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NV: An Intermediate language for Verification of Network Control Planes 2020

NV: An Intermediate Language for Verification of Network Con...

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41st acm sigplan conference on programming language design and implementation (pldi)

作者： Giannarakis, Nick Loehr, Devon Beckett, Ryan Walker, David Princeton Univ Princeton NJ 08544 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781450376136

Network misconfiguration has caused a raft of high-profile outages over the past decade, spurring researchers to develop a variety of network analysis and verification tools. Unfortunately, developing and maintaining such tools is an enormous challenge due to the complexity of network configuration languages. Inspired by work on intermediate languages for verification such as Boogie and Why3, we develop NV, an intermediate language for verification of network control planes. NV carefully walks the line between expressiveness and tractability, making it possible to build models for a practical subset of real protocols and their configurations, and also facilitate rapid development of tools that outperform state-of-the-art simulators (seconds vs minutes) and verifiers (often 10x faster). Furthermore, we show that it is possible to develop novel analyses just by writing new NV programs. In particular, we implement a new fault-tolerance analysis that scales to far larger networks than existing tools.

关键词： Network Verification Network Simulation Control Plane Analysis Router Configuration Analysis Intermediate Verification language

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Gillian, Part I: A Multi-language Platform for Symbolic Execution 2020

Gillian, Part I: A Multi-language Platform for Symbolic Exec...

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41st acm sigplan conference on programming language design and implementation (pldi)

作者： Santos, Jose Fragoso Maksimovic, Petar Ayoun, Sacha-Elie Gardner, Philippa Univ Lisbon INESC ID Inst Super Tecn Lisbon Portugal Imperial Coll London London England

ISBN: (纸本)9781450376136

We introduce Gillian, a platform for developing symbolic analysis tools for programming languages. Here, we focus on the symbolic execution engine at the heart of Gillian, which is parametric on the memory model of the target language. We give a formal description of the symbolic analysis and a modular implementation that closely follows this description. We prove a parametric soundness result, introducing restriction on abstract states, which generalises path conditions used in classical symbolic execution. We instantiate Gillian to obtain trusted symbolic testing tools for JavaScript and C, and use these tools to find bugs in real-world code, thus demonstrating the viability of our parametric approach.

关键词： Symbolic execution parametric semantics bug-finding bounded verification JavaScript C

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Multi-modal Synthesis of Regular Expressions 2020

Multi-modal Synthesis of Regular Expressions

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41st acm sigplan conference on programming language design and implementation (pldi)

作者： Chen, Qiaochu Wang, Xinyu Ye, Xi Durrett, Greg Dillig, Isil Univ Texas Austin Austin TX 78712 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781450376136

In this paper, we propose a multi-modal synthesis technique for automatically constructing regular expressions (regexes) from a combination of examples and natural language. Using multiple modalities is useful in this context because natural language alone is often highly ambiguous, whereas examples in isolation are often not sufficient for conveying user intent. Our proposed technique first parses the English description into a so-called hierarchical sketch that guides our programming-by-example (PBE) engine. Since the hierarchical sketch captures crucial hints, the PBE engine can leverage this information to both prioritize the search as well as make useful deductions for pruning the search space. We have implemented the proposed technique in a tool called Regel and evaluate it on over three hundred regexes. Our evaluation shows that Regel achieves 80% accuracy whereas the NLP-only and PBE-only baselines achieve 43% and 26% respectively. We also compare our proposed PBE engine against an adaptation of AlphaRegex, a state-of-the-art regex synthesis tool, and show that our proposed PBE engine is an order of magnitude faster, even if we adapt the search algorithm of AlphaRegex to leverage the sketch. Finally, we conduct a user study involving 20 participants and show that users are twice as likely to successfully come up with the desired regex using Regel compared to without it.

关键词： Program Synthesis programming by Natural languages programming by Example Regular Expression

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Armada: Low-Effort Verification of High-Performance Concurrent Programs 2020

Armada: Low-Effort Verification of High-Performance Concurre...

