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来自先前编译的分析和代码来减少重新编译工作。由于复杂性，这些编译器通常将与机器无关的优化和寄存器分配策略限制为增量粒度单位；一个用于粗粒度的模块和一个用于细粒度的语句。该项目涉及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中进行优化和寄存器分配。将设计、实施和实验评估细粒、粗粒和粒度组合的技术。将调查增量式系统的开销、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问题。这项研究最初将利用优化的概念，并记录以前为细粒技术开发的历史。这项工作的一个重要方面将是调查增量数据流技术的性能，这种技术能够增量地更新程序内和程序间的信息。数据流调查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来增加并行度而不断更新。

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.