

• 发表时间: 2021
• 期刊: ACM transactions on architecture and code optimization
• 影响因子: 1.6
• 作者: Stokes, Michael;Whalley, David;Onder, Soner
• 通讯作者: Onder, Soner

Improving Energy Efficiency by Memoizing Data Access Information

• DOI: 10.1109/islped.2019.8824951
• 发表时间: 2019-07
• 期刊: 2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)
• 作者: Michael Stokes;Ryan Baird;Zhaoxiang Jin;D. Whalley;Soner Önder