Code space is a critical issue facing designers of software for embedded systems. Many traditional compiler optimizations are designed to reduce the execution time of compiled code, but not necessarily the size of the compiled code. Further, di#erent results can be achieved by running some optimizations more than once and changing the order in which optimizations are applied. Register allocation only complicates matters, as the interactions between di#erent optimizations can cause more spill code to be generated. The compiler for embedded systems, then, must take care to use the best sequence of optimizations to minimize code space.

Grammarware comprises grammars and all grammar-dependent software, i.e., software artifacts that directly involve grammar knowledge. The term grammar is meant here in the widest sense to include XML schemas, syntax definitions, interface descriptions, APIs, and interaction protocols. The most obvious examples of grammar-dependent software are document processors, parsers, import/export functionality, and generative programming tools. Even though grammarware is so omnipresent, it is somewhat neglected --- from an engineering point of view. We lay out an agenda that is meant to promote research on improving the quality of grammarware and on increasing the productivity of grammarware development.

this article, we describe approximation methods for computing interprocedural aliases for a program written in a language that includes pointers, reference parameters, and recursion. We present the following contributions:

This paper explores compiler techniques for reducing the memory needed to load and run program executables. In embedded systems, where economic incentives to reduce both ram and rom are strong, the size of compiled code is increasingly important. Similarly, in mobile and network computing, the need to transmit an executable before running it places a premium on code size. Our work focuses on reducing the size of a program's code segment, using pattern-matching techniques to identify and coalesce together repeated instruction sequences. In contrast to other methods, our framework preserves the ability to run program executables directly, without an intervening decompression stage. Our compression framework is integrated into an industrial-strength optimizing compiler, which allows us to explore the interaction between code compression and classical code optimization techniques, and requires that we contend with the difficulties of compressing previously optimized code. The specific contributions in this paper include a comprehensive experimental evaluation of code compression for a Risc-like architecture, a more powerful pattern-matching scheme for improved identification of repeated code fragments, and a new form of profile-driven code compression that reduces the speed penalty arising from compression.

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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...

Most current data dependence tests cannot handle loop bounds or array subscripts that are symbolic, nonlinear expressions (e.g. A(n i+j), where 0 j n). In this paper, we describe a dependencetest,called the range test, that can handle such expressions. Briefly, the range test proves independence by determining whether certain symbolic inequalities hold for a permutation of the loop nest. Powerful symbolic analyses and constraint propagation techniques were developedtoprove such inequalities.Therange test has been implemented in Polaris, a parallelizing compiler being developed at the University of Illinois.

Building efficient tools for understanding large software systems is difficult. Many existing program understanding tools build control-flow and data-flow representations of the program a priori, and therefore may require prohibitive space and time when analyzing large systems. Since much of these representations may be unused during an analysis, we construct representations on demand, not in advance. Furthermore, some representations, such as the abstract syntax tree, may be used infrequently during an analysis. We discard these representations and recompute them as needed, reducing the overall space required. Finally, we permit the user to selectively trade-off time for precision and to customize the termination of these costly analyses in order to provide finer user control. We revised the traditional software architecture for compilers to provide these features without unnecessarily complicating the analyses themselves.

In this paper, we present a GSA-based technique that performs more efficient and more precise symbolic analysis of predicated assignments, recurrences and index arrays. The efficiency is improved by using a backward substitution scheme that performs resolution of assertions on-demand and uses heuristics to limit the number of substitution. The precision is increased by utilizing the gating predicate information embedded in the GSA and the control dependence information in the program flow graph. Examples from array privatization are used to illustrate how the technique aids loop parallelization.

A new algorithm, SSAPRE, for performing partial redundancy elimination based entirely on SSA form is presented. It achieves optimal code motion similar to lazy code motion [KRS94a, DS93], but is formulated independently and does not involve iterative data flow analysis and bit vectors in its solution. It not only exhibits the characteristics common to other sparse approaches, but also inherits the advantages shared by other SSA-based optimization techniques. SSAPRE also maintains its output in the same SSA form as its input. In describing the algorithm, we state theorems with proofs giving our claims about SSAPRE. We also give additional description about our practical implementation of SSAPRE, and analyze and compare its performance with a bit-vector-based implementation of PRE. We conclude with some discussion of the implications of this work. 1 Introduction The Static Single Assignment Form (SSA) has become a popular program representation in optimizing compilers, because it provid...