Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation最新文献_第7页

Templates and recurrences: better together 模板和递归:更好地结合在一起

Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2020-03-30 DOI: 10.1145/3385412.3386035

J. Breck, John Cyphert, Zachary Kincaid, T. Reps

{"title":"Templates and recurrences: better together","authors":"J. Breck, John Cyphert, Zachary Kincaid, T. Reps","doi":"10.1145/3385412.3386035","DOIUrl":"https://doi.org/10.1145/3385412.3386035","url":null,"abstract":"This paper is the confluence of two streams of ideas in the literature on generating numerical invariants, namely: (1) template-based methods, and (2) recurrence-based methods. A template-based method begins with a template that contains unknown quantities, and finds invariants that match the template by extracting and solving constraints on the unknowns. A disadvantage of template-based methods is that they require fixing the set of terms that may appear in an invariant in advance. This disadvantage is particularly prominent for non-linear invariant generation, because the user must supply maximum degrees on polynomials, bases for exponents, etc. On the other hand, recurrence-based methods are able to find sophisticated non-linear mathematical relations, including polynomials, exponentials, and logarithms, because such relations arise as the solutions to recurrences. However, a disadvantage of past recurrence-based invariant-generation methods is that they are primarily loop-based analyses: they use recurrences to relate the pre-state and post-state of a loop, so it is not obvious how to apply them to a recursive procedure, especially if the procedure is non-linearly recursive (e.g., a tree-traversal algorithm). In this paper, we combine these two approaches and obtain a technique that uses templates in which the unknowns are functions rather than numbers, and the constraints on the unknowns are recurrences. The technique synthesizes invariants involving polynomials, exponentials, and logarithms, even in the presence of arbitrary control-flow, including any combination of loops, branches, and (possibly non-linear) recursion. For instance, it is able to show that (i) the time taken by merge-sort is O(n log(n)), and (ii) the time taken by Strassen’s algorithm is O(nlog2(7)).","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80364653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Data-driven inference of representation invariants 数据驱动的表示不变量推理

Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2020-03-26 DOI: 10.1145/3385412.3385967

Anders Miltner, Saswat Padhi, T. Millstein, D. Walker

{"title":"Data-driven inference of representation invariants","authors":"Anders Miltner, Saswat Padhi, T. Millstein, D. Walker","doi":"10.1145/3385412.3385967","DOIUrl":"https://doi.org/10.1145/3385412.3385967","url":null,"abstract":"A representation invariant is a property that holds of all values of abstract type produced by a module. Representation invariants play important roles in software engineering and program verification. In this paper, we develop a counterexample-driven algorithm for inferring a representation invariant that is sufficient to imply a desired specification for a module. The key novelty is a type-directed notion of visible inductiveness, which ensures that the algorithm makes progress toward its goal as it alternates between weakening and strengthening candidate invariants. The algorithm is parameterized by an example-based synthesis engine and a verifier, and we prove that it is sound and complete for first-order modules over finite types, assuming that the synthesizer and verifier are as well. We implement these ideas in a tool called Hanoi, which synthesizes representation invariants for recursive data types. Hanoi not only handles invariants for first-order code, but higher-order code as well. In its back end, Hanoi uses an enumerative synthesizer called Myth and an enumerative testing tool as a verifier. Because Hanoi uses testing for verification, it is not sound, though our empirical evaluation shows that it is successful on the benchmarks we investigated.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73766406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Learning nonlinear loop invariants with gated continuous logic networks 用门控连续逻辑网络学习非线性环不变量

Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2020-03-17 DOI: 10.1145/3385412.3385986

