Collaborative Research: SHF: Medium: Collaborative Automatic Parallelization

在摩尔定律终结的背景下，多核的最大价值最终在于其加速顺序代码的潜力。这种潜力只能通过可靠地从程序中提取足够的多核适当的线程级并行性（MATLP）来实现。然而，尽管有许多针对多核设计的新工具、语言和库，MATLP提取中的困难仍然使多核未得到充分利用。这对能源和性能的影响几乎是普遍的。为了解决这个问题，这个项目的新颖之处在于(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提取所需的多种代码分析和转换，（ii）产生RAPPORT，第一个具有完全协作支持的公开可用编译器，这是强大的自动并行化的必要元素。这个项目的影响是通过可靠的MATLP提取使计算更快、更高效。在传统的编译器中，优化可以很好地独立地执行，从而实现简单的编译器模块化，而不会对性能产生太大影响。然而，在MATLP提取中，关键并行化技术只有在其他转换清除路径（有时通过反优化代码）的情况下才可能成功。在过去的十年中，研究人员通过新的MATLP并行化模式、更强的记忆分析和更有效的推测技术，朝着鲁棒和常规自动MATLP的目标取得了稳步进展。该小组认为，这些MATLP技术是足够的，但缺乏充分发挥其潜力所需的协调。这项工作产生了可靠的MATLP提取所必需的技术，通过重新定义编译器抽象，使转换和分析能够在不丢失模块化的情况下积极地协同工作。这种新技术通过以模块化的方式将传统编译器中分散和贪婪的决策集中起来，从而实现全局有益的行为。这样，就可以可靠、鲁棒地提取MATLP。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.