Automatic recognition of induction variables and recurrence relations by abstract interpretation
PLDI '90 Proceedings of the ACM SIGPLAN 1990 conference on Programming language design and implementation
Efficiently computing static single assignment form and the control dependence graph
ACM Transactions on Programming Languages and Systems (TOPLAS)
Loop monotonic computations: an approach for the efficient run-time detection of races
TAV4 Proceedings of the symposium on Testing, analysis, and verification
Beyond induction variables: detecting and classifying sequences using a demand-driven SSA form
ACM Transactions on Programming Languages and Systems (TOPLAS)
Symbolic analysis for parallelizing compilers
ACM Transactions on Programming Languages and Systems (TOPLAS)
Compiler analysis of irregular memory accesses
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Monotonic evolution: an alternative to induction variable substitution for dependence analysis
ICS '01 Proceedings of the 15th international conference on Supercomputing
The range test: a dependence test for symbolic, non-linear expressions
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Parallel Programming with Polaris
Computer
Analyses of Pointers, Induction Variables, and Container Objects for Dependence Testing
Analyses of Pointers, Induction Variables, and Container Objects for Dependence Testing
Simple and effective array prefetching in Java
JGI '02 Proceedings of the 2002 joint ACM-ISCOPE conference on Java Grande
Sensitivity analysis for automatic parallelization on multi-cores
Proceedings of the 21st annual international conference on Supercomputing
Automatic parallelization using the value evolution graph
LCPC'04 Proceedings of the 17th international conference on Languages and Compilers for High Performance Computing
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Traditional induction variable (IV) analyses focus on computing the closed form expressions of variables. This paper presents a new IV analysis based on a property called distance interval. This property captures the value changes of a variable along a given control-flow path of a program. Based on distance intervals, an efficient algorithm detects dependences for array accesses that involve induction variables. This paper describes how to compute distance intervals and how to compute closed form expressions and test dependences based on distance intervals. This work is an extension of the previous induction variable analyses based on monotonic evolution [11]. With the same computational complexity, the new algorithm improves the monotonic evolution-based analysis in two aspects: more accurate dependence testing and the ability to compute closed form expressions. The experimental results demonstrate that when dealing with induction variables, dependence tests based on distance intervals are both efficient and effective compared to closed-form based dependence tests.