Jianan Yao, Gabriel Ryan, Justin Wong, S. Jana, Ronghui Gu

{"title":"Learning nonlinear loop invariants with gated continuous logic networks","authors":"Jianan Yao, Gabriel Ryan, Justin Wong, S. Jana, Ronghui Gu","doi":"10.1145/3385412.3385986","DOIUrl":"https://doi.org/10.1145/3385412.3385986","url":null,"abstract":"Verifying real-world programs often requires inferring loop invariants with nonlinear constraints. This is especially true in programs that perform many numerical operations, such as control systems for avionics or industrial plants. Recently, data-driven methods for loop invariant inference have shown promise, especially on linear loop invariants. However, applying data-driven inference to nonlinear loop invariants is challenging due to the large numbers of and large magnitudes of high-order terms, the potential for overfitting on a small number of samples, and the large space of possible nonlinear inequality bounds. In this paper, we introduce a new neural architecture for general SMT learning, the Gated Continuous Logic Network (G-CLN), and apply it to nonlinear loop invariant learning. G-CLNs extend the Continuous Logic Network (CLN) architecture with gating units and dropout, which allow the model to robustly learn general invariants over large numbers of terms. To address overfitting that arises from finite program sampling, we introduce fractional sampling—a sound relaxation of loop semantics to continuous functions that facilitates unbounded sampling on the real domain. We additionally design a new CLN activation function, the Piecewise Biased Quadratic Unit (PBQU), for naturally learning tight inequality bounds. We incorporate these methods into a nonlinear loop invariant inference system that can learn general nonlinear loop invariants. We evaluate our system on a benchmark of nonlinear loop invariants and show it solves 26 out of 27 problems, 3 more than prior work, with an average runtime of 53.3 seconds. We further demonstrate the generic learning ability of G-CLNs by solving all 124 problems in the linear Code2Inv benchmark. We also perform a quantitative stability evaluation and show G-CLNs have a convergence rate of 97.5% on quadratic problems, a 39.2% improvement over CLN models.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84308740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 38

Automatic generation of efficient sparse tensor format conversion routines 自动生成高效的稀疏张量格式转换例程

Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2020-01-08 DOI: 10.1145/3385412.3385963

Stephen Chou, Fredrik Kjolstad, Saman P. Amarasinghe

{"title":"Automatic generation of efficient sparse tensor format conversion routines","authors":"Stephen Chou, Fredrik Kjolstad, Saman P. Amarasinghe","doi":"10.1145/3385412.3385963","DOIUrl":"https://doi.org/10.1145/3385412.3385963","url":null,"abstract":"This paper shows how to generate code that efficiently converts sparse tensors between disparate storage formats (data layouts) such as CSR, DIA, ELL, and many others. We decompose sparse tensor conversion into three logical phases: coordinate remapping, analysis, and assembly. We then develop a language that precisely describes how different formats group together and order a tensor’s nonzeros in memory. This lets a compiler emit code that performs complex remappings of nonzeros when converting between formats. We also develop a query language that can extract statistics about sparse tensors, and we show how to emit efficient analysis code that computes such queries. Finally, we define an abstract interface that captures how data structures for storing a tensor can be efficiently assembled given specific statistics about the tensor. Disparate formats can implement this common interface, thus letting a compiler emit optimized sparse tensor conversion code for arbitrary combinations of many formats without hard-coding for any specific combination. Our evaluation shows that the technique generates sparse tensor conversion routines with performance between 1.00 and 2.01× that of hand-optimized versions in SPARSKIT and Intel MKL, two popular sparse linear algebra libraries. And by emitting code that avoids materializing temporaries, which both libraries need for many combinations of source and target formats, our technique outperforms those libraries by 1.78 to 4.01× for CSC/COO to DIA/ELL conversion.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84216839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

EVA: an encrypted vector arithmetic language and compiler for efficient homomorphic computation EVA:一个加密的矢量算术语言和编译器，用于高效的同态计算

Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-12-27 DOI: 10.1145/3385412.3386023

Roshan Dathathri, Blagovesta Kostova, Olli Saarikivi, Wei Dai, Kim Laine, Madan Musuvathi

{"title":"EVA: an encrypted vector arithmetic language and compiler for efficient homomorphic computation","authors":"Roshan Dathathri, Blagovesta Kostova, Olli Saarikivi, Wei Dai, Kim Laine, Madan Musuvathi","doi":"10.1145/3385412.3386023","DOIUrl":"https://doi.org/10.1145/3385412.3386023","url":null,"abstract":"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 5.3× 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.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84195217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 79

A study of the learnability of relational properties: model counting meets machine learning (MCML) 关系属性的可学习性研究:模型计数与机器学习(MCML)

Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-12-25 DOI: 10.1145/3385412.3386015

Muhammad Usman, Wenxi Wang, Kaiyuan Wang, Marko Vasic, H. Vikalo, S. Khurshid

{"title":"A study of the learnability of relational properties: model counting meets machine learning (MCML)","authors":"Muhammad Usman, Wenxi Wang, Kaiyuan Wang, Marko Vasic, H. Vikalo, S. Khurshid","doi":"10.1145/3385412.3386015","DOIUrl":"https://doi.org/10.1145/3385412.3386015","url":null,"abstract":"This paper introduces the MCML approach for empirically studying the learnability of relational properties that can be expressed in the well-known software design language Alloy. A key novelty of MCML is quantification of the performance of and semantic differences among trained machine learning (ML) models, specifically decision trees, with respect to entire (bounded) input spaces, and not just for given training and test datasets (as is the common practice). MCML reduces the quantification problems to the classic complexity theory problem of model counting, and employs state-of-the-art model counters. The results show that relatively simple ML models can achieve surprisingly high performance (accuracy and F1-score) when evaluated in the common setting of using training and test datasets -- even when the training dataset is much smaller than the test dataset -- indicating the seeming simplicity of learning relational properties. However, MCML metrics based on model counting show that the performance can degrade substantially when tested against the entire (bounded) input space, indicating the high complexity of precisely learning these properties, and the usefulness of model counting in quantifying the true performance.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77772013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