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41st acm sigplan conference on programming language design and implementation (pldi)

作者： Lorch, Jacob R. Chen, Yixuan Kapritsos, Manos Parno, Bryan Qadeer, Shaz Sharma, Upamanyu Wilcox, James R. Zhao, Xueyuan Microsoft Res Redmond WA 98052 USA Univ Michigan Ann Arbor MI 48109 USA Yale Univ New Haven CT 06520 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Calibra Seattle WA USA Certora San Francisco CA USA

ISBN: (纸本)9781450376136

Safely writing high-performance concurrent programs is notoriously difficult. To aid developers, we introduce Armada, a language and tool designed to formally verify such programs with relatively little effort. Via a C-like language and a small-step, state-machine-based semantics, Armada gives developers the flexibility to choose arbitrary memory layout and synchronization primitives so they are never constrained in their pursuit of performance. To reduce developer effort, Armada leverages SMT-powered automation and a library of powerful reasoning techniques, including rely-guarantee, TSO elimination, reduction, and alias analysis. All these techniques are proven sound, and Armada can be soundly extended with additional strategies over time. Using Armada, we verify four concurrent case studies and show that we can achieve performance equivalent to that of unverified code.

关键词： refinement weak memory models x86-TSO

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Synthesizing Structured CAD Models with Equality Saturation and Inverse Transformations 2020

Synthesizing Structured CAD Models with Equality Saturation ...

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41st acm sigplan conference on programming language design and implementation (pldi)

作者： Nandi, Chandrakana Willsey, Max Anderson, Adam Wilcox, James R. Darulova, Eva Grossman, Dan Tatlock, Zachary Univ Washington Seattle WA 98195 USA Certora Raleigh NC USA MPI SWS Saarbrucken Germany

ISBN: (纸本)9781450376136

Recent program synthesis techniques help users customize CAD models (e.g., for 3D printing) by decompiling low-level triangle meshes to Constructive Solid Geometry (CSG) expressions. Without loops or functions, editing CSG can require many coordinated changes, and existing mesh decompilers use heuristics that can obfuscate high-level structure. This paper proposes a second decompilation stage to robustly lshrinkz unstructured CSG expressions into more editable programs with map and fold operators. We present Szalinski, a tool that uses Equality Saturation with semanticspreserving CAD rewrites to efficiently search for smaller equivalent programs. Szalinski relies on inverse transformations, a novel way for solvers to speculatively add equivalences to an E-graph. We qualitatively evaluate Szalinski in case studies, show how it composes with an existing mesh decompiler, and demonstrate that Szalinski can shrink large models in seconds.

关键词： Equality Saturation Program Synthesis Decompilation Computer-Aided design

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Predictable Accelerator design with Time-Sensitive Affine Types 2020

Predictable Accelerator Design with Time-Sensitive Affine Ty...

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41st acm sigplan conference on programming language design and implementation (pldi)

作者： Nigam, Rachit Atapattu, Sachille Thomas, Samuel Li, Zhijing Bauer, Theodore Ye, Yuwei Koti, Apurva Sampson, Adrian Zhang, Zhiru Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781450376136

Field-programmable gate arrays (FPGAs) provide an opportunity to co-design applications with hardware accelerators, yet they remain difficult to program. High-level synthesis (HLS) tools promise to raise the level of abstraction by compiling C or C++ to accelerator designs. Repurposing legacy software languages, however, requires complex heuristics to map imperative code onto hardware structures. We find that the black-box heuristics in HLS can be unpredictable: changing parameters in the program that should improve performance can counterintuitively yield slower and larger designs. This paper proposes a type system that restricts HLS to programs that can predictably compile to hardware accelerators. The key idea is to model consumable hardware resources with a time-sensitive affine type system that prevents simultaneous uses of the same hardware structure. We implement the type system in Dahlia, a language that compiles to HLS C++, and show that it can reduce the size of HLS parameter spaces while accepting Pareto-optimal designs.

关键词： Affine Type Systems High-Level Synthesis

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Towards an API for the Real Numbers 2020

Towards an API for the Real Numbers

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41st acm sigplan conference on programming language design and implementation (pldi)

作者： Boehm, Hans-J Google Mountain View CA 94043 USA

ISBN: (纸本)9781450376136

The real numbers are pervasive, both in daily life, and in mathematics. Students spend much time studying their properties. Yet computers and programming languages generally provide only an approximation geared towards performance, at the expense of many of the nice properties we were taught in high school. Although this is entirely appropriate for many applications, particularly those that are sensitive to arithmetic performance in the usual sense, we argue that there are others where it is a poor choice. If arithmetic computations and result are directly exposed to human users who are not floating point experts, floating point approximations tend to be viewed as bugs. For applications such as calculators, spreadsheets, and various verification tasks, the cost of precision sacrifices is high, and the performance benefit is often not critical. We argue that previous attempts to provide accurate and understandable results for such applications using the recursive reals were great steps in the right direction, but they do not suffice. Comparing recursive reals diverges if they are equal. In many cases, comparison of numbers, including equal ones, is both important, particularly in simple cases, and intractable in the general case. We propose an API for a real number type that explicitly provides decidable equality in the easy common cases, in which it is often unnatural not to. We describe a surprisingly compact and simple implementation in detail. The approach relies heavily on classical number theory results. We demonstrate the utility of such a facility in two applications: testing floating point functions, and to implement arithmetic in Google's Android calculator application.

