NGS: Collaborative Research: Adapting Program Code Continuously and Aggressively

在程序优化方面，过去和最近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，编译器研究社区已经开发出复杂而强大的优化算法，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。虽然对于特定的优化仍然有很有前途的研究途径，但我们现在所处的位置是，新的或改进的优化算法的性能增益通常很小。几个百分点的改善是典型的。今天好吗?优化研究面临的挑战是开发新的技术和方法，从而产生超越今天的性能改进。这是个位数的小改进。在本研究中，我们通过调查和开发一个创新的框架和系统来解决这一挑战，以实现持续和自适应的应用优化。我们的系统，连续编译器（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。与其专注于开发新的优化算法（例如，新的寄存器分配算法，新的循环交换算法）或改进现有的优化算法（例如，更好的着色启发式，更好的放置算法），拟议的研究侧重于了解现有优化的相互作用以及静态和动态优化的功效。使用这些知识以及通过静态分析、概要信息和监视收集到的有关应用程序的信息，CoCo将确定如何应用一套优化，以便优化协同工作以产生最佳改进。

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.