CacheQuery: learning replacement policies from hardware caches CacheQuery:从硬件缓存中学习替换策略

Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-12-20 DOI: 10.1145/3385412.3386008

Pepe Vila, P. Ganty, M. Guarnieri, Boris Köpf

引用次数: 34

Proving data-poisoning robustness in decision trees 证明决策树的数据中毒鲁棒性

Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-12-02 DOI: 10.1145/3385412.3385975

Samuel Drews, Aws Albarghouthi, Loris D'antoni

引用次数: 14

Automated derivation of parametric data movement lower bounds for affine programs 仿射程序参数数据移动下界的自动推导

Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-11-15 DOI: 10.1145/3385412.3385989

Auguste Olivry, J. Langou, L. Pouchet, P. Sadayappan, F. Rastello

{"title":"Automated derivation of parametric data movement lower bounds for affine programs","authors":"Auguste Olivry, J. Langou, L. Pouchet, P. Sadayappan, F. Rastello","doi":"10.1145/3385412.3385989","DOIUrl":"https://doi.org/10.1145/3385412.3385989","url":null,"abstract":"Researchers and practitioners have for long worked on improving the computational complexity of algorithms, focusing on reducing the number of operations needed to perform a computation. However the hardware trend nowadays clearly shows a higher performance and energy cost for data movements than computations: quality algorithms have to minimize data movements as much as possible. The theoretical operational complexity of an algorithm is a function of the total number of operations that must be executed, regardless of the order in which they will actually be executed. But theoretical data movement (or, I/O) complexity is fundamentally different: one must consider all possible legal schedules of the operations to determine the minimal number of data movements achievable, a major theoretical challenge. I/O complexity has been studied via complex manual proofs, e.g., refined from Ω(n3/√S) for matrix-multiply on a cache size S by Hong & Kung to 2n3/√S by Smith et al. While asymptotic complexity may be sufficient to compare I/O potential between broadly different algorithms, the accuracy of the reasoning depends on the tightness of these I/O lower bounds. Precisely, exposing constants is essential to enable precise comparison between different algorithms: for example the 2n3/√S lower bound allows to demonstrate the optimality of panel-panel tiling for matrix-multiplication. We present the first static analysis to automatically derive non-asymptotic parametric expressions of data movement lower bounds with scaling constants, for arbitrary affine computations. Our approach is fully automatic, assisting algorithm designers to reason about I/O complexity and make educated decisions about algorithmic alternatives.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84627310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Constant-time foundations for the new spectre era 新幽灵时代的恒定时间基础

Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-10-03 DOI: 10.1145/3385412.3385970

S. Cauligi, Craig Disselkoen, K. V. Gleissenthall, D. Stefan, Tamara Rezk, G. Barthe

{"title":"Constant-time foundations for the new spectre era","authors":"S. Cauligi, Craig Disselkoen, K. V. Gleissenthall, D. Stefan, Tamara Rezk, G. Barthe","doi":"10.1145/3385412.3385970","DOIUrl":"https://doi.org/10.1145/3385412.3385970","url":null,"abstract":"The constant-time discipline is a software-based countermeasure used for protecting high assurance cryptographic implementations against timing side-channel attacks. Constant-time is effective (it protects against many known attacks), rigorous (it can be formalized using program semantics), and amenable to automated verification. Yet, the advent of micro-architectural attacks makes constant-time as it exists today far less useful. This paper lays foundations for constant-time programming in the presence of speculative and out-of-order execution. We present an operational semantics and a formal definition of constant-time programs in this extended setting. Our semantics eschews formalization of microarchitectural features (that are instead assumed under adversary control), and yields a notion of constant-time that retains the elegance and tractability of the usual notion. We demonstrate the relevance of our semantics in two ways: First, by contrasting existing Spectre-like attacks with our definition of constant-time. Second, by implementing a static analysis tool, Pitchfork, which detects violations of our extended constant-time property in real world cryptographic libraries.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80061689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 68