Provably correct peephole optimizations with alive

Lopes, Nuno P.; Menendez, David; Nagarakatte, Santosh; Regehr, John

doi:10.1145/2737924.2737965

Cited by 89 publications

(34 citation statements)

References 49 publications

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“…In addition, many peephole optimizations can be readily modeled as patterns, and several tools exist for automatic discovery and verification of such patterns [5,10,43]. We intend to exploit these tools to automatically extend the pattern set with these patterns in order to further improve code quality.…”

Section: Discussionmentioning

confidence: 99%

Complete and Practical Universal Instruction Selection

Blindell

Carlsson

Lozano

et al. 2017

ACM Trans. Embed. Comput. Syst.

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In code generation, instruction selection chooses processor instructions to implement a program under compilation where code quality crucially depends on the choice of instructions. Using methods from combinatorial optimization, this paper proposes an expressive model that integrates global instruction selection with global code motion. The model introduces (1) handling of memory computations and function calls, (2) a method for inserting additional jump instructions where necessary, (3) a dependency-based technique to ensure correct combinations of instructions, (4) value reuse to improve code quality, and (5) an objective function that reduces compilation time and increases scalability by exploiting bounding techniques. The approach is demonstrated to be complete and practical, competitive with LLVM, and potentially optimal (w.r.t. the model) for medium-sized functions. The results show that combinatorial optimization for instruction selection is well-suited to exploit the potential of modern processors in embedded systems.

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Section: Discussionmentioning

confidence: 99%

Complete and Practical Universal Instruction Selection

Blindell

Carlsson

Lozano

et al. 2017

ACM Trans. Embed. Comput. Syst.

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“…They implemented 28 peephole optimizations which required 3.3k lines of code and 6.6k lines of proofs (∼350 LoC each). Even if this is small compared to the size of CompCert, the burden is non-trivial considering that LLVM has more than 1,000 peephole optimizations [12].…”

Section: Compiler Verificationmentioning

confidence: 99%

“…Various frameworks have been proposed to verify optimizations of industrial compilers. Among them, Alive [12] is a tool for verifying peephole optimizations of LLVM that has been successfully adopted by compiler developers. Since it was released, Alive has helped developers find dozens of bugs.…”

Section: Introductionmentioning

confidence: 99%

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AliveInLean: A Verified LLVM Peephole Optimization Verifier

Lee

Hur

Lopes

2019

Lecture Notes in Computer Science

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Ensuring that compiler optimizations are correct is important for the reliability of the entire software ecosystem, since all software is compiled. Alive [12] is a tool for verifying LLVM's peephole optimizations. Since Alive was released, it has helped compiler developers proactively find dozens of bugs in LLVM, avoiding potentially hazardous miscompilations. Despite having verified many LLVM optimizations so far, Alive is itself not verified, which has led to at least once declaring an optimization correct when it was not. We introduce AliveInLean, a formally verified peephole optimization verifier for LLVM. As the name suggests, AliveInLean is a reengineered version of Alive developed in the Lean theorem prover [14]. Assuming that the proof obligations are correctly discharged by an SMT solver, AliveInLean gives the same level of correctness guarantees as state-ofthe-art formal frameworks such as CompCert [11], Peek [15], and Vellvm [26], while inheriting the advantages of Alive (significantly more automation and easy adoption by compiler developers).

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“…However, compilers are large code bases and have been shown to have bugs and produce wrong code at times [6,23,26,29,46]. The rest of the paper describes our approach for automatically verifying that the machine code of U satisfies WCFI-RW, thereby removing the compiler from the TCB.…”

Section: Overviewmentioning

confidence: 99%

A design and verification methodology for secure isolated regions

Sinha

Costa

Lal

et al. 2016

Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation

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Hardware support for isolated execution (such as Intel SGX) enables development of applications that keep their code and data confidential even while running on a hostile or compromised host. However, automatically verifying that such applications satisfy confidentiality remains challenging. We present a methodology for designing such applications in a way that enables certifying their confidentiality. Our methodology consists of forcing the application to communicate with the external world through a narrow interface, compiling it with runtime checks that aid verification, and linking it with a small runtime library that implements the interface. The runtime library includes core services such as secure communication channels and memory management. We formalize this restriction on the application as Information Release Confinement (IRC), and we show that it allows us to decompose the task of proving confidentiality into (a) one-time, human-assisted functional verification of the runtime to ensure that it does not leak secrets, (b) automatic verification of the application's machine code to ensure that it satisfies IRC and does not directly read or corrupt the runtime's internal state. We present /CONFIDENTIAL: a verifier for IRC that is modular, automatic, and keeps our compiler out of the trusted computing base. Our evaluation suggests that the methodology scales to real-world applications.

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Provably correct peephole optimizations with alive

Cited by 89 publications

References 49 publications

Complete and Practical Universal Instruction Selection

Complete and Practical Universal Instruction Selection

AliveInLean: A Verified LLVM Peephole Optimization Verifier

A design and verification methodology for secure isolated regions

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