It has long been known that a fixed ordering of optimization phases will not produce the best code for every application. One approach for addressing this phase ordering problem is to use an ev olutionary algorithm to search for a specific sequence of phases for each module or function. While such searches have been shown to produce more efficient code, the approach can be extremely slow because the application is compiled and executed to evaluate each sequence's effectiveness. Consequently, evolutionary or iterative compilation schemes have been promoted for compilation systems targeting embedded applications where longer compilation times may be tolerated in the final stage of development. In this paper we describe two complementary general approaches for achieving faster searches for effective optimization sequences when using a genetic algorithm. The first approach reduces the search time by avoiding unnecessary executions of the application when possible. Results indicate search time reductions of 65% on average, often reducing searches from hours to minutes. The second approach modifies the search so fewer generations are required to achieve the same results. Measurements show that the average number of required generations decreased by 68%. These improvements have the potential for making evolutionary compilation a viable choice for tuning embedded applications.

New processors, both embedded and general purpose, often have conflicting design requirements involving space, power, and performance. Architectural features and compiler optimizations often target one or more design goals at the expense of the others. This paper presents a novel architectural and compiler approach to simultaneously reduce power requirements, decrease code size, and improve performance by integrating an instruction register file (IRF) into the architecture. Frequently occurring instructions are placed in the IRF. Multiple entries in the IRF can be referenced by a single packed instruction in ROM or L1 instruction cache. Unlike conventional code compression, our approach allows the frequent instructions to be referenced in arbitrary combinations. The experimental results show significant improvements in space and power, as well as some improvement in execution time when using only 32 entries. These advantages make packing instructions into registers an effective approach for improving overall efficiency.

It has long been known that a fixed ordering of optimization phases will not produce the best code for every application. One approach for addressing this phase-ordering problem is to use an evolutionary algorithm to search for a specific sequence of phases for each module or function. While such searches have been shown to produce more efficient code, the approach can be extremely slow because the application is compiled and possibly executed to evaluate each sequence's effectiveness. Consequently, evolutionary or iterative compilation schemes have been promoted for compilation systems targeting embedded applications where meeting strict constraints on execution time, code size, and power consumption is paramount and longer compilation times may be tolerated in the final stage of development, when an application is compiled one last time and embedded in a product. Unfortunately, even for small embedded applications, the search process can take many hours or even days making the approach less attractive to developers. In this paper, we describe two complementary general approaches for achieving faster searches for effective optimization sequences when using a genetic algorithm. The first approach reduces the search time by avoiding unnecessary executions of the application when possible. Results indicate search time reductions of 62%, on average, often reducing searches from hours to minutes. The second approach modifies the search so fewer generations are required to achieve the same results. Measurements show this approach decreases the average number of required generations by 59%. These improvements have the potential for making evolutionary compilation a viable choice for tuning embedded applications.

Abstract

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