SHF: Small: Optimizing Compiler and Runtime for Concurrency-Oriented Execution Model

“暗硅”效应，即在每一代晶体管缩小时，越来越多的核心将不得不保持断电（或“暗”），这使得通过半导体技术的缩放来维持进一步的效率提高变得困难。然而，应用程序及其数据对存储和处理能力的需求正在迅速增长，从而增加了系统堆栈效率与现代应用程序需求之间的差距。该研究项目旨在基于一种结合了吞吐量处理架构和以并发为中心的编译框架的新范式重新设计系统堆栈。本研究项目中使用的系统堆栈由专门用于吞吐量的体系结构组成，该体系结构将单线程指令级并行（ILP）开发单元转换为吞吐量单元。该编译器专门用于并发，通过交错执行大量并发线程来最小化单线程延迟。这个研究项目揭示了并发执行对吞吐量处理器的影响，以及这些影响如何影响编译时决策和相应的运行时优化。智力上的优点有两方面：1)它揭示了现有的主流CPU编译技术是并发无关的，这使得许多具有挑战性的问题没有得到解决，也留下了许多性能改进的机会；2)它通过解决编译时决策的资源分配和指令/线程调度方面的问题来解决这些问题，这是并发执行模型与传统CPU执行模型之间的根本区别所在。该项目的更广泛影响是，研究成果将推动商业、教育和计算应用的创新，通过重新发明系统堆栈来提高效率，并帮助实现下一个超级计算里程碑，即百亿亿次计算。

Title: SHF:Small:Optimizing Compiler and Runtime for Concurrency-Oriented Execution ModelThe "dark silicon" effect, where an increasing fraction of cores will have to be kept powered off (or, "dark"), at every generation of transistor downsizing, has made it difficult to sustain further efficiency gains via the scaling of semiconductor technology. However, the demands of applications and their data on storage and processing capabilities are rapidly growing, thus increasing the gap between the efficiency of the system stack and the needs of modern applications. This research project aims to redesign the system stack based on a novel paradigm that combines throughput-processing architecture and a concurrency-centric compilation framework. The system stack used in this research project consists of architecture specialized for throughput, which trades single-thread instruction level parallelism (ILP) exploitation units for throughput units. The compiler is specialized for concurrency, which minimizes single thread latency by interleaved execution of a tremendous number of concurrent threads.This research project reveals the implications of concurrent execution on throughput processors and how these implications affect compile-time decisions and the corresponding runtime optimization. The intellectual merits are two-fold: 1) it reveals that the existing mainstream CPU compilation techniques are concurrency-oblivious, which leaves both many challenging problems unanswered and many opportunities for performance improvement to be explored, and 2) it tackles these problems by addressing both the resource allocation and instruction/thread scheduling aspects of compile-time decision making, which is where the fundamental difference between the concurrent execution model and the traditional CPU execution model arises. The broader impacts of this project are that the research results will drive innovation in business, education, and computing applications by reinventing the system stack to enhance efficiency and to help achieve the next supercomputing milestone, namely, exascale-computing.