Implementation of parallel algorithm for run of k-local tree automata

Tato práce se zabývá k-lokálními deterministickými konečnými stromovými automaty (DKSA), které hrají důležitou roli při hledání vzorů ve stromových strukturách. Existuje pracovně optimální paralelní algoritmus pro běh k-lokálních DKSA na výpočetním modelu EREW PRAM. Tento algoritmus bude implementován, experimentálně změřen a porovnán se sekvenčním algoritmem v této práci.This thesis deals with k-local deterministic finite tree automata (DFTA) which are important for tree pattern matching. There exists a work-optimal parallel algorithm for a run of k-local DFTA on EREW PRAM. This algorithm will be implemented, experimentally measured and compared with the sequential algorithm in this thesis

Efficient Linked List Ranking Algorithms and Parentheses Matching as a New Strategy for Parallel Algorithm Design

The goal of a parallel algorithm is to solve a single problem using multiple processors working together and to do so in an efficient manner. In this regard, there is a need to categorize strategies in order to solve broad classes of problems with similar structures and requirements. In this dissertation, two parallel algorithm design strategies are considered: linked list ranking and parentheses matching

Some Optimally Adaptive Parallel Graph Algorithms on EREW PRAM Model

The study of graph algorithms is an important area of research in computer science, since graphs offer useful tools to model many real-world situations. The commercial availability of parallel computers have led to the development of efficient parallel graph algorithms. Using an exclusive-read and exclusive-write (EREW) parallel random access machine (PRAM) as the computation model with a fixed number of processors, we design and analyze parallel algorithms for seven undirected graph problems, such as, connected components, spanning forest, fundamental cycle set, bridges, bipartiteness, assignment problems, and approximate vertex coloring. For all but the last two problems, the input data structure is an unordered list of edges, and divide-and-conquer is the paradigm for designing algorithms. One of the algorithms to solve the assignment problem makes use of an appropriate variant of dynamic programming strategy. An elegant data structure, called the adjacency list matrix, used in a vertex-coloring algorithm avoids the sequential nature of linked adjacency lists. Each of the proposed algorithms achieves optimal speedup, choosing an optimal granularity (thus exploiting maximum parallelism) which depends on the density or the number of vertices of the given graph. The processor-(time)2 product has been identified as a useful parameter to measure the cost-effectiveness of a parallel algorithm. We derive a lower bound on this measure for each of our algorithms

Parallel Parsing in a Multiprocessor Environment

Parsing in a multiprocessor environment is considered. Two models for asynchronous bottom-up parallel parsing are presented. A method for estimating speedup in asynchronous bottom-up parallel parsing is developed, and it is used to estimate speedup obtainable by bottom-up parallel parsing of Pascal-like languages. It is found that bottom-up parallel parsing algorithms can attain a maximum speedup of 0 (L1/2) with (L1/2) processors, where L is the number of tokens in the string being parsed. Hence, bottom-up parallel parsing technique does not yield good speedup. A new parsing technique is proposed for parsing a class of block-structured languages. The novelty of the technique is that it is inherently parallel. By applying this new technique, a string of L tokens can be parsed in O (log L) time with (L /log L) processors. The parsing algorithm uses a parenthesis-matching algorithm developed here. The parenthesis-matching algorithm can find matching of a sequence of parentheses in O (log L) time with (L /log L) processors. Thus, the new parsing algorithm is cost optimal

Parallel and scalable combinatorial string algorithms on distributed memory systems

Methods for processing and analyzing DNA and genomic data are built upon combinatorial graph and string algorithms. The advent of high-throughput DNA sequencing is enabling the generation of billions of reads per experiment. Classical and sequential algorithms can no longer deal with these growing data sizes - which for the last 10 years have greatly out-paced advances in processor speeds. Processing and analyzing state-of-the-art genomic data sets require the design of scalable and efficient parallel algorithms and the use of large computing clusters. Suffix arrays and trees are fundamental string data structures, which lie at the foundation of many string algorithms, with important applications in text processing, information retrieval, and computational biology. Conversely, the parallel construction of these indices is an actively studied problem. However, prior approaches lacked good worst-case run-time guarantees and exhibit poor scaling and overall performance. In this work, we present our distributed-memory parallel algorithms for indexing large datasets, including algorithms for the distributed construction of suffix arrays, LCP arrays, and suffix trees. We formulate a generalized version of the All-Nearest-Smaller-Values problem, provide an optimal distributed solution, and apply it to the distributed construction of suffix trees - yielding a work-optimal parallel algorithm. Our algorithms for distributed suffix array and suffix tree construction improve the state-of-the-art by simultaneously improving worst-case run-time bounds and achieving superior practical performance. Next, we introduce a novel distributed string index, the Distributed Enhanced Suffix Array (DESA) - based on the suffix and LCP arrays, the DESA consists of these and additional distributed data structures. The DESA is designed to allow efficient pattern search queries in distributed memory while requiring at most O(n/p) memory per process. We present efficient distributed-memory parallel algorithms for querying, as well as for the efficient construction of this distributed index. Finally, we present our work on distributed-memory algorithms for clustering de Bruijn graphs and its application to solving a grand challenge metagenomic dataset.Ph.D

