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检索条件"任意字段=40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2019"

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pldi 2019 - Proceedings of the 40th acm sigplan conference on programming language design and implementation

PLDI 2019 - Proceedings of the 40th ACM SIGPLAN Conference o...

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40th acm sigplan conference on programming language design and implementation, pldi 2019

ISBN: (纸本)9781450367127

the proceedings contain 76 papers. the topics discussed include: data-trace types for distributed stream processing systems;cost analysis of nondeterministic probabilistic programs;mesh: compacting memory management for C/C++ applications;transactional concurrency control for intermittent, energy-harvesting computing systems;an applied quantum Hoare logic;Ignis: scaling distribution-oblivious systems with light-touch distribution;reusable inline caching for JavaScript performance;synthesizing database programs for schema refactoring;and lightweight multi-language syntax transformation with parser parser combinators.

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Incremental Precision-Preserving Symbolic Inference for Probabilistic Programs 2019

Incremental Precision-Preserving Symbolic Inference for Prob...

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40th acm sigplan conference on programming language design and implementation (pldi) part of acm's Federated Computing Research conference (FCRC)

作者： Zhang, Jieyuan Xue, Jingling UNSW Sydney Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9781450367127

We present ISymb, an incremental symbolic inference framework for probabilistic programs in situations when some loop-manipulated array data, upon which their probabilistic models are conditioned, undergoes small changes. To tackle the path explosion challenge, ISymb is intra-procedurally path-sensitive except that it conducts a "meet-over-all-paths" analysis within an iteration of a loop (conditioned on some observed array data). By recomputing only the probability distributions for the paths affected, ISymb avoids expensive symbolic inference from scratch while also being precision-preserving. Our evaluation with a set of existing benchmarks shows that ISymb can lead to orders of magnitude performance improvements compared to its non-incremental counterpart (under small changes in observed array data).

关键词： Probabilistic programming Symbolic Inference Incremental Computation Static analysis

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Reusable Inline Caching for JavaScript Performance 2019

Reusable Inline Caching for JavaScript Performance

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40th acm sigplan conference on programming language design and implementation (pldi) part of acm's Federated Computing Research conference (FCRC)

作者： Choi, Jiho Shull, thomas Torrellas, Josep Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781450367127

JavaScript performance is paramount to a user's browsing experience. Browser vendors have gone to great lengths to improve JavaScript's steady-state performance. this has led to sophisticated web applications. However, as users increasingly expect instantaneous page load times, another important goal for JavaScript engines is to attain minimal startup times. In this paper, we reduce the startup time of JavaScript programs by enhancing the reuse of compilation and optimization information across different executions. Specifically, we propose a new scheme to increase the startup performance of Inline Caching (IC), a key optimization for dynamic type systems. the idea is to represent a substantial portion of the IC information in an execution in a context-independent way, and reuse it in subsequent executions. We call our enhanced IC design Reusable Inline Caching (RIC). We integrate RIC into the state-of-the-art Google V8 JavaScript engine and measure its impact on the initialization time of popular JavaScript libraries. By recycling IC information collected from a previous execution, RIC reduces the average initialization time per library by 17%.

关键词： Inline Caching JavaScript Scripting language Dynamic Typing

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An Applied Quantum Hoare Logic 2019

An Applied Quantum Hoare Logic

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40th acm sigplan conference on programming language design and implementation (pldi) part of acm's Federated Computing Research conference (FCRC)

作者： Zhou, Li Yu, Nengkun Ying, Mingsheng Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Univ Technol Sydney CQSI FEIT Sydney NSW Australia Chinese Acad Sci Inst Software Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781450367127

We derive a variant of quantum Hoare logic (QHL), called applied quantum Hoare logic (aQHL for short), by: (1) restricting QHL to a special class of preconditions and postconditions, namely projections, which can significantly simplify verification of quantum programs and are much more convenient when used in debugging and testing;and (2) adding several rules for reasoning about robustness of quantum programs, i.e. error bounds of outputs. the effectiveness of aQHL is shown by its applications to verify two sophisticated quantum algorithms: HHL (Harrow-Hassidim-Lloyd) for solving systems of linear equations and qPCA (quantum Principal Component Analysis).

关键词： Quantum computation programming languages Hoare logic projections robustness

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Renaissance: Benchmarking Suite for Parallel Applications on the JVM 2019

Renaissance: Benchmarking Suite for Parallel Applications on...