关键词： real numbers API design comparison decidability

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On the Principles of Differentiable Quantum programming languages 2020

On the Principles of Differentiable Quantum Programming Lang...

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41st acm sigplan conference on programming language design and implementation (pldi)

作者： Zhu, Shaopeng Hung, Shih-Han Chakrabarti, Shouvanik Wu, Xiaodi Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781450376136

Variational Quantum Circuits (VQCs), or the so-called quantum neural-networks, are predicted to be one of the most important near-term quantum applications, not only because of their similar promises as classical neural-networks, but also because of their feasibility on near-term noisy intermediate-size quantum (NISQ) machines. The need for gradient information in the training procedure of VQC applications has stimulated the development of auto-differentiation techniques for quantum circuits. We propose the first formalization of this technique, not only in the context of quantum circuits but also for imperative quantum programs (e.g., with controls), inspired by the success of differentiable programming languages in classical machine learning. In particular, we overcome a few unique difficulties caused by exotic quantum features (such as quantum no-cloning) and provide a rigorous formulation of differentiation applied to bounded-loop imperative quantum programs, its code-transformation rules, as well as a sound logic to reason about their correctness. Moreover, we have implemented our code transformation in OCaml and demonstrated the resource-efficiency of our scheme both analytically and empirically. We also conduct a case study of training a VQC instance with controls, which shows the advantage of our scheme over existing auto-differentiation for quantum circuits without controls.

关键词： quantum programming languages differentiable programming languages quantum machine learning

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SOAP 2020 - Proceedings of the 9th acm sigplan International Workshop on the State of the Art in Program Analysis, co-located with pldi 2020

SOAP 2020 - Proceedings of the 9th ACM SIGPLAN International...

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9th acm sigplan International Workshop on the State Of the Art in Program Analysis, SOAP 2020, co-located with the 41st acm sigplan conference on programming language design and implementation, pldi 2020

The proceedings contain 3 papers. The topics discussed include: formal reasoning and the hacker way;TACAI: an intermediate representation based on abstract interpretation;value and allocation sensitivity in static pyt...

ISBN: (纸本)9781450379977

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EVA: An Encrypted Vector Arithmetic language and Compiler for Efficient Homomorphic Computation 2020

EVA: An Encrypted Vector Arithmetic Language and Compiler fo...

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41st acm sigplan conference on programming language design and implementation (pldi)

作者： Dathathri, Roshan Kostova, Blagovesta Saarikivi, Olli Dai, Wei Laine, Kim Musuvathi, Madan Univ Texas Austin Austin TX 78712 USA Ecole Polytech Fed Lausanne Lausanne Switzerland Microsoft Res Redmond WA USA

ISBN: (纸本)9781450376136

Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations have made it all the more attractive. At the same time, FHE is notoriously hard to use with a very constrained programming model, a very unusual performance profile, and many cryptographic constraints. Existing compilers for FHE either target simpler but less efficient FHE schemes or only support specific domains where they can rely on expert-provided high-level runtimes to hide complications. This paper presents a new FHE language called Encrypted Vector Arithmetic (EVA), which includes an optimizing compiler that generates correct and secure FHE programs, while hiding all the complexities of the target FHE scheme. Bolstered by our optimizing compiler, programmers can develop efficient general-purpose FHE applications directly in EVA. For example, we have developed image processing applications using EVA, with a very few lines of code. EVA is designed to also work as an intermediate representation that can be a target for compiling higher-level domain-specific languages. To demonstrate this, we have re-targeted CHET, an existing domain-specific compiler for neural network inference, onto EVA. Due to the novel optimizations in EVA, its programs are on average 5.3x faster than those generated by CHET. We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.

关键词： Homomorphic encryption compiler neural networks privacy-preserving machine learning

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