This paper reports on the design, implementation, and empirical results of a new method for dealing with the aliasing problem in C. The method is based on approximating the points-to relationships between accessible stack locations, and can be used to generate alias pairs, or used directly for other analyses and transformations. Our method provides context-sensitive interprocedural information based on analysis over invocation graphs that capture all calling contexts including recursive and mutually-recursive calling contexts. Furthermore, the method allows the smooth integration for handling general function pointers in C.

Optimizing compilers should produce efficient code even in the presence of high-level language constructs. However, current programming support systems are significantly lacking in their ability to analyze procedure calls. This deficiency complicates parallel programming, because loops with calls can be a significant source of parallelism. We describe an implementation of regular section analysis, which summarizes interprocedural side effects on subarrays in a form useful to dependence analysis while avoiding the complexity of prior solutions. The paper gives the results of experiments on the Linpack library and a small set of scientific codes.

The first interprocedural modification side-effects analysis for C (MOD_C) that obtains better than worst-case precision on programs with general-purpose pointer usage is presented with empirical results. The analysis consists of an algorithm schema corresponding to a family of MODC algorithms with two independent phases: one for determining pointer-induced aliases and a subsequent one for propagating interprocedural side effects. These MOD_C algorithms are parameterized by the aliasing method used. The empirical results compare the performance of two dissimilar MOD_C algorithms: MOD_C(FSAlias) uses a flow-sensitive, calling-context-sensitive interprocedural alias analysis [LR92]; MOD_C(FIAlias) uses a flow-insensitive, calling-context-insensitive alias analysis which is much faster, but less accurate. These two algorithms were profiled on 45 programs ranging in size from 250 to 30,000 lines of C code, and the results demonstrate dramatically the possible cost-precision tradeoffs. This first comparative implementation of MODC analyses offers insight into the differences between flow-/context-sensitive and flow-/context-insensitive analyses. The analysis cost versus precision tradeoffs in side-effect information obtained is reported. The results show surprisingly that the precision of flow-sensitive side-effect analysis is not always prohibitive in cost, and that the precision of flow-insensitive analysis is substantially better than worst-case estimates and seems sufficient for certain applications. On average MODC (FSAlias) for procedures and calls is in the range of 20% more precise than MODC (F IAlias); however, the performance was found to be at least an order of magnitude slower than MODC (F IAlias).

This paper presents an overview of automatic program parallelization techniques. It covers dependence analysis techniques, followed by a discussion of program transformations, including straight-line code parallelization, do loop transformations, and parallelization of recursive routines. The last section of the paper surveys several experimental studies on the effectiveness of parallelizing compilers.

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This dissertation addresses a number of important issues related to interprocedural optimization. Interprocedural optimization is an integral component in a compilation system for high-performance computing. The importance of interprocedural optimization stems from two sources: it increases the context available to the optimizing compiler, and it enables programmers to use procedure calls without the concern of hurting execution time. While important, interprocedural optimization can introduce some significant compile-time costs. When interprocedural information is used to optimize a procedure, the procedure is then dependent on those interprocedural facts. Thus, even if the procedure is not edited, it may require recompilation due to changes in the interprocedural facts. In addition to these effects on recompilation, interprocedural information can also be expensive to compute. Furthermore, interprocedural optimizations can increase program size which can in turn increase compile tim...

An Alias occurs at some program point during execution when two or more names exist for the same location. We've investigated the theoretical difficulty of determining the aliases of a program, developed an approximation algorithm for solving for aliases in C like languages, and explored the precision (i.e., closeness of our approximate solution to the actual solution) and time behavior of this algorithm. Myers [Mye81] explored the theoretical difficulty of solving flow sensitive interprocedural data flow problems in the presence of aliasing. However, he did not make any claims about the difficulty of determining aliases. We isolate various programming language mechanisms that create aliases. The complexity of the alias problem (i.e., determining the aliases for a program) induced by each mechanism and their combinations is considered s...

A unified model of a family of data flow algorithms, called elimination methods, is presented. The algorithms, which gather information about the definition and use of data in a program or a set of programs, are characterized by the manner in which they solve the systems of equations that describe data flow problems of interest. The unified model

Compiling for efficient execution on advanced computer architectures requires extensive program analysis and transformation. Most compilers limit their analysis to simple phenomena within single procedures, limiting effective optimization of modular codes and making the programmer's job harder. We present methods for analyzing array side effects and for comparing nonconstant values computed in the same and different procedures. Regular sections, described by rectangular bounds and stride, prove as effective in describing array side effects in Linpack as more complicated summary techniques. On a set of six programs, regular section analysis of array side effects gives 0 to 39 percent reductions in array dependences at call sites, with 10 to 25 percent increases in analysis time. Symbolic analysis is essential to data dependence testing, array section analysis, and other high-level program manipulations. We give methods for building symb...