Parallel Algorithms for Counting Problems on Graphs Using Graphics Processing Units

The availability of Graphics Processing Units (GPUs) with multicore architecture have enabled parallel computations using extensive multi-threading. Recent advancements in computer hardware have led to the usage of graphics processors for solving general purpose problems. Using GPUs for computation is a highly efficient and low-cost alternative as compared to currently available multicore Central Processing Units (CPUs). Also, in the past decade there has been tremendous growth in the World Wide Web and Online Social Networks. Social networking sites such as Facebook, Twitter and LinkedIn, with millions of users are a huge source of data. These data sets can be used for research in the fields of anthropology, social psychology, economics among others. Our research focuses on converting real-world problems into graph theoretic problems and using GPUs to solve them. The graph problems that we focus on in our research involve counting the number of subgraphs that satisfy a given property. For example, given a graph G=(V,E) and an integer k<=|V|, we provide algorithms to count the number of: a) connected subgraphs of size k; b) cliques of size k; and c) independent sets of size k, and other similar problems. Also, properties that are affected by the dynamic nature of the graphs i.e., addition or removal of edges or nodes, for example change in the number of triangles and connected components in the graph, are also studied. Sequential access to global memory and contention at the size-limited shared memory have been main impediments to fully exploiting potential performance in GPUs. Therefore, we propose novel memory storage and retrieval methods, based on using search techniques on graphs and converting it into trees, that enable parallel graph computations to overcome the above issues. We also analyze and utilize primitives such as memory access coalescing and avoiding partition camping that offset the increase in access latency of using a slower but larger global memory. In addition, we introduce graph compression techniques that further reduce memory requirements and overheads. Our experimental results for the GPU implementation show a significant speedup over the CPU counterpart for the problems described above

Programming and symbolic computation in Maude

[EN] Rewriting logic is both a flexible semantic framework within which widely different concurrent systems can be naturally specified and a logical framework in which widely different logics can be specified. Maude programs are exactly rewrite theories. Maude has also a formal environment of verification tools. Symbolic computation is a powerful technique for reasoning about the correctness of concurrent systems and for increasing the power of formal tools. We present several new symbolic features of Maude that enhance formal reasoning about Maude programs and the effectiveness of formal tools. They include: (i) very general unification modulo user-definable equational theories, and (ii) symbolic reachability analysis of concurrent systems using narrowing. The paper does not focus just on symbolic features: it also describes several other new Maude features, including: (iii) Maude's strategy language for controlling rewriting, and (iv) external objects that allow flexible interaction of Maude object-based concurrent systems with the external world. In particular, meta-interpreters are external objects encapsulating Maude interpreters that can interact with many other objects. To make the paper self-contained and give a reasonably complete language overview, we also review the basic Maude features for equational rewriting and rewriting with rules, Maude programming of concurrent object systems, and reflection. Furthermore, we include many examples illustrating all the Maude notions and features described in the paper.Duran has been partially supported by MINECO/FEDER project TIN2014-52034-R. Escobar has been partially supported by the EU (FEDER) and the MCIU under grant RTI2018-094403-B-C32, by the Spanish Generalitat Valenciana under grant PROMETE0/2019/098, and by the US Air Force Office of Scientific Research under award number FA9550-17-1-0286. MartiOliet and Rubio have been partially supported by MCIU Spanish project TRACES (TIN2015-67522-C3-3-R). Rubio has also been partially supported by a MCIU grant FPU17/02319. Meseguer and Talcott have been partially supported by NRL Grant N00173 -17-1-G002. Talcott has also been partially supported by ONR Grant N00014-15-1-2202.Durán, F.; Eker, S.; Escobar Román, S.; NARCISO MARTÍ OLIET; José Meseguer; Rubén Rubio; Talcott, C. (2020). Programming and symbolic computation in Maude. Journal of Logical and Algebraic Methods in Programming. 110:1-58. https://doi.org/10.1016/j.jlamp.2019.100497S158110Alpuente, M., Escobar, S., Espert, J., & Meseguer, J. (2014). A modular order-sorted equational generalization algorithm. 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Symbolic reachability analysis using narrowing and its application to verification of cryptographic protocols. Higher-Order and Symbolic Computation, 20(1-2), 123-160. doi:10.1007/s10990-007-9000-6C. Olarte, E. Pimentel, C. Rocha, Proving structural properties of sequent systems in rewriting logic, in: [121], 2018, pp. 115–135.Ölveczky, P. C., & Meseguer, J. (2007). Semantics and pragmatics of Real-Time Maude. Higher-Order and Symbolic Computation, 20(1-2), 161-196. doi:10.1007/s10990-007-9001-5Ölveczky, P. C., & Thorvaldsen, S. (2009). Formal modeling, performance estimation, and model checking of wireless sensor network algorithms in Real-Time Maude. Theoretical Computer Science, 410(2-3), 254-280. doi:10.1016/j.tcs.2008.09.022Rocha, C., Meseguer, J., & Muñoz, C. (2017). Rewriting modulo SMT and open system analysis. Journal of Logical and Algebraic Methods in Programming, 86(1), 269-297. doi:10.1016/j.jlamp.2016.10.001Şerbănuţă, T. F., Roşu, G., & Meseguer, J. (2009). 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