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检索条件"任意字段=ACM SIGPLAN 95 Conference on Programming Language Design and Implementation"

共 931 条记录，以下是151-160 订阅

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Proceedings of the acm sigplan conference on programming language design and implementation (PLDI)

Proceedings of the ACM SIGPLAN Conference on Programming Lan...

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1st acm sigplan International Workshop on Libraries, languages and Compilers for Array programming, ARRAY 2014 - Part of PLDI 2014

ISBN: (纸本)9781450329378

The proceedings contain 17 papers. The topics discussed include: exploiting implicit parallelism in dynamic array programming languages;NOVA: a functional language for data parallelism;abstracting vector architectures in library generators: case study convolution filters;just-in-time length specialization of dynamic vector code;a local-view array library for partitioned global address space C++ programs;GPGPU composition with OCaml;a composable array function interface for heterogeneous computing in java;just-in-time shape inference for array-based languages;array operators using multiple dispatch;Co-dfns: ancient language, modern compiler;extract and extend parallelism using C/C++ extension for array notation on multicore and many-core platforms - an empirical investigation with quantitative finance examples;on predicting the impact of resource redistributions in streaming applications;***: transformation-based code generation for GPUs and CPUs;and multi-target C code generation from MATLAB.

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From Datalog to FLIX: A Declarative language for Fixed Points on Lattices 16

From Datalog to FLIX: A Declarative Language for Fixed Point...

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37th acm sigplan conference on programming language design and implementation

作者： Madsen, Magnus Yee, Ming-Ho Lhotak, Ondrej Univ Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781450342612

We present FLIX, a declarative programming language for specifying and solving least fixed point problems, particularly static program analyses. FLIX is inspired by Datalog and extends it with lattices and monotone functions. Using FLIX, implementors of static analyses can express a broader range of analyses than is currently possible in pure Datalog, while retaining its familiar rule-based syntax. We define a model-theoretic semantics of FLIX as a natural extension of the Datalog semantics. This semantics captures the declarative meaning of FLIX programs without imposing any specific evaluation strategy. An efficient strategy is semi-naive evaluation which we adapt for FLIX. We have implemented a compiler and runtime for FLIX, and used it to express several well-known static analyses, including the IFDS and IDE algorithms. The declarative nature of FLIX clearly exposes the similarity between these two algorithms.

关键词： logic programming static analysis Datalog Logic programming Static Analysis

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Synthesizing Transformations on Hierarchically Structured Data 16

Synthesizing Transformations on Hierarchically Structured Da...

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37th acm sigplan conference on programming language design and implementation

作者： Yaghmazadeh, Navid Klinger, Christian Dillig, Isil Chaudhuri, Swarat Univ Texas Austin Austin TX 78712 USA Univ Freiburg Freiburg Germany Rice Univ Houston TX 77251 USA

ISBN: (纸本)9781450342612

This paper presents a new approach for synthesizing transformations on tree-structured data, such as Unix directories and XML documents. We consider a general abstraction for such data, called hierarchical data trees (HDTs) and present a novel example-driven synthesis algorithm for HDT transformations. Our central insight is to reduce the problem of synthesizing tree transformers to the synthesis of list transformations that are applied to the paths of the tree. The synthesis problem over lists is solved using a new algorithm that combines SMT solving and decision tree learning. We have implemented our technique in a system called HADES and show that HADES can automatically synthesize a variety of interesting transformations collected from online forums.

关键词： languages Program synthesis programming by example data transformations

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Temporal NetKAT 16

Temporal NetKAT

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37th acm sigplan conference on programming language design and implementation

作者： Beckett, Ryan Greenberg, Michael Walker, David Princeton Univ Princeton NJ 08544 USA Pomona Coll Claremont CA 91711 USA

ISBN: (纸本)9781450342612

Over the past 5-10 years, the rise of software-defined networking (SDN) has inspired a wide range of new systems, libraries, hypervisors and languages for programming, monitoring, and debugging network behavior. Oftentimes, these systems are disjoint-one language for programming and another for verification, and yet another for run-time monitoring and debugging. In this paper, we present a new, unified framework, called Temporal NetKAT, capable of facilitating all of these tasks at once. As its name suggests, Temporal NetKAT is the synthesis of two formal theories: past-time (finite trace) linear temporal logic and (network) Kleene Algebra with Tests. Temporal predicates allow programmers to write down concise properties of a packet's path through the network and to make dynamic packet-forwarding, access control or debugging decisions on that basis. In addition to being useful for programming, the combined equational theory of LTL and NetKAT facilitates proofs of pathbased correctness properties. Using new, general, proof techniques, we show that the equational semantics is sound with respect to the denotational semantics, and, for a class of programs we call network-wide programs, complete. We have also implemented a compiler for temporal NetKAT, evaluated its performance on a range of benchmarks, and studied the effectiveness of several optimizations.

