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

职务名称：SHF：小型：优化面向并发执行模型的并行处理器和并行处理器"暗硅"效应，即在每一代晶体管缩小尺寸时，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快速增长，从而增加了系统堆栈的效率与现代应用程序的需求之间的差距。 本研究计画的目的是重新设计系统堆栈的基础上，结合了吞吐量处理架构和并发为中心的编译框架的新范式。 在这个研究项目中使用的系统堆栈由专门的吞吐量，交易单线程指令级并行（ILP）开发单位的吞吐量单位的体系结构。 该编译器是专门为并发，它最大限度地减少了单线程延迟交错执行大量的并发线程。本研究项目揭示了并发执行吞吐量处理器的影响，以及这些影响如何影响编译时的决策和相应的运行时优化。知识价值是双重的：1）它揭示了现有的主流CPU编译技术是并发无关的，这留下了许多挑战性的问题未被回答，并且留下了许多有待探索的性能改进的机会，以及2）它通过解决编译时决策的资源分配和指令/线程调度方面来解决这些问题，这就是并发执行模型和传统CPU执行模型之间的根本区别所在。该项目更广泛的影响是，研究成果将通过重新发明系统堆栈来推动商业，教育和计算应用的创新，以提高效率，并帮助实现下一个超级计算里程碑，即exascale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.