Evaluating Value-Graph Translation Validation for LLVM

Translation validators are static analyzers that attempt to verify that program transformations preserve semantics. Normalizing trans- lation validators do so by trying to match the value-graphs of an original function and its transformed counterpart. In this paper, we present the design of such a validator for LLVM’s intra-procedural optimizations, a design that does not require any instrumentation of the optimizer, nor any rewriting of the source code to compile, and needs to run only once to validate a pipeline of optimizations. We present the results of our preliminary experiments on a set of bench- marks that include GCC, a perl interpreter, SQLite3, and other C programs.Engineering and Applied Science

Verified Code Generation for the Polyhedral Model

International audienceThe polyhedral model is a high-level intermediate representation for loop nests that supports elegantly a great many loop optimizations. In a compiler, after polyhedral loop optimizations have been performed, it is necessary and difficult to regenerate sequential or parallel loop nests before continuing compilation. This paper reports on the formalization and proof of semantic preservation of such a code generator that produces sequential code from a polyhedral representation. The formalization and proofs are mechanized using the Coq proof assistant

Doctor of Philosophy

dissertationCompilers are indispensable tools to developers. We expect them to be correct. However, compiler correctness is very hard to be reasoned about. This can be partly explained by the daunting complexity of compilers. In this dissertation, I will explain how we constructed a random program generator, Csmith, and used it to find hundreds of bugs in strong open source compilers such as the GNU Compiler Collection (GCC) and the LLVM Compiler Infrastructure (LLVM). The success of Csmith depends on its ability of being expressive and unambiguous at the same time. Csmith is composed of a code generator and a GTAV (Generation-Time Analysis and Validation) engine. They work interactively to produce expressive yet unambiguous random programs. The expressiveness of Csmith is attributed to the code generator, while the unambiguity is assured by GTAV. GTAV performs program analyses, such as points-to analysis and effect analysis, efficiently to avoid ambiguities caused by undefined behaviors or unspecifed behaviors. During our 4.25 years of testing, Csmith has found over 450 bugs in the GNU Compiler Collection (GCC) and the LLVM Compiler Infrastructure (LLVM). We analyzed the bugs by putting them into different categories, studying the root causes, finding their locations in compilers' source code, and evaluating their importance. We believe analysis results are useful to future random testers, as well as compiler writers/users

Evaluating Value-Graph Translation Validation for LLVM

Translation validators are static analyzers that attempt to verify that program transformations preserve semantics. Normalizing trans- lation validators do so by trying to match the value-graphs of an original function and its transformed counterpart. In this paper, we present the design of such a validator for LLVM’s intra-procedural optimizations, a design that does not require any instrumentation of the optimizer, nor any rewriting of the source code to compile, and needs to run only once to validate a pipeline of optimizations. We present the results of our preliminary experiments on a set of bench- marks that include GCC, a perl interpreter, SQLite3, and other C programs.Engineering and Applied Science

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book

DRAFT Evaluating Value-Graph Translation Validation for LLVM

Translation validators are static analyzers that attempt to verify that program transformations preserve semantics. Normalizing translation validators do so by trying to match the value-graphs of an original function and it’s transformed counterpart. In this paper, we present the design of such a validator for LLVM’s intra-procedural optimizations, a design that does not require any instrumentation of the optimizer, nor any rewriting of the source code to compile, and needs to run only once to validate a pipeline of optimizations. We present the results of our preliminary experiments on a set of benchmarks that include GCC, a perl interpreter, sqlite3, and other C programs. 1