Research on Compilers that can Adaptively Adopt Optimizations

本研究的目标是编译中的自适应优化。今天，计算平台正在分化。它们包括奔腾和RISC的单处理器计算机、小规模SMP、大规模并行系统和异构GRID环境。它强烈要求我们可以用统一的编程模型编写程序，并且编译器可以负责获取。在每个平台上的最佳性能。因此，编译器中的优化器对良好的性能负有重大责任。然而，在当今复杂的计算机/网络/软件体系结构中，大量的分析对于提高性能是必要的。在本研究中，我们研究了(1)一个在绩效分析中具有准确性的绩效模型。具体来说，我们已经为有序和无序处理器建立了展开形状，这是一个循环展开的符号和定量性能模型。该模型能够准确地捕捉到循环展开过程中的CPU行为，如软件流水线效应和内存延迟隐藏。(2)根据数值数据结构的特点自适应选择算法。这种选择在安装时、编译时和运行时都有应用。我们观察到使用这种自适应算法选择可以提高性能。作为优化的两个基本要求，我们可以列出它们不改变程序语义和它们真正提高性能。作为优化验证编译器项目，我们正在努力保证这两个要求。事实上，我们在这项研究中研究了适应以提高表现，这可以成为实现这一目标的垫脚石。

This research aims at Adaptive Optimization in compilation. Today, computing platforms are diverging. They include uniprocessor computer of Pentium and RISC' s, small scale SMP, large scale parallel systems, and heterogeneous GRID environment. It is strongly required that we can make programs in uniform programming model, and that compilers can take care of getting. best performance on each platform. As its result, optimizers in a compiler is heavily responsible for good performance. However, in today's complexity of computer/network/software architecture, heavy analysis is necessary to improve performance. In this research, we have studied(1) a performance model that has accuracy in performance analysis. Specifically, we have built unrolling shape, a symbolic and quantitative performance model for loop unrolling, for both in-order and out-of-order processors. This model is proved to be accurate enough to capture CPU behaviors in loop unrolling such as effect of software pipelining and memory latency hiding.(2) adaptive selection of algorithms in accordance with characteristics of numerical data structure This selection is applied in installation time, compile time, and runtime. We observed performance improvement using this adaptive algorithm selection.As two essential requirements to optimizations, we can list that they do not change program semantics and that they truly improve performance. As Optimization Verifying Compiler Project, we are trying to guarantee the two requirements. The fact that we have studied adaptation for improving performance in this research can be a stepping stone to this end.