Fine and Coarse Grain Incremental Compilation of Optimized Code

编程环境中的增量编译器试图通过重用以前编译中的分析和代码来减少重新编译的工作量。由于复杂性，这些编译器通常将机器无关的优化和寄存器分配策略限制在粒度的增量单位上；粗粒度的模块和细粒度的语句。该项目解决了粗粒度和细粒度增量编译技术的设计和性能，这些技术允许在增量单元之间进行优化和寄存器分配。将设计、实施和实验评估细颗粒、粗颗粒和组合颗粒的表演技术。将研究诸如增量系统的开销、适当的粒度单位以及维护的信息量与重新编译检测过程的准确性之间的权衡等问题。该研究最初将利用优化的概念和先前为细颗粒技术开发的注册历史。这项工作的一个重要方面将是调查能够逐步更新程序内和程序间信息的增量数据流技术的性能。数据流研究的结果对并行体系结构的编译技术也很重要，因为数据流必须随着代码的转换而不断更新，以增加并行性。

Incremental compilers in programming environments attempt to reduce the recompilation effort by reusing analysis and code from a previous compilation. Because of the complexity, these compilers typically limit the machine independent optimizations and register allocation strategies to the incremental unit of granularity; a module for coarse grain and a statement for fine grain. This project addresses the design and performance of both coarse and fine grain incremental compiling techniques that permit optimizations and register allocation across the incremental unit. Techniques for performing fine grain, coarse grain and a combination of the granularities will be designed, implemented and experimentally evaluated. Issues such as the overhead of incremental systems, the appropriate granularity unit and the trade-off between the amount of information maintained and the preciseness of the recompilation detection process will be investigated. The research will initially utilize the notion of optimization and register histories previously developed for fine grain techniques. An important aspect of this work will be the investigation into the performance of incremental data flow techniques which are capable of incrementally updating both intraprocedural and interprocedural information. The results on the data flow investigation are also important to compiling techniques for parallel architectures, for data flow must be continually updated as code is transformed to increase parallelism.