Array privatization is one of the most effective transformations for the exploitation of parallelism. In this paper, we present a technique for automatic array privatization. Our algorithm uses data flow analysis of array references to identify privatizable arrays intraprocedurally as well as interprocedurally. It employs static and dynamic resolution to determine the last value of a lived private array.We compare the result of automatic array privatization with that of manual array privatization and identify directions for future improvement. To enhance the effectiveness of our algorithm, wedevelop a goal directly technique to analysis symbolic variables in the present of conditional statements, loops and index arrays.

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.

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.

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...

This thesis presents a framework for describing optimizations. It shows how to combine two such frameworks and how to reason about the properties of the resulting framework. The structure of the framework provides insight into when a combination yields better results. Also presented is a simple iterative algorithm for solving these frameworks. A framework is shown that combines Constant Propagation, Unreachable Code Elimination, Global Congruence Finding and Global Value Numbering. For these optimizations, the iterative algorithm runs in O(n^2) time. This thesis then presents an O(n log n) algorithm for combining the same optimizations. This technique also finds many of the common subexpressions found by Partial Redundancy Elimination. However, it requires a global code motion pass to make the optimized code correct, also presented. The global code motion algorithm removes some Partially Dead Code as a side-effect. An implementation demonstrates that the algorithm has shorter compile times than repeated passes of the separate optimizations while producing run-time speedups of 4%–7%. While global analyses are stronger, peephole analyses can be unexpectedly powerful. This thesis demonstrates parse-time peephole optimizations that find more than 95% of the constants and common subexpressions found by the best combined analysis. Finding constants and common subexpressions while parsing reduces peak intermediate representation size. This speeds up the later global analyses, reducing total compilation time by 10%. In conjunction with global code motion, these peephole optimizations generate excellent code very quickly, a useful feature for compilers that stress compilation speed over code quality.

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.

In order to perform powerful program optimizations, an exact interprocedural analysis of array data ow is needed. For that purpose, two new types of array region are introduced. IN and OUT regions represent the sets of array elements, the values of which are imported to or exported from the current statement or procedure. Among the various applications are: compilation of communications for message-passing machines, array privatization, compile-time optimization of local memory or cache behavior in hierarchical memory machines.

This paper presents a compiler optimization algorithm to reduce the run time overhead of array subscript range checks in programs without compromising safety. The algorithm is based on partial redundancy elimination and it incorporates previously developed algorithms for range check optimization. We implemented the algorithm in our research compiler, Nascent, and conducted experiments on a suite of 10 benchmark programs to obtain four results: (1) the execution overhead of naive range checking is high enough to merit optimization, (2) there are substantial differences between various optimizations, (3) loop-based optimizations that hoist checks out of loops are effective in eliminating about 98% of the range checks, and (4) more sophisticated analysis and optimization algorithms produce very marginal benefits. 1 Introduction Program statements that access elements of an array outside the declared array ranges introduce errors which can be difficult to detect. Since compile-time check...

Static single assignment (SSA) form is a program representation becoming increasingly popular for compiler-based code optimization. In this paper, we address three problems that have arisen in our use of SSA form. Two are variations to the SSA construction algorithms presented by Cytron et al. The first variation is a version of...

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...