关键词： Software-defined networking Network programming languages Domain-specific languages Kleene algebra with tests NetKAT Temporal logic

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Program Synthesis from Polymorphic Refinement Types 16

Program Synthesis from Polymorphic Refinement Types

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37th acm sigplan conference on programming language design and implementation

作者： Polikarpova, Nadia Kuraj, Ivan Solar-Lezama, Armando MIT CSAIL Cambridge MA USA

ISBN: (纸本)9781450342612

We present a method for synthesizing recursive functions that provably satisfy a given specification in the form of a polymorphic refinement type. We observe that such specifications are particularly suitable for program synthesis for two reasons. First, they offer a unique combination of expressive power and decidability, which enables automatic verification-and hence synthesis-of nontrivial programs. Second, a type-based specification for a program can often be effectively decomposed into independent specifications for its components, causing the synthesizer to consider fewer component combinations and leading to a combinatorial reduction in the size of the search space. At the core of our synthesis procedure is a newalgorithm for refinement type checking, which supports specification decomposition. We have evaluated our prototype implementation on a large set of synthesis problems and found that it exceeds the state of the art in terms of both scalability and usability. The tool was able to synthesize more complex programs than those reported in prior work (several sorting algorithms and operations on balanced search trees), as well as most of the benchmarks tackled by existing synthesizers, often starting from a more concise and intuitive user input.

关键词： languages Verification Program Synthesis Functional programming Refinement Types Predicate Abstraction

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A Distributed OpenCL Framework using Redundant Computation and Data Replication 16

A Distributed OpenCL Framework using Redundant Computation a...

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37th acm sigplan conference on programming language design and implementation

作者： Kim, Junghyun Jo, Gangwon Jung, Jaehoon Kim, Jungwon Lee, Jaejin Seoul Natl Univ Ctr Manycore Programming Dept Comp Sci & Engn Seoul 08826 South Korea

ISBN: (纸本)9781450342612

Applications written solely in OpenCL or CUDA cannot execute on a cluster as a whole. Most previous approaches that extend these programming models to clusters are based on a common idea: designating a centralized host node and coordinating the other nodes with the host for computation. However, the centralized host node is a serious performance bottleneck when the number of nodes is large. In this paper, we propose a scalable and distributed OpenCL framework called SnuCL-D for large-scale clusters. SnuCL-D's remote device virtualization provides an OpenCL application with an illusion that all compute devices in a cluster are confined in a single node. To reduce the amount of control-message and data communication between nodes, SnuCL-D replicates the OpenCL host program execution and data in each node. We also propose a new OpenCL host API function and a queueing optimization technique that significantly reduce the overhead incurred by the previous centralized approaches. To show the effectiveness of SnuCL-D, we evaluate SnuCL-D with a microbenchmark and eleven benchmark applications on a large-scale CPU cluster and a medium-scale GPU cluster.

关键词： OpenCL clusters heterogeneous computing programming models runtime systems redundant computation data replication

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Higher-Order and Tuple-Based Massively-Parallel Prefix Sums 16

Higher-Order and Tuple-Based Massively-Parallel Prefix Sums

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37th acm sigplan conference on programming language design and implementation

作者： Maleki, Sepideh Yang, Annie Burtscher, Martin Texas State Univ Dept Comp Sci San Marcos TX 78666 USA

ISBN: (纸本)9781450342612

Prefix sums are an important parallel primitive, especially in massively-parallel programs. This paper discusses two orthogonal generalizations thereof, which we call higher-order and tuple-based prefix sums. Moreover, it describes and evaluates SAM, a GPU-friendly algorithm for computing prefix sums and other scans that directly supports higher orders and tuple values. Its templated CUDA implementation unifies all of these computations in a single 100-statement kernel. SAM is communication-efficient in the sense that it minimizes main-memory accesses. When computing prefix sums of a million or more values, it outperforms Thrust and CUDPP on both a Titan X and a K40 GPU. On the Titan X, SAM reaches memory-copy speeds for large input sizes, which cannot be surpassed. SAM outperforms CUB, the currently fastest conventional prefix sum implementation, by up to a factor of 2.9 on eighth-order prefix sums and by up to a factor of 2.6 on eight-tuple prefix sums.