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40th acm sigplan conference on programming language design and implementation (pldi) part of acm's Federated Computing Research conference (FCRC)

作者： Prokopec, Aleksandar Rosa, Andrea Leopoldseder, David Duboscq, Gilles Tuma, Petr Studener, Martin Bulej, Lubomir Zheng, Yudi Villazon, Alex Simon, Doug Wurthinger, thomas Binder, Walter Oracle Labs Zurich Switzerland Univ Svizzera Italiana Lugano Switzerland Johannes Kepler Univ Linz Linz Austria Charles Univ Prague Prague Czech Republic Univ Privada Boliviana Colcapirhua Bolivia

ISBN: (纸本)9781450367127

Established benchmark suites for the Java Virtual Machine (JVM), such as DaCapo, ScalaBench, and SPECjvm2008, lack workloads that take advantage of the parallel programming abstractions and concurrency primitives offered by the JVM and the Java Class Library. However, such workloads are fundamental for understanding the way in which modern applications and data-processing frameworks use the JVM's concurrency features, and for validating new just-in-time (JIT) compiler optimizations that enable more efficient execution of such workloads. We present Renaissance, a new benchmark suite composed of modern, real-world, concurrent, and object-oriented workloads that exercise various concurrency primitives of the JVM. We show that the use of concurrency primitives in these workloads reveals optimization opportunities that were not visible with the existing workloads. We use Renaissance to compare performance of two state-of-the-art, production-quality JIT compilers (HotSpot C2 and Graal), and show that the performance differences are more significant than on existing suites such as DaCapo and SPECjvm2008. We also use Renaissance to expose four new compiler optimizations, and we analyze the behavior of several existing ones. Evaluating these optimizations using four benchmark suites shows a more prominent impact on the Renaissance workloads than on those of other suites.

关键词： benchmarks JIT compilation parallelism concurrency JVM object-oriented programming benchmarks functional programming benchmarks Big Data benchmarks

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Gen: A General-Purpose Probabilistic programming System with Programmable Inference 2019

Gen: A General-Purpose Probabilistic Programming System with...

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40th acm sigplan conference on programming language design and implementation (pldi) part of acm's Federated Computing Research conference (FCRC)

作者： Cusumano-Towner, Marco F. Saad, Feras A. Lew, Alexander K. Mansinghka, Vikash K. MIT Cambridge MA 02139 USA

ISBN: (纸本)9781450367127

Although probabilistic programming is widely used for some restricted classes of statistical models, existing systems lack the flexibility and efficiency needed for practical use with more challenging models arising in fields like computer vision and robotics. this paper introduces Gen, a generalpurpose probabilistic programming system that achieves modeling flexibility and inference efficiency via several novel language constructs: (i) the generative function interface for encapsulating probabilistic models;(ii) interoperable modeling languages that strike different flexibility/efficiency tradeoffs;(iii) combinators that exploit common patterns of conditional independence;and (iv) an inference library that empowers users to implement efficient inference algorithms at a high level of abstraction. We show that Gen outperforms state-of-the-art probabilistic programming systems, sometimes by multiple orders of magnitude, on diverse problems including object tracking, estimating 3D body pose from a depth image, and inferring the structure of a time series.

关键词： Probabilistic programming Markov chain Monte Carlo sequential Monte Carlo variational inference

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Scalable Verification of Probabilistic Networks 2019

Scalable Verification of Probabilistic Networks

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40th acm sigplan conference on programming language design and implementation (pldi) part of acm's Federated Computing Research conference (FCRC)

作者： Smolka, Steffen Kumar, Praveen Kahn, David M. Foster, Nate Hsu, Justin Kozen, Dexter Silva, Alexandra Cornell Univ Ithaca NY 14850 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Wisconsin Madison WI USA UCL London England

ISBN: (纸本)9781450367127

this paper presents McNetKAT, a scalable tool for verifying probabilistic network programs. McNetKAT is based on a new semantics for the guarded and history-free fragment of Probabilistic NetKAT in terms of finite-state, absorbing Markov chains. this view allows the semantics of all programs to be computed exactly, enabling construction of an automatic verification tool. Domain-specific optimizations and a parallelizing backend enable McNetKAT to analyze networks with thousands of nodes, automatically reasoning about general properties such as probabilistic program equivalence and refinement, as well as networking properties such as resilience to failures. We evaluate McNetKAT's scalability using real-world topologies, compare its performance against state-of-the-art tools, and develop an extended case study on a recently proposed data center network design.

关键词： Network verification Probabilistic programming

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Genie: A Generator of Natural language Semantic Parsers for Virtual Assistant Commands 2019

Genie: A Generator of Natural Language Semantic Parsers for ...

