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检索条件"任意字段=Proceedings of the ACM SIGPLAN 1991 conference on Programming language design and implementation"

共 1210 条记录，以下是121-130 订阅

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Compiling KB-Sized Machine Learning Models to Tiny IoT Devices 2019

Compiling KB-Sized Machine Learning Models to Tiny IoT Devic...

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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： Gopinath, Sridhar Ghanathe, Nikhil Seshadri, Vivek Sharma, Rahul Microsoft Res Bengaluru India

ISBN: (纸本)9781450367127

Recent advances in machine learning (ML) have produced KiloByte-size models that can directly run on constrained IoT devices. This approach avoids expensive communication between IoT devices and the cloud, thereby enabling energy-efficient real-time analytics. However, ML models are expressed typically in floating-point, and IoT hardware typically does not support floating-point. Therefore, running these models on IoT devices requires simulating IEEE-754 floating-point using software, which is very inefficient. We present SeeDot, a domain-specific language to express ML inference algorithms and a compiler that compiles SeeDot programs to fixed-point code that can efficiently run on constrained IoT devices. We propose 1) a novel compilation strategy that reduces the search space for some key parameters used in the fixed-point code, and 2) new efficient implementations of expensive operations. SeeDot compiles state-of-the-art KB-sized models to various microcontrollers and low-end FPGAs. We show that SeeDot outperforms 1) software emulation of floating-point (Arduino), 2) high-bitwidth fixed-point (MATLAB), 3) post-training quantization (TensorFlow-Lite), and 4) floating- and fixed-point FPGA implementations generated using high-level synthesis tools.

关键词： Machine Learning IoT device programming language Compiler Fixed-point Microcontroller FPGA

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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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SEMCLUSTER: Clustering of Imperative programming Assignments Based on Quantitative Semantic Features 2019

SEMCLUSTER: Clustering of Imperative Programming Assignments...

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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： Perry, David M. Kim, Dohyeong Samanta, Roopsha Zhang, Xiangyu Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781450367127

A fundamental challenge in automated reasoning about programming assignments at scale is clustering student submissions based on their underlying algorithms. State-of-the-art clustering techniques are sensitive to control structure variations, cannot cluster buggy solutions with similar correct solutions, and either require expensive pair-wise program analyses or training efforts. We propose a novel technique that can cluster small imperative programs based on their algorithmic essence: (A) how the input space is partitioned into equivalence classes and (B) how the problem is uniquely addressed within individual equivalence classes. We capture these algorithmic aspects as two quantitative semantic program features that are merged into a program's vector representation. Programs are then clustered using their vector representations. The computation of our first semantic feature leverages model counting to identify the number of inputs belonging to an input equivalence class. The computation of our second semantic feature abstracts the program's data flow by tracking the number of occurrences of a unique pair of consecutive values of a variable during its lifetime. The comprehensive evaluation of our tool SemCluster on benchmarks drawn from solutions to small programming assignments shows that SemCluster (1) generates far fewer clusters than other clustering techniques, (2) precisely identifies distinct solution strategies, and (3) boosts the performance of clustering-based program repair, all within a reasonable amount of time.

关键词： Program clustering Program analysis Quantitative reasoning

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ARRAY 2019 - proceedings of the 6th acm sigplan International Workshop on Libraries, languages and Compilers for Array programming, co-located with PLDI 2019

ARRAY 2019 - Proceedings of the 6th ACM SIGPLAN Internationa...

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6th acm sigplan International Workshop on Libraries, languages and Compilers for Array programming, ARRAY 2019, co-located with PLDI 2019

ISBN: (纸本)9781450367172

The proceedings contain 9 papers. The topics discussed include: finite difference methods fengshui: alignment through a mathematics of arrays;data-parallel flattening by expansion;ALPyNA: acceleration of loops in python for novel architectures;high-level synthesis of functional patterns with lift;toward generalized tensor algebra for ab initio quantum chemistry methods;TeIL: a type-safe imperative tensor intermediate language;convolutional neural networks in APL;records with rank polymorphism;and linear algebraic depth-first search. © 2019 Association for Computing Machinery. All rights reserved.

