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检索条件"任意字段=Proceedings of the ACM Sigplan 2003 Conference on Programming Language Design and Implementation"

共 1292 条记录，以下是131-140 订阅

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Inferring Type Rules for Syntactic Sugar 2018

Inferring Type Rules for Syntactic Sugar

引用

39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Pombrio, Justin Krishnamurthi, Shriram Brown Univ Providence RI 02912 USA

ISBN: (纸本)9781450356985

Type systems and syntactic sugar are both valuable to programmers, but sometimes at odds. While sugar is a valuable mechanism for implementing realistic languages, the expansion process obscures program source structure. As a result, type errors can reference terms the programmers did not write (and even constructs they do not know), baffling them. The language developer must also manually construct type rules for the sugars, to give a typed account of the surface language. We address these problems by presenting a process for automatically reconstructing type rules for the surface language using rules for the core. We have implemented this theory, and show several interesting case studies.

关键词： programming languages Syntactic Sugar Macros Type Systems Resugaring

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Bayonet: Probabilistic Inference for Networks 2018

Bayonet: Probabilistic Inference for Networks

引用

39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Gehr, Timon Misailovic, Sasa Tsankov, Petar Vanbever, Laurent Wiesmann, Pascal Vechev, Martin Swiss Fed Inst Technol Zurich Switzerland UIUC Champaign IL USA

ISBN: (纸本)9781450356985

Network operators often need to ensure that important probabilistic properties are met, such as that the probability of network congestion is below a certain threshold. Ensuring such properties is challenging and requires both a suitable language for probabilistic networks and an automated procedure for answering probabilistic inference queries. We present BAYONET, a novel approach that consists of: (i) a probabilistic network programming language and (ii) a system that performs probabilistic inference on BAYONET programs. The key insight behind BAYONET is to phrase the problem of probabilistic network reasoning as inference in existing probabilistic languages. As a result, BAYONET directly leverages existing probabilistic inference systems and offers a flexible and expressive interface to operators. We present a detailed evaluation of BAYONET on common network scenarios, such as network congestion, reliability of packet delivery, and others. Our results indicate that BAYONET can express such practical scenarios and answer queries for realistic topology sizes (with up to 30 nodes).

关键词： Probabilistic programming Computer Networks

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Probabilistic programming with Programmable Inference 2018

Probabilistic Programming with Programmable Inference

引用

39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Mansinghka, Vikash K. Schaechtle, Ulrich Handa, Shivam Radul, Alexey Chen, Yutian Rinard, Martin MIT Cambridge MA 02139 USA Google DeepMind London England

ISBN: (纸本)9781450356985

We introduce inference metaprogramming for probabilistic programming languages, including new language constructs, a formalism, and the first demonstration of effectiveness in practice. Instead of relying on rigid black-box inference algorithms hard-coded into the language implementation as in previous probabilistic programming languages, inference metaprogramming enables developers to 1) dynamically decompose inference problems into subproblems, 2) apply inference tactics to subproblems, 3) alternate between incorporating new data and performing inference over existing data, and 4) explore multiple execution traces of the probabilistic program at once. Implemented tactics include gradient-based optimization, Markov chain Monte Carlo, variational inference, and sequental Monte Carlo techniques. Inference metaprogramming enables the concise expression of probabilistic models and inference algorithms across diverse fields, such as computer vision, data science, and robotics, within a single probabilistic programming language.

关键词： Probabilistic programming Semantics Inference

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Search, Align, and Repair: Data-Driven Feedback Generation for Introductory programming Exercises 2018

Search, Align, and Repair: Data-Driven Feedback Generation f...

引用

39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Wang, Ke Singh, Rishabh Su, Zhendong Univ Calif Davis Davis CA 95616 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781450356985

This paper introduces the "Search, Align, and Repair" data-driven program repair framework to automate feedback generation for introductory programming exercises. Distinct from existing techniques, our goal is to develop an efficient, fully automated, and problem-agnostic technique for large or MOOC-scale introductory programming courses. We leverage the large amount of available student submissions in such settings and develop new algorithms for identifying similar programs, aligning correct and incorrect programs, and repairing incorrect programs by finding minimal fixes. We have implemented our technique in the SARFGEN system and evaluated it on thousands of real student attempts from the Microsoft-DEV204.1x edX course and the Microsoft Code-Hunt platform. Our results show that SARFGEN can, within two seconds on average, generate concise, useful feedback for 89.7% of the incorrect student submissions. It has been integrated with the Microsoft-DEV204.1X edX class and deployed for production use.

