12 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    LIPIcs, Volume 244, ESA 2022, Complete Volume

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    LIPIcs, Volume 244, ESA 2022, Complete Volum

    LIPIcs, Volume 248, ISAAC 2022, Complete Volume

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    LIPIcs, Volume 248, ISAAC 2022, Complete Volum

    Efficient local search for Pseudo Boolean Optimization

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    Algorithms and the Foundations of Software technolog

    27th Annual European Symposium on Algorithms: ESA 2019, September 9-11, 2019, Munich/Garching, Germany

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    Parallel and External High Quality Graph Partitioning

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    Partitioning graphs into k blocks of roughly equal size such that few edges run between the blocks is a key tool for processing and analyzing large complex real-world networks. The graph partitioning problem has multiple practical applications in parallel and distributed computations, data storage, image processing, VLSI physical design and many more. Furthermore, recently, size, variety, and structural complexity of real-world networks has grown dramatically. Therefore, there is a demand for efficient graph partitioning algorithms that fully utilize computational power and memory capacity of modern machines. A popular and successful heuristic to compute a high-quality partitions of large networks in reasonable time is multi-level graph partitioning\textit{multi-level graph partitioning} approach which contracts the graph preserving its structure and then partitions it using a complex graph partitioning algorithm. Specifically, the multi-level graph partitioning approach consists of three main phases: coarsening, initial partitioning, and uncoarsening. During the coarsening phase, the graph is recursively contracted preserving its structure and properties until it is small enough to compute its initial partition during the initial partitioning phase. Afterwards, during the uncoarsening phase the partition of the contracted graph is projected onto the original graph and refined using, for example, local search. Most of the research on heuristical graph partitioning focuses on sequential algorithms or parallel algorithms in the distributed memory model. Unfortunately, previous approaches to graph partitioning are not able to process large networks and rarely take in into account several aspects of modern computational machines. Specifically, the amount of cores per chip grows each year as well as the price of RAM reduces slower than the real-world graphs grow. Since HDDs and SSDs are 50 – 400 times cheaper than RAM, external memory makes it possible to process large real-world graphs for a reasonable price. Therefore, in order to better utilize contemporary computational machines, we develop efficient multi-level graph partitioning\textit{multi-level graph partitioning} algorithms for the shared-memory and the external memory models. First, we present an approach to shared-memory parallel multi-level graph partitioning that guarantees balanced solutions, shows high speed-ups for a variety of large graphs and yields very good quality independently of the number of cores used. Important ingredients include parallel label propagation for both coarsening and uncoarsening, parallel initial partitioning, a simple yet effective approach to parallel localized local search, and fast locality preserving hash tables that effectively utilizes caches. The main idea of the parallel localized local search is that each processors refines only a small area around a random vertex reducing interactions between processors. For example, on 79 cores, our algorithms partitions a graph with more than 3 billions of edges into 16 blocks cutting 4.5% less edges than the closest competitor and being more than two times faster. Furthermore, another competitors is not able to partition this graph. We then present an approach to external memory graph partitioning that is able to partition large graphs that do not fit into RAM. Specifically, we consider the semi-external and the external memory model. In both models a data structure of size proportional to the number of edges does not fit into the RAM. The difference is that the former model assumes that a data structure of size proportional to the number of vertices fits into the RAM whereas the latter assumes the opposite. We address the graph partitioning problem in both models by adapting the size-constrained label propagation technique for the semi-external model and by developing a size-constrained clustering algorithm based on graph coloring in the external memory. Our semi-external size-constrained label propagation algorithm (or external memory clustering algorithm) can be used to compute graph clusterings and is a prerequisite for the (semi-)external graph partitioning algorithm. The algorithms are then used for both the coarsening and the uncoarsening phase of a multi-level algorithm to compute graph partitions. Our (semi-)external algorithm is able to partition and cluster huge complex networks with billions of edges on cheap commodity machines. Experiments demonstrate that the semi-external graph partitioning algorithm is scalable and can compute high quality partitions in time that is comparable to the running time of an efficient internal memory implementation. A parallelization of the algorithm in the semi-external model further reduces running times. Additionally, we develop a speed-up technique for the hypergraph partitioning algorithms. Hypergraphs are an extension of graphs that allow a single edge to connect more than two vertices. Therefore, they describe models and processes more accurately additionally allowing more possibilities for improvement. Most multi-level hypergraph partitioning algorithms perform some computations on vertices and their set of neighbors. Since these computations can be super-linear, they have a significant impact on the overall running time on large hypergraphs. Therefore, to further reduce the size of hyperedges, we develop a pin-sparsifier based on the min-hash technique that clusters vertices with similar neighborhood. Further, vertices that belong to the same cluster are substituted by one vertex, which is connected to their neighbors, therefore, reducing the size of the hypergraph. Our algorithm sparsifies a hypergraph such that the resulting graph can be partitioned significantly faster without loss in quality (or with insignificant loss). On average, KaHyPar with sparsifier performs partitioning about 1.5 times faster while preserving solution quality if hyperedges are large. All aforementioned frameworks are publicly available

