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The use of pointers presents serious problems for software productivity tools for software understanding, restructuring, and testing. Pointers enable indirect memory accesses through pointer dereferences, as well as indirect procedure calls (e.g., through function pointers in C). Such indirect accesses and calls can be disambiguated with pointer analysis. In this paper we evaluate the precision of one specific pointer analysis (the FA pointer analysis by Zhang et al.) for the purposes of call graph construction for C programs with function pointers. The analysis is incorporated in a production-strength code-browsing tool from Siemens Corporate Research in which the program call graph is used as a primary tool for code understanding. The FA pointer analysis uses an inexpensive, almost-linear, flow- and context-insensitive algorithm. To measure analysis precision, we compare the call graph constructed by this analysis with the most precise call graph obtainable by a large category of existing pointer analyses. Surprisingly, for all our data programs the FA analysis achieves the best possible precision. This result indicates that for the purposes of call graph construction, inexpensive pointer analyses may provide precision comparable to the precision of expensive pointer analyses.

The frequent occurrence of implicitly thrown exceptions poses one of the challenges present in a Java compiler. Not only do these implicitly thrown exceptions directly affect the performance by requiring explicit checks, they also indirectly impact the performance by restricting code movement in order to satisfy the precise exception model in Java. In particular, instruction scheduling is one transformation that is restricted by implicitly thrown exceptions due to the heavy reliance on reordering instructions to exploit maximum hardware performance. The goal of this study is two-fold: first, investigate the degree to which implicitly thrown exceptions in Java hinder instruction scheduling, and second, find new techniques for allowing more efficient execution of Java programs containing implicitly thrown exceptions. Experimental results show that with aggressive scheduling techniques, such as superblock scheduling, the negative performance impact can be greatly reduced.

We present an effective approach to performing data flow analysis in parallel and identify three types of parallelism inherent in this solution process: independent-problem parallelism, separate-unit parallelism and algorithmic parallelism. We present our investigations of Fortran procedures from thePerfect Benchmarks andnetlib libraries, which reveal structural characteristics of program flow graphs that are amenable to algorithmic parallelism. Previously, the utility of algorithmic parallelism had been explored using our parallel hybrid algorithm in the context of solving the Reaching Definitions problem for Fortran procedures. Here we present new refinements that optimize performance by increasing the grain size of the parallelism, to improve communication on distributed-memory machines. The empirical performance of our optimized and unoptimized hybrid algorithms for Reaching Definitions are compared on this large data set using an iPSC/2. Our empirical findings qualitatively validate the usefulness of algorithmic parallelism.

We have designed a family of parallel data flow analysis algorithms for execution on distributed-memory MIMD machines, based on general-purpose, hybrid algorithms for data flow analysis [Marlowe and Ryder 1990]. We exploit a natural partitioning of the hybrid algorithms and explore a static mapping, dynamic scheduling strategy. Alternative mapping-scheduling choices and refinements of the flow graph condensation used are discussed. Our parallel hybrid algorithm family is illustrated on Reaching Definitions, although parallel algorithms also exist for many interprocedural (e.g., Aliasing) and intraprocedural (e.g., Available Expressions) problems [Marlowe 1989]. We have implemented the parallel hybrid algorithm for Reaching Definitions on an Intel iPSC/2. Our empirical results suggest the practicality of parallel hybrid algorithms.

A comprehensive overview of data flow frameworks and their characterizing properties is presented, to clarify property definitions and demonstrate their interrelation. Properties ensuring the existence of a solution are differentiated from those guaranteeing particular convergence behavior for specific solution procedures. Examples illustrate the orthogonality of these precision and convergence properties. In addition, several data flow problems are categorized with respect to these properties.