关键词： Automatic Grading Computer-Aided Education Program Analysis

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Automated Clustering and Program Repair for Introductory programming Assignments 2018

Automated Clustering and Program Repair for Introductory Pro...

引用

39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Gulwani, Sumit Radicek, Ivan Zuleger, Florian Microsoft Corp Redmond WA 98052 USA TU Wien Vienna Austria

ISBN: (纸本)9781450356985

Providing feedback on programming assignments is a tedious task for the instructor, and even impossible in large Massive Open Online Courses with thousands of students. Previous research has suggested that program repair techniques can be used to generate feedback in programming education. In this paper, we present a novel fully automated program repair algorithm for introductory programming assignments. The key idea of the technique, which enables automation and scalability, is to use the existing correct student solutions to repair the incorrect attempts. We evaluate the approach in two experiments: (I) We evaluate the number, size and quality of the generated repairs on 4,293 incorrect student attempts from an existing MOOC. We find that our approach can repair 97% of student attempts, while 81% of those are small repairs of good quality. (II) We conduct a preliminary user study on performance and repair usefulness in an interactive teaching setting. We obtain promising initial results (the average usefulness grade 3.4 on a scale from 1 to 5), and conclude that our approach can be used in an interactive setting.

关键词： programming education MOOC dynamic analysis program repair clustering

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Incremental Inference for Probabilistic Programs 2018

Incremental Inference for Probabilistic Programs

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39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Cusumano-Towner, Marco Bichsel, Benjamin Gehr, Timon Vechev, Martin Mansinghka, Vikash K. MIT 77 Massachusetts Ave Cambridge MA 02139 USA Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781450356985

We present a novel approach for approximate sampling in probabilistic programs based on incremental inference. The key idea is to adapt the samples for a program P into samples for a program Q, thereby avoiding the expensive sampling computation for program Q. To enable incremental inference in probabilistic programming, our work: (i) introduces the concept of a trace translator which adapts samples from P into samples of Q, (ii) phrases this translation approach in the context of sequential Monte Carlo (SMC), which gives theoretical guarantees that the adapted samples converge to the distribution induced by Q, and (iii) shows how to obtain a concrete trace translator by establishing a correspondence between the random choices of the two probabilistic programs. We implemented our approach in two different probabilistic programming systems and showed that, compared to methods that sample the program Q from scratch, incremental inference can lead to orders of magnitude increase in efficiency, depending on how closely related P and Q are.

关键词： Probabilistic programming sequential Monte Carlo incremental computation

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Spatial: A language and Compiler for Application Accelerators 2018

Spatial: A Language and Compiler for Application Accelerator...

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39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Koeplinger, David Feldman, Matthew Prabhakar, Raghu Zhang, Yaqi Hadjis, Stefan Fiszel, Ruben Zhao, Tian Nardi, Luigi Pedram, Ardavan Kozyrakis, Christos Olukotun, Kunle Stanford Univ Stanford CA 94305 USA Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781450356985

Industry is increasingly turning to reconfigurable architectures like FPGAs and CGRAs for improved performance and energy efficiency. Unfortunately, adoption of these architectures has been limited by their programming models. HDLs lack abstractions for productivity and are difficult to target from higher level languages. HLS tools are more productive, but offer an ad-hoc mix of software and hardware abstractions which make performance optimizations difficult. In this work, we describe a new domain-specific language and compiler called Spatial for higher level descriptions of application accelerators. We describe Spatial's hardware-centric abstractions for both programmer productivity and design performance, and summarize the compiler passes required to support these abstractions, including pipeline scheduling, automatic memory banking, and automated design tuning driven by active machine learning. We demonstrate the language's ability to target FPGAs and CGRAs from common source code. We show that applications written in Spatial are, on average, 42% shorter and achieve a mean speedup of 2.9x over SDAccel HLS when targeting a Xilinx UltraScale+ VU9P FPGA on an Amazon EC2 F1 instance.

