Collaborative Research: SHF: Medium: Collaborative Automatic Parallelization

在摩尔定律结束的背景下，多核的最大价值最终在于其加速顺序代码的潜力。 这种潜力只能通过从程序中可靠地提取足够的多核线程级并行（MATLP）来实现。 然而，尽管有许多新的工具、语言和库是为多核设计的，但MATLP提取的困难使多核的利用率严重不足。 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）重新定义了编译器中使用的传统抽象，以实现建设性的和紧密的协作，旨在协调MATLP提取所需的多个代码分析和转换，（ii）生产RAPPORT，第一个公开可用的编译器，具有完全的协作支持，是强大的自动并行化的必要元素。 该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，优化可以独立地执行，并且可以轻松实现编译器模块化，而不会对性能产生太大影响。 然而，在MATLP提取中，只有当其他转换清除路径时，关键的并行化技术才可能成功，有时是通过对代码进行反优化。 在过去的十年中，研究人员已经取得了稳步的进展，朝着强大的和例行的自动MATLP的目标与新的MATLP并行模式，更强大的内存分析，更有效的投机技术。该团队认为这些MATLP技术已经足够，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提取所需的技术，通过重新定义编译器抽象，使转换和分析，积极地一起工作，而不损失模块化。 这种新技术通过以模块化的方式集中传统编译器中的分散和贪婪决策，实现了全局有益的行为。 通过这种方式，它使可靠且强大的MATLP提取成为可能。该奖项反映了NSF的法定使命，并且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.