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40th acm sigplan conference on programming language design and implementation (pldi) part of acm's Federated Computing Research conference (FCRC)

作者： Campagna, Giovanni Xu, Silei Moradshahi, Mehrad Socher, Richard Lam, Monica S. Stanford Univ Dept Comp Sci Stanford CA 94305 USA Salesforce Inc Palo Alto CA USA

ISBN: (纸本)9781450367127

To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. this paper presents a methodology and the Genie toolkit that can handle new compound commands with significantly less manual effort. We advocate formalizing the capability of virtual assistants with a Virtual Assistant programming language (VAPL) and using a neural semantic parser to translate natural language into VAPL code. Genie needs only a small realistic set of input sentences for validating the neural model. Developers write templates to synthesize data;Genie uses crowdsourced paraphrases and data augmentation, along with the synthesized data, to train a semantic parser. We also propose design principles that make VAPL languages amenable to natural language translation. We apply these principles to revise thingTalk, the language used by the Almond virtual assistant. We use Genie to build the first semantic parser that can support compound virtual assistants commands with unquoted free-form parameters. Genie achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's generality by showing a 19% and 31% improvement over the previous state of the art on a music skill, aggregate functions, and access control.

关键词： virtual assistants semantic parsing training data generation data augmentation data engineering

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Lightweight Multi-language Syntax Transformation with Parser Parser Combinators 2019

Lightweight Multi-Language Syntax Transformation with Parser...

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40th acm sigplan conference on programming language design and implementation (pldi) part of acm's Federated Computing Research conference (FCRC)

作者： van Tonder, Rijnard Le Goues, Claire Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA

ISBN: (纸本)9781450367127

Automatically transforming programs is hard, yet critical for automated program refactoring, rewriting, and repair. Multi-language syntax transformation is especially hard due to heterogeneous representations in syntax, parse trees, and abstract syntax trees (ASTs). Our insight is that the problem can be decomposed such that (1) a common grammar expresses the central context-free language (CFL) properties shared by many contemporary languages and (2) open extension points in the grammar allow customizing syntax (e.g., for balanced delimiters) and hooks in smaller parsers to handle language-specific syntax (e.g., for comments). Our key contribution operationalizes this decomposition using a Parser Parser combinator (PPC), a mechanism that generates parsers for matching syntactic fragments in source code by parsing declarative user-supplied templates. this allows our approach to detach from translating input programs to any particular abstract syntax tree representation, and lifts syntax rewriting to a modularly-defined parsing problem. A notable effect is that we skirt the complexity and burden of defining additional translation layers between concrete user input templates and an underlying abstract syntax representation. We demonstrate that these ideas admit efficient and declarative rewrite templates across 12 languages, and validate effectiveness of our approach by producing correct and desirable lightweight transformations on popular real-world projects (over 50 syntactic changes produced by our approach have been merged into 40+). Our declarative rewrite patterns require an order of magnitude less code compared to analog implementations in existing, language-specific tools.

关键词： syntax transformation parsers rewriting

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LoCal: A language for Programs Operating on Serialized Data 2019

LoCal: A Language for Programs Operating on Serialized Data

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40th acm sigplan conference on programming language design and implementation (pldi) part of acm's Federated Computing Research conference (FCRC)

作者： Vollmer, Michael Koparkar, Chaitanya Rainey, Mike Sakka, Laith Kulkarni, Milind Newton, Ryan R. Indiana Univ Comp Sci Bloomington IN 47405 USA Purdue Univ Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781450367127

In a typical data-processing program, the representation of data in memory is distinct from its representation in a serialized form on disk. the former has pointers and arbitrary, sparse layout, facilitating easy manipulation by a program, while the latter is packed contiguously, facilitating easy I/O. We propose a language, LoCal, to unify in-memory and serialized formats. LoCal extends a region calculus into a location calculus, employing a type system that tracks the byte-addressed layout of all heap values. We formalize LoCal and prove type safety, and show how LoCal programs can be inferred from unannotated source terms. We transform the existing Gibbon compiler to use LoCal as an intermediate language, with the goal of achieving a balance between code speed and data compactness by introducing just enough indirection into heap layouts, preserving the asymptotic complexity of traditional representations, but working with mostly or completely serialized data. We show that our approach yields significant performance improvement over prior approaches to operating on packed data, without abandoning idiomatic programming with recursive functions.

关键词： Region Calculus Compiler Optimization Data Encoding Tree Traversal

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