20,417 research outputs found

    Data parallel string manipulating programs

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    String-manipulating programs are an important class of programs with applications in malware detection, graphics, input sanitization for Web security, and large-scale HTML processing. This paper extends prior work on BEK, an expressive domain-specific language for writing string-manipulating programs, with algorithmic insights that make BEK both analyzable and data-parallel. By analyzable we mean that unlike most general purpose programming languages, many algebraic properties of a BEK program are decidable (i.e., one can check whether two programs commute or compute the inverse of a program). By data-parallel we mean that a BEK program can compute on arbitrary subsections of its input in parallel, thus exploiting parallel hardware. This latter requirement is particularly important for programs which operate on large data: without data parallelism, a programmer cannot hide the latency of reading data from various storage media (i.e., reading a terabyte of data from a modern hard drive takes about 3 hours). With a data-parallel approach, the system can split data across multiple disks and thus hide the latency of reading the data. A BEK program is expressive: a programmer can use conditionals, switch statements, and registers--or local variables--in order to implement common string-manipulating programs. Unfortunately, this expressivity induces data dependencies, which are an obstacle to parallelism. The key contribution of this paper is an algorithm which automatically removes these data dependencies by mapping a B EK program into a intermediate format consisting of symbolic transducers, which extend classical transducers with symbolic predicates and symbolic assignments. We present a novel algorithm that we call exploration which performs symbolic loop unrolling of these transducers to obtain simplified versions of the original program. We show how these simplified versions can then be lifted to a stateless form, and from there compiled to data-parallel hardware. To evaluate the efficacy of our approach, we demonstrate up to 8x speedups for a number of real-world, BEK programs, (e.g., HTML encoder and decoder) on data-parallel hardware. To the best of our knowledge, these are the first data parallel implementation of these programs. To validate that our approach is correct, we use an automatic testing technique to compare our generated code to the original implementations and find no semantic deviations

    Expressiveness of Streaming String Transducers

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    Streaming string transducers define (partial) functions from input strings to output strings. A streaming string transducer makes a single pass through the input string and uses a finite set of variables that range over strings from the output alphabet. At every step, the transducer processes an input symbol, and updates all the variables in parallel using assignments whose right-hand-sides are concatenations of output symbols and variables with the restriction that a variable can be used at most once in a right-hand-side expression. It has been shown that streaming string transducers operating on strings over infinite data domains are of interest in algorithmic verification of list-processing programs, as they lead to Pspace decision procedures for checking pre/postconditions and for checking semantic equivalence, for a well-defined class of heap-manipulating programs. In order to understand the theoretical expressiveness of streaming transducers, we focus on streaming transducers processing strings over finite alphabets, given the existence of a robust and well-studied class of ``regular\u27\u27 transductions for this case. Such regular transductions can be defined either by two-way deterministic finite-state transducers, or using a logical MSO-based characterization. Our main result is that the expressiveness of streaming string transducers coincides exactly with this class of regular transductions

    LIPIcs

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    Streaming string transducers [1] define (partial) functions from input strings to output strings. A streaming string transducer makes a single pass through the input string and uses a finite set of variables that range over strings from the output alphabet. At every step, the transducer processes an input symbol, and updates all the variables in parallel using assignments whose right-hand-sides are concatenations of output symbols and variables with the restriction that a variable can be used at most once in a right-hand-side expression. It has been shown that streaming string transducers operating on strings over infinite data domains are of interest in algorithmic verification of list-processing programs, as they lead to PSPACE decision procedures for checking pre/post conditions and for checking semantic equivalence, for a well-defined class of heap-manipulating programs. In order to understand the theoretical expressiveness of streaming transducers, we focus on streaming transducers processing strings over finite alphabets, given the existence of a robust and well-studied class of "regular" transductions for this case. Such regular transductions can be defined either by two-way deterministic finite-state transducers, or using a logical MSO-based characterization. Our main result is that the expressiveness of streaming string transducers coincides exactly with this class of regular transductions

    Rumble: Data Independence for Large Messy Data Sets

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    This paper introduces Rumble, an engine that executes JSONiq queries on large, heterogeneous and nested collections of JSON objects, leveraging the parallel capabilities of Spark so as to provide a high degree of data independence. The design is based on two key insights: (i) how to map JSONiq expressions to Spark transformations on RDDs and (ii) how to map JSONiq FLWOR clauses to Spark SQL on DataFrames. We have developed a working implementation of these mappings showing that JSONiq can efficiently run on Spark to query billions of objects into, at least, the TB range. The JSONiq code is concise in comparison to Spark's host languages while seamlessly supporting the nested, heterogeneous data sets that Spark SQL does not. The ability to process this kind of input, commonly found, is paramount for data cleaning and curation. The experimental analysis indicates that there is no excessive performance loss, occasionally even a gain, over Spark SQL for structured data, and a performance gain over PySpark. This demonstrates that a language such as JSONiq is a simple and viable approach to large-scale querying of denormalized, heterogeneous, arborescent data sets, in the same way as SQL can be leveraged for structured data sets. The results also illustrate that Codd's concept of data independence makes as much sense for heterogeneous, nested data sets as it does on highly structured tables.Comment: Preprint, 9 page

    A study of systems implementation languages for the POCCNET system

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    The results are presented of a study of systems implementation languages for the Payload Operations Control Center Network (POCCNET). Criteria are developed for evaluating the languages, and fifteen existing languages are evaluated on the basis of these criteria

    Parallel Computation of Feynman diagrams with DIANA

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    Co-operation of the Feynman DIagram ANAlyzer (DIANA) with the underlying operational system (UNIX) is presented. We discuss operators to run external commands and a recent development of parallel processing facilities and an extension in the spirit of a component model.Comment: 23 pages, Latex using psfig, epsf and alltt, 3 figure

    Corpus access for beginners: the W3Corpora project

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