    Using MapReduce Streaming for Distributed Life Simulation on the Cloud

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    Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

    Verification, slicing, and visualization of programs with contracts

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    Tese de doutoramento em Informática (área de especialização em Ciências da Computação)As a specification carries out relevant information concerning the behaviour of a program, why not explore this fact to slice a program in a semantic sense aiming at optimizing it or easing its verification? It was this idea that Comuzzi, in 1996, introduced with the notion of postcondition-based slicing | slice a program using the information contained in the postcondition (the condition Q that is guaranteed to hold at the exit of a program). After him, several advances were made and different extensions were proposed, bridging the two areas of Program Verification and Program Slicing: specifically precondition-based slicing and specification-based slicing. The work reported in this Ph.D. dissertation explores further relations between these two areas aiming at discovering mutual benefits. A deep study of specification-based slicing has shown that the original algorithm is not efficient and does not produce minimal slices. In this dissertation, traditional specification-based slicing algorithms are revisited and improved (their formalization is proposed under the name of assertion-based slicing), in a new framework that is appropriate for reasoning about imperative programs annotated with contracts and loop invariants. In the same theoretical framework, the semantic slicing algorithms are extended to work at the program level through a new concept called contract based slicing. Contract-based slicing, constituting another contribution of this work, allows for the study of a program at an interprocedural level, enabling optimizations in the context of code reuse. Motivated by the lack of tools to prove that the proposed algorithms work in practice, a tool (GamaSlicer) was also developed. It implements all the existing semantic slicing algorithms, in addition to the ones introduced in this dissertation. This third contribution is based on generic graph visualization and animation algorithms that were adapted to work with verification and slice graphs, two specific cases of labelled control low graphs.Tendo em conta que uma especificação contém informação relevante no que diz respeito ao comportamento de um programa, faz sentido explorar este facto para o cortar em fatias (slice) com o objectivo de o optimizar ou de facilitar a sua verificação. Foi precisamente esta ideia que Comuzzi introduziu, em 1996, apresentando o conceito de postcondition-based slicing que consiste em cortar um programa usando a informação contida na pos-condicão (a condição Q que se assegura ser verdadeira no final da execução do programa). Depois da introdução deste conceito, vários avanços foram feitos e diferentes extensões foram propostas, aproximando desta forma duas áreas que até então pareciam desligadas: Program Verification e Program Slicing. Entre estes conceitos interessa-nos destacar as noções de precondition-based slicing e specification-based slicing, que serão revisitadas neste trabalho. Um estudo aprofundado do conceito de specification-based slicing relevou que o algoritmo original não é eficiente e não produz slices mínimos. O trabalho reportado nesta dissertação de doutoramento explora a ideia de tornar mais próximas essas duas áreas visando obter benefícios mútuos. Assim, estabelecendo uma nova base teórica matemática, os algoritmos originais de specification-based slicing são revistos e aperfeiçoados | a sua formalizacão é proposta com o nome de assertion-based slicing. Ainda sobre a mesma base teórica, os algoritmos de slicing são extendidos, de forma a funcionarem ao nível do programa; alem disso introduz-se um novo conceito: contract-based slicing. Este conceito, contract-based slicing, sendo mais um dos contributos do trabalho aqui descrito, possibilita o estudo de um programa ao nível externo de um procedimento, permitindo, por um lado, otimizações no contexto do seu uso, e por outro, a sua reutilização segura. Devido à falta de ferramentas que provem que os algoritmos propostos de facto funcionam na prática, foi desenvolvida uma, com o nome GamaSlicer, que implementa todos os algoritmos existentes de slicing semântico e os novos propostos. Uma terceira contribuição é baseada nos algoritmos genéricos de visualização e animação de grafos que foram adaptados para funcionar com os grafos de controlo de fluxo etiquetados e os grafos de verificação e slicing.Fundação para a Ciência e a Tecnologia (FCT) através da Bolsa de Doutoramento SFRH/BD/33231/2007Projecto RESCUE (contrato FCT sob a referência PTDC / EIA / 65862 /2006)Projecto CROSS (contrato FCT sob a referência PTDC / EIACCO / 108995 / 2008

    Eight Biennial Report : April 2005 – March 2007

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