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Optimizing compilers (particularly parallel compilers) are constrained by their ability to predict performance consequences of the transformations they apply. Many factors, such as unknowns in control structures, dynamic behavior of programs, and complexity of the underlying hardware, make it very difficult for compilers to estimate the performance of the transformations accurately and efficiently. In this paper, we present a performance prediction framework that combines several innovative approaches to solve this problem. First, the framework employs a detailed, architecture-specific, but portable, cost model that can be used to estimate the cost of straight line code efficiently. Second, aggregated costs of loops and conditional statements are computed and represented symbolically. This avoids unnecessary, premature guesses and preserves the precision of the prediction. Third, symbolic comparison allows compilers to choose the best transformation dynamically and systematically. Some methodologies for applying the framework to optimizing parallel compilers to support automatic, performance-guided program restructuring are discussed.

Precise compile-time performance prediction for superscalar-based computers