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.

Partial redundancy elimination is a code optimization with a long history of literature and implementation. In practice, its effectiveness depends on issues of naming and code shape. This paper shows that a combination of global reassociation and global value numbering can increase the effectiveness of partial redundancy elimination. By imposing a discipline on the choice of names and the shape of expressions, we are able to expose more redundancies. As part of the work, we introduce a new algorithm for global reassociation of expressions. It uses global information to reorder expressions, creating opportunities for other optimizations. The new algorithm generalizes earlier work that ordered FORTRAN array address expressions to improve optimization [25]. 1 Introduction Partial redundancy elimination is a powerful optimization that has been discussed in the literature for many years (e.g., [21, 8, 14, 12, 18]). Unfortunately, partial redundancy elimination has two serious limitations...

Improving the performance of C programs has been a topic of great interest for many years. Both hardware technology and compiler optimization research has been applied in an effort to make C programs execute faster. In many application domains, the C++ language is replacing C as the programming language of choice. In this paper, we measure the empirical behavior of a group of significant C and C++ programs and attempt to identify and quantify behavioral differences between them. Our goal is to determine whether optimization technology that has been successful for C programs will also be successful in C++ programs. We furthermore identify behavioral characteristics of C++ programs that suggest optimizations that should be applied in those programs. Our results show that C++ programs exhibit behavior that is significantly different than C programs. These results should be of interest to compiler writers and architecture designers who are designing systems to execute object-oriented programs.

This paper presents a new al gS ithm for operator strengM reduction, called OSR. OSR improves upon an earlier alg orithm due to Allen, Cocke, and Kennedy [Allen et al. 1981]. OSR operates on the static sing e assig4 ent (SSA) form of a procedure [Cytron et al. 1991]. By taking advantag of the properties of SSA form, we have derived an alg--- ithm that is simple to understand, quick to implement, and, in practice, fast to run. Its asymptotic complexity is, in the worst case, the same as the Allen, Cocke, and Kennedy al gS ithm (ACK). OSR achieves optimization results that are equivalent to those obtained with the ACK alg orithm. OSR has been implemented in several research and production compilers

. Data flow analysis expresses the solution of an information gathering problem as the fixed point of a system of monotone equations. This paper presents a technique to improve the performance of data flow analysis by systematically reducing the size of the equation system in any monotone data flow problem. Reductions result from partitioning the equations in the system according to congruence relations. We present a fast O(n log n) partitioning algorithm, where n is the size of the program, that exploits known algebraic properties in equation systems. From the resulting partition a reduced equation system is constructed that is minimized with respect to the computed congruence relation while still providing the data flow solution at all program points. 1 Introduction Along with the growing importance of static data flow analysis in current optimizing and parallelizing compilers comes an increased concern about the high time and space requirements of solving data flow problems. Experi...

Optimizing compilers have become an essential component in achieving high levels of performance. Various simple and sophisticated optimizations are implemented at different stages of compilation to yield significant improvements, but little work has been done in characterizing the effectiveness of optimizers, or in understanding where most of this improvement comes from. In this paper we study the performance impact of optimization in the context of our methodology for CPU performance characterization based on the abstract machine model. The abstract machine model considers all machines to be different implementations of the same high level language machine; in previous research, we have used this model as a basis to analyze machine and benchmark performance. In this paper, we: 1) show that our model can be extended to characterize the performance improvement provided by optimizers and to predict the run time of optimized programs; 2) measure the effectiveness of several optimizing com...

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An algebraic compiler allows incremental development of the source program and builds its target image by composing the target images of the program components. In this paper we describe the general structure of an algebraic compiler focusing on compositional code generation. We show that the mathematical model for register management by an algebraic compiler is a graph coloring problem in which an optimally colored graph is obtained by composing optimally colored subgraphs. More precisely, we define the clique-composition of graphs G 1 and G 2 as the graph obtained by joining all the vertices in a clique in G 1 with all the vertices in a clique in G 2 and show that optimal register management by an algebraic compiler is achieved by performing clique-composition operations. Thus, an algebraic compiler provides automatically adequate clique separation of the global register management graph. We present a linear-time algorithm that takes as input optimally colored graphs G 1 and G 2 and...