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programming Support for Autonomizing Software 2019

Programming Support for Autonomizing Software

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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： Lee, Wen-Chuan Liu, Peng Liu, Yingqi Ma, Shiqing Zhang, Xiangyu Purdue Univ W Lafayette IN 47907 USA IBM Res San Jose CA USA

ISBN: (纸本)9781450367127

Most traditional software systems are not built with the artificial intelligence support (AI) in mind. Among them, some may require human interventions to operate, e.g., the manual specification of the parameters in the data processing programs, or otherwise, would behave poorly. We propose a novel framework called Autonomizer to autonomize these systems by installing the AI into the traditional programs. Autonomizer is general so it can be applied to many real-world applications. We provide the primitives and the runtime support, where the primitives abstract common tasks of autonomization and the runtime support realizes them transparently. With the support of Autonomizer, the users can gain the AI support with little engineering efforts. Like many other AI applications, the challenge lies in the feature selection, which we address by proposing multiple automated strategies based on the program analysis. Our experiment results on nine real-world applications show that the autonomization only requires adding a few lines to the source code. Besides, for the data-processing programs, Autonomizer improves the output quality by 161% on average over the default settings. For the interactive programs such as game/driving, Autonomizer achieves higher success rate with lower training time than existing autonomized programs.

关键词： AI Deep Learning Software Autonomization Dynamic Program Analysis

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Piranha: Reducing Feature Flag Debt at Uber 42

Piranha: Reducing Feature Flag Debt at Uber

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42nd acm/IEEE International conference on Software Engineering - Companion proceedings (ICSE-Companion) / 42nd acm/IEEE International conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP)

作者： Ramanathan, Murali Krishna Clapp, Lazaro Barik, Rajkishore Sridharan, Manu Uber Technol Inc New York NY 10001 USA Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781450371230

Feature flags are commonly used in mobile app development and can introduce technical debt related to deleting their usage from the codebase. This can adversely affect the overall reliability of the apps and increase their maintenance complexity. Reducing this debt without imposing additional overheads on the developers necessitates the design of novel tools and automated workflows. In this paper, we describe the design and implementation of PIRANHA, an automated code refactoring tool which is used to automatically generate differential revisions (a.k.a diffs) to delete code corresponding to stale feature flags. PIRANHA takes as input the name of the flag, expected treatment behavior, and the name of the flag's author. It analyzes the ASTs of the program to generate appropriate refactorings which are packaged into a diff. The diff is assigned to the author of the flag for further processing, who can land it after performing any additional refactorings. We have implemented PIRANHA to delete code in Objective-C, Java, and Swift programs, and deployed it to handle stale flags in multiple Uber apps. We present our experiences with the deployment of PIRANHA from Dec 2017 to May 2019, including the following highlights: (a) generated code cleanup diffs for 1381 flags (17% of total flags), (b) 65% of the diffs landed without any changes, (c) over 85% of the generated diffs compile and pass tests successfully, (d) around 80% of the diffs affect more than one file, (e) developers process more than 88% of the generated diffs, (f) 75% of the generated diffs are processed within a week, and (g) PIRANHA diffs have been interacted with by similar to 200 developers across Uber. Piranha is available as open source at https://***/uber/piranha.

关键词： C (programming language)

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Demo: Light-weight programming language for blockchain 17

Demo: Light-weight programming language for blockchain

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17th acm International conference on Mobile Systems, Applications, and Services, MobiSys 2019

作者： Kim, Junhui Kim, Joongheon Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781450366618

This demo abstract introduces a new light-weight programming language koa which is suitable for blockchain system design and implementation. In this abstract, the basic features of koa are introduced including working system (with playground), architecture, and virtual machine operations. Rum-time execution of software implemented by koa will be presented during the session. © 2019 Copyright held by the owner/author(s).

关键词： Smart contract

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Inferring Type Rules for Syntactic Sugar 2018

Inferring Type Rules for Syntactic Sugar

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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

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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

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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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