Detecting equality of variables in programs
POPL '88 Proceedings of the 15th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
An efficient method of computing static single assignment form
POPL '89 Proceedings of the 16th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Constant propagation with conditional branches
ACM Transactions on Programming Languages and Systems (TOPLAS)
Efficiently computing static single assignment form and the control dependence graph
ACM Transactions on Programming Languages and Systems (TOPLAS)
Translation validation for an optimizing compiler
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
TACAS '98 Proceedings of the 4th International Conference on Tools and Algorithms for Construction and Analysis of Systems
Translation and Run-Time Validation of Loop Transformations
Formal Methods in System Design
Translation validation of optimizing compilers
Translation validation of optimizing compilers
Formal verification of backward compatibility of microcode
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
TVOC: a translation validator for optimizing compilers
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
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The NYU Tvoc project applies the method of translation validation to verify that optimized code is semantically equivalent to the unoptimized code, by establishing, for each run of the optimizing compiler, a set of verification conditions (VCs) whose validity implies the correctness of the optimized run. The core of Tvoc is Tvoc-sp, that handles structure preserving optimizations, i.e., optimizations that do not alter the inner loop structures. The underlying proof rule, Val, on whose soundness Tvoc-sp is based, requires, among other things, to generating invariants at each ''cutpoint'' of the control graph of both source and target codes. The current implementation of Tvoc-sp employs somewhat naive fix-point computations to obtain the invariants. In this paper, we propose an alternative method to compute invartiants which is based on simple data-flow analysis techniques.