Precise and efficient dependence tests are essential to the effectiveness of a parallelizing compiler. This paper proposes a dependence testing scheme based on classifying pairs of subscripted variable references. Exact yet fast dependence tests are presented for certain classes of array references, as well as empirical results showing that these references dominate scientific Fortran codes. These dependence tests are being implemented at Rice University in both PFC, a parallelizing compiler, and ParaScope, a parallel programming environment.

this paper we present a practical technique for detecting a broader class of linear induction variables than is usually recognized, as well as several other sequence forms, including periodic, polynomial, geometric, monotonic, and wrap-around variables. Our method is based on Factored Use-Def (FUD) chains, a demand-driven representation of the popular Static Single Assignment form. In this form, strongly connected components of the associated SSA graph correspond to sequences in the source program: we describe a simple yet efficient algorithm for detecting and classifying these sequences. We have implemented this algorithm in Nascent, our restructuring Fortran 90+ compiler, and we present some results showing the effectiveness of our approach.

Symbolic Domain The objects in our abstract symbolic domain are canonical symbolic expressions. A canonical symbolic expression is a lexicographically ordered sequence of symbolic terms. Each symbolic term is in turn a pair of an integer coefficient and a sequence of pairs of pointers to program variables in the program symbol table and their exponents. The latter sequence is also lexicographically ordered. For example, the abstract value of the symbolic expression 2ij+3jk in an environment that i is bound to (1; (( " i ; 1))), j is bound to (1; (( " j ; 1))), and k is bound to (1; (( " k ; 1))) is ((2; (( " i ; 1); ( " j ; 1))); (3; (( " j ; 1); ( " k ; 1)))). In our framework, environment is the abstract analogous of state concept; an environment is a function from program variables to abstract symbolic values. Each environment e associates a canonical symbolic value e x for each variable x 2 V ; it is said that x is bound to e x. An environment might be represented by...

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.

This paper presents Sharlit, a tool to support the construction of modular and extensible global optimizers. We will show how Sharlit helps in constructing data-flow analyzers and the transformations that use data-flow analysis information: both are major components of any optimizer.

The SUIF Explorer is an interactive parallelization tool that is more effective than previous systems in minimizing the number of lines of code that require programmer assistance. First, the interprocedural analyses in the SUIF system is successful in parallelizing many coarse-grain loops, thus minimizing the number of spurious dependences requiring attention. Second, the system uses dynamic execution analyzers to identify those important loops that are likely to be parallelizable. Third, the SUIF Explorer is the first to apply program slicing to aid programmers in interactive parallelization. The system guides the programmer in the parallelization process using a set of sophisticated visualization techniques. This paper demonstrates the effectiveness of the SUIF Explorer with three case studies. The programmer was able to speed up all three programs by examining only a small fraction of the program and privatizing a few variables. 1. Introduction Exploiting coarse-grain parallelism i...

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We present a simple method for normalizing the control-flow of programs to facilitate program transformations, program analysis, and automatic parallelization. While previous methods result in programs whose control flowgraphs are reducible, programs normalized by this technique satisfy a stronger condition than reducibility and are therefore simpler in their syntax and structure than with previous methods. In particular, all control-flow cycles are normalized into single-entry, single-exit while loops, and all goto's are eliminated. Furthermore, the method avoids problems of code replication that are characteristic of node-splitting techniques. This restructuring obviates the control dependence graph, since afterwards control dependence relations are manifest in the syntax tree of the program. In this paper we present transformations that effect this normalization, and study the complexity of the method. Index Terms: Continuations, control-flow, elimination algorithms, normalization,...

Higher-1evel parallel programming languages can be difficult to implement efficiently on parallel machines. This paper shows how a flexible, compiler-controlled memory system can help achieve good performance for language constructs that previously appeared too costly to be practical. Our compiler-controlled memory system is called Loosely Coherent Memory (LCM). It is an example of a larger class of Reconcilable Shared Memory (RSM) systems, which generalize the replication and merge policies of cache-coherent shared-memory. RSM pro-tocols differ in the action taken by a processor in re-sponse to a request for a location and the way in which a processor reconcdes multiple outstanding copies of a location, LCM memory becomes temporarily in-consistent to implement the semantics of C* * par-allel functions efficiently. RSM provides a compiler with control over memory-system policies, which it can use to implement a language’s semantics, improve performance, or detect errors. We illustrate the first two points with LCM and our compiler for the data-parallel language C**.

A program flow analysis framework is proposed for parallelizing compilers. Within this framework, symbolic analysis is used as an abstract interpretation technique to solve many of the flow analysis problems in a unified way. Some of these problems are constant propagation, global forward substitution, detection of loop invariant computations, and induction variable substitution. The solution space of the above problems is much larger than that handled by existing compiler technology. It covers many of the cases in benchmark codes that other parallelizing compilers can not handle. Employing finite difference methods, the symbolic analyzer derives a functional representation of programs, which is used in dependence analysis. A systematic method for generalized strength reduction based on this representation is also presented. This results in an effective scheme for exploitation of parallelism and optimization of the code. Symbolic analysis also serves as a basis for other code generatio...