Collaborative Research: SHF: Medium: Collaborative Automatic Parallelization

协作研究：SHF：中：协作自动并行化

• 批准号: 2107257
• 负责人: David August
• 金额: $ 60万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107257&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Automatic

In the context of the end of Moore's law, the greatest value of multicore is ultimately in its potential to accelerate sequential codes. This potential can only be realized with the reliable extraction of sufficient multicore-appropriate thread-level parallelism (MATLP) from programs. Yet, despite many new tools, languages, and libraries designed for multicore, difficulties in MATLP extraction keep multicore grossly underutilized. The energy and performance impact of this is nearly universal. To address this problem, this project's novelties are in (i) redefining traditional abstractions used within compilers to enable constructive and tight collaborations that aim to coordinate the multiple code analyses and transformations required for MATLP extraction, (ii) producing RAPPORT, the first publicly available compiler with full collaboration support, a necessary element for robust automatic parallelization. This project's impact is in making computing faster and more efficient with reliable MATLP extraction.In conventional compilers, optimizations perform well greedily and independently, enabling easy compiler modularity without much performance impact. However, in MATLP extraction, key parallelization techniques may succeed only if other transformations clear the path, sometimes by de-optimizing the code. Over the last decade, researchers have made steady progress toward the goal of robust and routine automatic MATLP with new MATLP parallelization patterns, stronger memory analyses, and more efficient speculation techniques. This team believes these MATLP technologies are sufficient but lack the coordination necessary to realize their full potential. This work produces the technology necessary for reliable MATLP extraction by redefining compiler abstractions to enable transformations and analyses to work together actively without loss of modularity. This new technology enables a globally beneficial behavior by centralizing, in a modular way, the decentralized and greedy decision-making found in conventional compilers. In this way, it makes the reliable and robust extraction of MATLP possible.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在摩尔定律终结的背景下，多核的最大价值最终是其加速顺序代码的潜力。 只有从程序中可靠地提取足够的多重螺纹级并行性（MATLP），才能实现这种潜力。 然而，尽管设计了许多用于多功能的新工具，语言和库，但MATLP提取的困难仍使多项核心详尽地充分利用。 这种能量和性能的影响几乎是普遍的。 为了解决这个问题，该项目的新颖性在（i）重新定义编译器中使用的传统抽象，以实现建设性和紧密的协作，旨在协调MATLP提取所需的多个代码分析和转换，（ii）产生Rapport，生成Rapport，这是首个公开可用的编译器，这是首个与完整的合作支持，可鲁棒的自动配置的元素，这是首个公开可用的编译器。 该项目的影响在于，通过可靠的MATLP提取器使计算更快，更有效。在常规的编译器中，优化性能很好地贪婪，独立性，从而实现了简单的编译器模块化，而没有太大的性能影响。 但是，在MATLP提取中，仅当其他转换清除路径，有时通过降低代码来清除路径时，关键的并行技术才能成功。 在过去的十年中，研究人员以新的MATLP并行化模式，更强大的记忆分析和更有效的推测技术的稳健和常规自动MATLP取得了稳定的进步。该团队认为这些MATLP技术足够，但缺乏实现其全部潜力所需的协调。 这项工作通过重新定义编译器抽象来使转换和分析能够积极合作而不会丧失模块化，从而产生了可靠的MATLP提取所需的技术。 这项新技术通过以模块化的方式集中了在常规编译器中发现的分散和贪婪的决策来实现全球有益的行为。 通过这种方式，它使MATLP的可靠且强大的提取成为可能。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的审查标准来评估值得通过评估。

项目成果

Safer at any speed: automatic context-aware safety enhancement for Rust

在任何速度下都更安全：Rust 的自动上下文感知安全增强

• DOI: 10.1145/3485480
• 发表时间: 2021
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Popescu, Natalie;Xu, Ziyang;Apostolakis, Sotiris;August, David I.;Levy, Amit
• 通讯作者: Levy, Amit

EMISSARY: Enhanced Miss Awareness Replacement Policy for L2 Instruction Caching

• DOI: 10.1145/3579371.3589097
• 发表时间: 2023-06
• 期刊: Proceedings of the 50th Annual International Symposium on Computer Architecture
• 作者: N. P. Nagendra;Bhargav Reddy Godala;Ishita Chaturvedi;Atmn Patel;Svilen Kanev;Tipp Moseley;Jared Stark;Gilles A. Pokam;Simone Campanoni;David I. August

NOELLE Offers Empowering LLVM Extensions

NOELLE 提供强大的 LLVM 扩展

• DOI: 10.1109/cgo53902.2022.9741276
• 发表时间: 2022
• 期刊: 2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO
• 作者: Matni, Angelo;Deiana, Enrico Armenio;Su, Yian;Gross, Lukas;Ghosh, Souradip;Apostolakis, Sotiris;Xu, Ziyang;Tan, Zujun;Chaturvedi, Ishita;Homerding, Brian
• 通讯作者: Homerding, Brian