关键词： domain-specific languages compilers hardware accelerators high-level synthesis reconfigurable architectures FPGAs CGRAs

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Bounded Expectations: Resource Analysis for Probabilistic Programs 2018

Bounded Expectations: Resource Analysis for Probabilistic Pr...

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39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Van Chan Ngo Carbonneaux, Quentin Hoffmann, Jan Carnegie Mellon Univ Pittsburgh PA 15213 USA Yale Univ New Haven CT 06520 USA

ISBN: (纸本)9781450356985

This paper presents a new static analysis for deriving upper bounds on the expected resource consumption of probabilistic programs. The analysis is fully automatic and derives symbolic bounds that are multivariate polynomials in the inputs. The new technique combines manual state-of-the-art reasoning techniques for probabilistic programs with an effective method for automatic resource-bound analysis of deterministic programs. It can be seen as both, an extension of automatic amortized resource analysis (AARA) to probabilistic programs and an automation of manual reasoning for probabilistic programs that is based on weakest preconditions. An advantage of the technique is that it combines the clarity and compositionality of a weakest-precondition calculus with the efficient automation of AARA. As a result, bound inference can be reduced to off-the-shelf LP solving in many cases and automatically-derived bounds can be interactively extended with standard program logics if the automation fails. Building on existing work, the soundness of the analysis is proved with respect to an operational semantics that is based on Markov decision processes. The effectiveness of the technique is demonstrated with a prototype implementation that is used to automatically analyze 39 challenging probabilistic programs and randomized algorithms. Experiments indicate that the derived constant factors in the bounds are very precise and even optimal for some programs.

关键词： probabilistic programming resource bound analysis static analysis

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Heartbeat Scheduling: Provable Efficiency for Nested Parallelism 2018

Heartbeat Scheduling: Provable Efficiency for Nested Paralle...

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39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Acar, Umut A. Chargueraud, Arthur Guatto, Adrien Rainey, Mike Sieczkowski, Filip Carnegie Mellon Univ Pittsburgh PA 15213 USA INRIA Rocquencourt France Univ Strasbourg ICube Strasbourg France CREST Bloomington IN USA

ISBN: (纸本)9781450356985

A classic problem in parallel computing is to take a high-level parallel program written, for example, in nested-parallel style with fork-join constructs and run it efficiently on a real machine. The problem could be considered solved in theory, but not in practice, because the overheads of creating and managing parallel threads can overwhelm their benefits. Developing efficient parallel codes therefore usually requires extensive tuning and optimizations to reduce parallelism just to a point where the overheads become acceptable. In this paper, we present a scheduling technique that delivers provably efficient results for arbitrary nested-parallel programs, without the tuning needed for controlling parallelism overheads. The basic idea behind our technique is to create threads only at a beat (which we refer to as the "heartbeat") and make sure to do useful work in between. We specify our heartbeat scheduler using an abstract-machine semantics and provide mechanized proofs that the scheduler guarantees low overheads for all nested parallel programs. We present a prototype C++ implementation and an evaluation that shows that Heartbeat competes well with manually optimized Cilk Plus codes, without requiring manual tuning.

关键词： parallel programming languages granularity control

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MixT: A language for Mixing Consistency in Geodistributed Transactions 2018

MixT: A Language for Mixing Consistency in Geodistributed Tr...

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39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Milano, Matthew Myers, Andrew C. Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

ISBN: (纸本)9781450356985

programming concurrent, distributed systems is hard-especially when these systems mutate shared, persistent state replicated at geographic scale. To enable high availability and scalability, a new class of weakly consistent data stores has become popular. However, some data needs strong consistency. To manipulate both weakly and strongly consistent data in a single transaction, we introduce a new abstraction: mixed-consistency transactions, embodied in a new embedded language, MixT. Programmers explicitly associate consistency models with remote storage sites;each atomic, isolated transaction can access a mixture of data with different consistency models. Compile-time information-flow checking, applied to consistency models, ensures that these models are mixed safely and enables the compiler to automatically partition transactions. New run-time mechanisms ensure that consistency models can also be mixed safely, even when the data used by a transaction resides on separate, mutually unaware stores. Performance measurements show that despite their stronger guarantees, mixed-consistency transactions retain much of the speed of weak consistency, significantly outperforming traditional serializable transactions.

关键词： Consistency Transactions Information Flow

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