Handling Global Constraints in Compiler Strategy

To achieve high-performance on processors featuring ILP, most compilers apply locally a set of heuristics. This leads to a potentially high-performance on separate code fragments. Unfortunately, most optimizations also increase code size, which may lead to a global net performance loss. In this paper, we propose a Global Constraints-Driven Strategy (GCDS) for guiding code optimization. When using GCDS, the final code optimization decision is taken according to global criteria rather than local criteria. For instance, such criteria might be performance, code size, instruction cache behavior, etc. The performancecode size trade-off is a particularly important problem for embedded systems. We show how GCDS can be used to master code size while optimizing performance