集体调整倡议
Collective Tuning Initiative
摘要：
由于不断增加的硬件和软件复杂性以及当前优化技术的限制，计算系统很少提供最佳可能性能。通常需要额外的代码和架构优化来改善计算系统的执行时间、大小、功耗、可靠性和其他重要特性。然而，这往往是一个繁琐、重复、孤立和耗时的过程。为了使程序优化和架构设计自动化、简化和系统化，我们正在开发开源模块化的基于插件的集体调优基础设施（CTI，http://cTuning.org），它可以分布优化过程并利用多个用户的优化经验。CTI提供了一种全新的完全集成的协作式“一键”方法，可基于系统迭代编译、统计集体优化和机器学习来改进现有性能低下的计算系统，从嵌入式架构到高性能服务器。我们的实验结果表明，它是可能的，以减少执行时间（和代码大小）的一些程序，从SPEC2006和EEMBC等超过一个因素2自动。它还可以大大减少开发和测试时间。连同第一个生产质量的机器学习启用交互式研究编译器（MILEPOST GCC），这个基础设施开辟了许多研究机会，研究和开发未来现实的自调整和自组织自适应智能计算系统的基础上系统的统计性能评估和基准测试。最后，使用公共优化知识库旨在提高架构和代码优化研究的质量和可重复性。
英文摘要：
Computing systems rarely deliver best possible performance due to ever increasing hardware and software complexity and limitations of the current optimization technology. Additional code and architecture optimizations are often required to improve execution time, size, power consumption, reliability and other important characteristics of computing systems. However, it is often a tedious, repetitive, isolated and time consuming process. In order to automate, simplify and systematize program optimization and architecture design, we are developing open-source modular plugin-based Collective Tuning Infrastructure (CTI, http://cTuning.org) that can distribute optimization process and leverage optimization experience of multiple users. CTI provides a novel fully integrated, collaborative, "one button" approach to improve existing underperfoming computing systems ranging from embedded architectures to high-performance servers based on systematic iterative compilation, statistical collective optimization and machine learning. Our experimental results show that it is possible to reduce execution time (and code size) of some programs from SPEC2006 and EEMBC among others by more than a factor of 2 automatically. It can also reduce development and testing time considerably. Together with the first production quality machine learning enabled interactive research compiler (MILEPOST GCC) this infrastructure opens up many research opportunities to study and develop future realistic self-tuning and self-organizing adaptive intelligent computing systems based on systematic statistical performance evaluation and benchmarking. Finally, using common optimization repository is intended to improve the quality and reproducibility of the research on architecture and code optimization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
