Abstract

Modern compilers generate good code by performing global optimizations. Unlike other functions of the compiler such as parsing and code generation which examine only one statement or one basic block at a time, optimizers examine large parts of a program and coordinate changes in widely separated parts of a program. Thus optimizers use more complex data structures and consume more time. To generate the best code, optimizers perform not one global transformation, but many in concert. These transformations can interact in unforeseen ways. This dissertation concerns the building of optimizers that are modular and extensible. It espouses an optimizer architecture, first proposed by Kildall, in which each phase is based on a data-flow analysis (DFA) of the program and on an optimization function that transforms the program. To support the architecture, a set of abstractions---flow values, flow functions, path simplification rules, action routines---is provided. A tool called Sharlit turns a DFA specification consisting of these abstractions into a solver for a DFA problem. At the heart of Sharlit is an algorithm called path simplification, an extension of Tarjan's fast path algorithm. Path simplification unifies several powerful DFA solution techniques. By using path simplification rules, compiler writers can construct a wide range of data-flow analyzers, from simple iterative ones, to solvers that use local analysis, interval analysis, or sparse data-flow evaluation. Sharlit frees compiler writers from the details of how these various solution techniques. The compiler writer can view the program representation as a simple flow graph in which each instruction is a node. Data structures to represent basic blocks and other regions are automatically generated. Sharlit promotes ...

Citations