Verifying the correctness of compiler transformations on basic blocks using abstract interpretation

1991 ◽  
Vol 26 (9) ◽  
pp. 106-115 ◽  
Author(s):  
Timothy S. McNerney
Author(s):  
S. Blom ◽  
S. Darabi ◽  
M. Huisman ◽  
M. Safari

AbstractA commonly used approach to develop deterministic parallel programs is to augment a sequential program with compiler directives that indicate which program blocks may potentially be executed in parallel. This paper develops a verification technique to reason about such compiler directives, in particular to show that they do not change the behaviour of the program. Moreover, the verification technique is tool-supported and can be combined with proving functional correctness of the program. To develop our verification technique, we propose a simple intermediate representation (syntax and semantics) that captures the main forms of deterministic parallel programs. This language distinguishes three kinds of basic blocks: parallel, vectorised and sequential blocks, which can be composed using three different composition operators: sequential, parallel and fusion composition. We show how a widely used subset of OpenMP can be encoded into this intermediate representation. Our verification technique builds on the notion of iteration contract to specify the behaviour of basic blocks; we show that if iteration contracts are manually specified for single blocks, then that is sufficient to automatically reason about data race freedom of the composed program. Moreover, we also show that it is sufficient to establish functional correctness on a linearised version of the original program to conclude functional correctness of the parallel program. Finally, we exemplify our approach on an example OpenMP program, and we discuss how tool support is provided.


2014 ◽  
Vol 49 (1) ◽  
pp. 47-59
Author(s):  
Stefano Dissegna ◽  
Francesco Logozzo ◽  
Francesco Ranzato

2019 ◽  
Vol 3 (OOPSLA) ◽  
pp. 1-28 ◽  
Author(s):  
Sven Keidel ◽  
Sebastian Erdweg

2021 ◽  
Vol 54 (7) ◽  
pp. 1-37
Author(s):  
Jihyeok Park ◽  
Hongki Lee ◽  
Sukyoung Ryu

Understanding program behaviors is important to verify program properties or to optimize programs. Static analysis is a widely used technique to approximate program behaviors via abstract interpretation. To evaluate the quality of static analysis, researchers have used three metrics: performance, precision, and soundness. The static analysis quality depends on the analysis techniques used, but the best combination of such techniques may be different for different programs. To find the best combination of analysis techniques for specific programs, recent work has proposed parametric static analysis . It considers static analysis as black-box parameterized by analysis parameters , which are techniques that may be configured without analysis details. We formally define the parametric static analysis, and we survey analysis parameters and their parameter selection in the literature. We also discuss open challenges and future directions of the parametric static analysis.


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