关键词： Algorithms Performance design Higher-order prefix sums tuple-based prefix sums carry propagation GPU programming

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Occurrence Typing Modulo Theories 16

Occurrence Typing Modulo Theories

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37th acm sigplan conference on programming language design and implementation

作者： Kent, Andrew M. Kempe, David Tobin-Hochstadt, Sam Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9781450342612

We present a new type system combining occurrence typing- a technique previously used to type check programs in dynamically-typed languages such as Racket, Clojure, and JavaScript-with dependent refinement types. We demonstrate that the addition of refinement types allows the integration of arbitrary solver-backed reasoning about logical propositions from external theories. By building on occurrence typing, we can add our enriched type system as a natural extension of Typed Racket, reusing its core while increasing its expressiveness. The result is a well-tested type system with a conservative, decidable core in which types may depend on a small but extensible set of program terms. In addition to describing our design, we present the following: a formal model and proof of correctness;a strategy for integrating new theories, with specific examples including linear arithmetic and bitvectors;and an evaluation in the context of the full Typed Racket implementation. Specifically, we take safe vector operations as a case study, examining all vector accesses in a 56,000 line corpus of Typed Racket programs. Our system is able to prove that 50% of these are safe with no new annotations, and with a few annotations and modifications we capture more than 70%.

关键词： languages design Verification Refinement types occurrence typing Racket

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Event-Driven Network programming 16

Event-Driven Network Programming

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37th acm sigplan conference on programming language design and implementation

作者： McClurg, Jedidiah Hojjat, Hossein Foster, Nate Cerny, Pavol CU Boulder Boulder CO 80309 USA Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781450342612

Software-defined networking (SDN) programs must simultaneously describe static forwarding behavior and dynamic updates in response to events. Event-driven updates are critical to get right, but difficult to implement correctly due to the high degree of concurrency in networks. Existing SDN platforms offer weak guarantees that can break application invariants, leading to problems such as dropped packets, degraded performance, security violations, etc. This paper introduces event-driven consistent updates that are guaranteed to preserve well-defined behaviors when transitioning between configurations in response to events. We propose network event structures (NESs) to model constraints on updates, such as which events can be enabled simultaneously and causal dependencies between events. We define an extension of the NetKAT language with mutable state, give semantics to stateful programs using NESs, and discuss provably-correct strategies for implementing NESs in SDNs. Finally, we evaluate our approach empirically, demonstrating that it gives well-defined consistency guarantees while avoiding expensive synchronization and packet buffering.

关键词： network update consistent update event structure software-defined networking SDN NetKAT

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Latte: A language, Compiler, and Runtime for Elegant and Efficient Deep Neural Networks 16

Latte: A Language, Compiler, and Runtime for Elegant and Eff...

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37th acm sigplan conference on programming language design and implementation

作者： Truong, Leonard Barik, Rajkishore Totoni, Ehsan Liu, Hai Markley, Chick Fox, Armando Shpeisman, Tatiana Univ Calif Berkeley Intel Labs Berkeley CA USA Intel Labs Berkeley CA USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781450342612

Deep neural networks (DNNs) have undergone a surge in popularity with consistent advances in the state of the art for tasks including image recognition, natural language processing, and speech recognition. The computationally expensive nature of these networks has led to the proliferation of implementations that sacrifice abstraction for high performance. In this paper, we present Latte, a domain-specific language for DNNs that provides a natural abstraction for specifying new layers without sacrificing performance. Users of Latte express DNNs as ensembles of neurons with connections between them. The Latte compiler synthesizes a program based on the user specification, applies a suite of domain-specific and general optimizations, and emits efficient machine code for heterogeneous architectures. Latte also includes a communication runtime for distributed memory data-parallelism. Using networks described using Latte, we demonstrate 3-6x speedup over Caffe (C++/MKL) on the three state-of-the-art ImageNet models executing on an Intel Xeon E5-2699 v3 x86 CPU.

关键词： Deep Learning Neural Networks Domain Specific language Compiler Optimization

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