Исследовательская группа

Лаборатория языковых инструментов

Публикации

  • Egor Orachev, Ilya Epelbaum, Rustam Azimov, Semyon Grigorev

    Context-free path queries (CFPQ) extend the regular path queries (RPQ) by allowing context-free grammars to be used as constraints for paths. Algorithms for CFPQ are actively developed, but J. Kuijpers et al. have recently concluded, that existing algorithms are not performant enough to be used in real-world applications. Thus the development of new algorithms for CFPQ is justified. In this paper, we provide a new CFPQ algorithm which is based on such linear algebra operations as Kronecker product and transitive closure and handles grammars presented as recursive state machines. Thus, the proposed algorithm can be implemented by using high-performance libraries and modern parallel hardware. Moreover, it avoids grammar growth which provides the possibility for queries optimization.

    ADBIS 2020. Advances in Databases and Information Systems. Lecture Notes in Computer Science.,
  • Ciro Medeiros, Umberto Costa, Semyon Grigorev, Martin A. Musicante
    Regular expressions are used in SPARQL property paths to query RDF graphs. However, regular expressions can only define the most limited class of languages, called regular languages. Context-free languages are a wider class containing all regular languages. There are no context-free expressions to define them, so it is necessary to write grammars. We propose an extension of regular expressions, called recursive expressions, to support the definition of a subset of context-free languages. The goal of our work is therefore to provide simple operators allowing the definition of languages as close as possible to context-free languages.
    ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium,
  • Evgenii Moiseenko, Anton Podkopaev, Ori Lahav, Orestis Melkonian, Viktor Vafeiadis
    The European Conference on Object-Oriented Programming (ECOOP),
  • Conrad Watt, Christopher Pulte, Anton Podkopaev, Guillaume Barbier, Stephen Dolan, Shaked Flur, Jean Pichon-Pharabod Shu-yu Guo
    41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2020),
  • Sung-Hwan Lee, Minki Cho, Anton Podkopaev, Soham Chakraborty, Chung-Kil Hur, Ori Lahav, Viktor Vafeiadis
    41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2020),
  • Susanina Y.A., Yaveyn A.N., Grigorev S.V.

    В данной работе предложен алгоритм, который является модификацией алгоритма Валианта. Его основным достоинством является возможность разбиения матрицы разбора на подслои непересекающихся подматриц, которые могут быть обработаны независимо. Доказана корректность и приведена оценка сложности предложенного алгоритма. Проведенные эксперименты показывают, что он сохранил основные преимущества исходного алгоритма, главное из которых – высокая производительность, полученная за счет использования эффективных методов перемножения матриц. Также предложенный алгоритм позволил заметно уменьшить время, затрачиваемое на поиск подстрок, сократив большое количество избыточных вычислений.

    Proceedings of the Institute for System Programming,
  • Arseniy Terekhov, Artyom Khoroshev, Rustam Azimov, Semyon Grigorev

    A recent study showed that the applicability of context-free path querying (CFPQ) algorithms with relational query semantics integrated with graph databases is limited because of low performance and high memory consumption of existing solutions. In this work, we implement a matrix-based CFPQ algorithm by using appropriate high-performance libraries for linear algebra and integrate it with RedisGraph graph database. Also, we introduce a new CFPQ algorithm with single-path query semantics that allows us to extract one found path for each pair of nodes. Finally, we provide the evaluation of our algorithms for both semantics which shows that matrix-based CFPQ implementation for Redis-Graph database is performant enough for real-world data analysis.

    GRADES-NDA'20: Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA),
  • Yuliya Susanina
    Context-free path querying (CFPQ) widely used for graph-structured data analysis in different areas. It is crucial to develop highly efficient algorithms for CFPQ since the size of the input data is typically large. We show how to reduce GFPQ evaluation to solving systems of matrix equations over R --- a problem for which there exist high-performance solutions. Also, we demonstrate the applicability of our approach to real-world data analysis.
    Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data,
  • Aleksey Tyurin, Daniil Berezun, Semyon Grigorev

    While GPU utilization allows one to speed up computations to the orders of magnitude, memory management remains the bottleneck making it often a challenge to achieve the desired performance. Hence, different memory optimizations are leveraged to make memory being used more effectively. We propose an approach automating memory management utilizing partial evaluation, a program transformation technique that enables data accesses to be pre-computed, optimized, and embedded into the code, saving memory transactions. An empirical evaluation of our approach shows that the transformed program could be up to 8 times as efficient as the original one in the case of CUDA C naïve string pattern matching algorithm implementation.

    PPoPP '20: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming,
  • R. Azimov and S. Grigorev

    Path querying with conjunctive grammars is known to be undecidable. There is an algorithm for path querying with linear conjunctive grammars which provides an over-approximation of the result, but there is no algorithm for arbitrary conjunctive grammars. We propose the first algorithm for path querying with arbitrary conjunctive grammars. The proposed algorithm is matrix-based and allows us to efficiently apply GPGPU computing techniques and other optimizations for matrix operations.

    Programming and Computer Software,

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