NGS: Collaborative Research: Adapting Program Code Continuously and Adaptively

过去和最近在程序优化方面的许多研究都集中在开发新的算法来执行特定的优化或转换。事实上，在过去的十年中，编译器研究界已经开发出复杂的、强大的优化算法，用于各种代码改进：寄存器分配和赋值、公共子表达式消除、部分冗余消除、循环优化（例如，循环融合、循环展开、循环交换等），代码调度和函数内联等等。虽然仍然有一些有前途的研究，特别是优化的途径，我们在一个新的或改进的优化算法的性能增益通常是小的点？百分之几的改进是典型的。 今天吗？优化研究面临的挑战是开发新的技术和方法，以产生超越今天的性能改进？的小个位数的改进。在这项研究中，我们通过研究和开发一个创新的框架和系统来解决这一挑战，以持续和自适应地应用优化。我们的系统，连续优化器（CoCo），使用在编译时开发并在运行时调整的优化计划，在编译时静态地和在运行时动态地应用优化。新的寄存器分配算法，新的循环交换算法）或改进现有的优化（例如，更好的着色算法，更好的布局算法），所提出的研究重点是理解现有优化的相互作用以及静态和动态优化的有效性。 利用这些知识沿着通过静态分析、配置文件信息和监控收集的应用程序信息，CoCo将确定如何应用一套优化，以便优化协同工作，从而产生最佳改进。

Much of the past and recent research in program optimization has focused on developing new algorithms to perform a particular optimization or transformation. Indeed, over the previous decade the compiler research community has developed sophisticated, powerful optimization algorithms for a variety of code improvements: register allocation and assignment, common subexpression elimination, partial redundancy elimination, loop optimizations (e.g., loop fusion, loop unrolling, loop interchange, etc.), code scheduling, and function inlining to name a few. While there are still avenues of promising research for particular optimizations, we are at the point where the performance gains of a new or improved optimization algorithm is usually small?an improvement of a few percent is typical. Today?s challenge for optimization research is to develop new techniques and approaches that yield performance improvements that go beyond today?s small single digit improvements. In this research, we address this challenge by investigating and developing an innovative framework and system for continuously and adaptively applying optimizations. Our system, the Continuous Compiler (CoCo), applies optimizations both statically at compile-time and dynamically at run-time using optimization plans developed at compile time and adapted at run time.Rather than focusing on developing new optimization algorithms (e.g., a new register allocation algorithm, anew loop interchange algorithm) or improving existing optimizations (e.g., better coloring heuristics, better placement algorithms), the proposed research focuses on understanding the interaction of existing optimizations and the efficacy of static and dynamic optimizations. Using this knowledge along with information about the application gathered by static analysis, profile information and monitoring, CoCo will determine how to apply a suite of optimizations so that the optimizations work in concert to yield the best improvements.