Data distribution and performance optimization models for parallel data mining

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.Thesis (Ph. D.) -- Bilkent University, 2013.Includes bibliographical references leaves 117-128.We have embarked upon a multitude of approaches to improve the efficiency of selected fundamental tasks in data mining. The present thesis is concerned with improving the efficiency of parallel processing methods for large amounts of data. We have devised new parallel frequent itemset mining algorithms that work on both sparse and dense datasets, and 1-D and 2-D parallel algorithms for the all-pairs similarity problem. Two new parallel frequent itemset mining (FIM) algorithms named NoClique and NoClique2 parallelize our sequential vertical frequent itemset mining algorithm named bitdrill, and uses a method based on graph partitioning by vertex separator (GPVS) to distribute and selectively replicate items. The method operates on a graph where vertices correspond to frequent items and edges correspond to frequent itemsets of size two. We show that partitioning this graph by a vertex separator is sufficient to decide a distribution of the items such that the sub-databases determined by the item distribution can be mined independently. This distribution entails an amount of data replication, which may be reduced by setting appropriate weights to vertices. The data distribution scheme is used in the design of two new parallel frequent itemset mining algorithms. Both algorithms replicate the items that correspond to the separator. NoClique replicates the work induced by the separator and NoClique2 computes the same work collectively. Computational load balancing and minimization of redundant or collective work may be achieved by assigning appropriate load estimates to vertices. The performance is compared to another parallelization that replicates all items, and ParDCI algorithm. We introduce another parallel FIM method using a variation of item distribution with selective item replication. We extend the GPVS model for parallel FIM we have proposed earlier, by relaxing the condition of independent mining. Instead of finding independently mined item sets, we may minimize the amount of communication and partition the candidates in a fine-grained manner. We introduce a hypergraph partitioning model of the parallel computation where vertices correspond to candidates and hyperedges correspond to items. A load estimate is assigned to each candidate with vertex weights, and item frequencies are given as hyperedge weights. The model is shown to minimize data replication and balance load accurately. We also introduce a re-partitioning model since we can generate only so many levels of candidates at once, using fixed vertices to model previous item distribution/replication. Experiments show that we improve over the higher load imbalance of NoClique2 algorithm for the same problem instances at the cost of additional parallel overhead. For the all-pairs similarity problem, we extend recent efficient sequential algorithms to a parallel setting, and obtain document-wise and term-wise parallelizations of a fast sequential algorithm, as well as an elegant combination of two algorithms that yield a 2-D distribution of the data. Two effective algorithmic optimizations for the term-wise case are reported that make the term-wise parallelization feasible. These optimizations exploit local pruning and block processing of a number of vectors, in order to decrease communication costs, the number of candidates, and communication/computation imbalance. The correctness of local pruning is proven. Also, a recursive term-wise parallelization is introduced. The performance of the algorithms are shown to be favorable in extensive experiments, as well as the utility of two major optimizations.Özkural, ErayPh.D

Livro de atas do XVI Congresso da Associação Portuguesa de Investigação Operacional

Fundação para a Ciência e Tecnologia - FC

Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial

Este documento contiene el proyecto docente e investigador del candidato Germán Moltó Martínez presentado como requisito para el concurso de acceso a plazas de Cuerpos Docentes Universitarios. Concretamente, el documento se centra en el concurso para la plaza 6708 de Catedrático de Universidad en el área de Ciencia de la Computación en el Departamento de Sistemas Informáticos y Computación de la Universitat Politécnica de València. La plaza está adscrita a la Escola Técnica Superior d'Enginyeria Informàtica y tiene como perfil las asignaturas "Infraestructuras de Cloud Público" y "Estructuras de Datos y Algoritmos".También se incluye el Historial Académico, Docente e Investigador, así como la presentación usada durante la defensa.Germán Moltó Martínez (2022). Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial. http://hdl.handle.net/10251/18903

Algorithmic skeletons for exact combinatorial search at scale

Exact combinatorial search is essential to a wide range of application areas including constraint optimisation, graph matching, and computer algebra. Solutions to combinatorial problems are found by systematically exploring a search space, either to enumerate solutions, determine if a specific solution exists, or to find an optimal solution. Combinatorial searches are computationally hard both in theory and practice, and efficiently exploring the huge number of combinations is a real challenge, often addressed using approximate search algorithms. Alternatively, exact search can be parallelised to reduce execution time. However, parallel search is challenging due to both highly irregular search trees and sensitivity to search order, leading to anomalies that can cause unexpected speedups and slowdowns. As core counts continue to grow, parallel search becomes increasingly useful for improving the performance of existing searches, and allowing larger instances to be solved. A high-level approach to parallel search allows non-expert users to benefit from increasing core counts. Algorithmic Skeletons provide reusable implementations of common parallelism patterns that are parameterised with user code which determines the specific computation, e.g. a particular search. We define a set of skeletons for exact search, requiring the user to provide in the minimal case a single class that specifies how the search tree is generated and a parameter that specifies the type of search required. The five are: Sequential search; three general-purpose parallel search methods: Depth-Bounded, Stack-Stealing, and Budget; and a specific parallel search method, Ordered, that guarantees replicable performance. We implement and evaluate the skeletons in a new C++ parallel search framework, YewPar. YewPar provides both high-level skeletons and low-level search specific schedulers and utilities to deal with the irregularity of search and knowledge exchange between workers. YewPar is based on the HPX library for distributed task-parallelism potentially allowing search to execute on multi-cores, clusters, cloud, and high performance computing systems. Underpinning the skeleton design is a novel formal model, MT^3 , a parallel operational semantics that describes multi-threaded tree traversals, allowing reasoning about parallel search, e.g. describing common parallel search phenomena such as performance anomalies. YewPar is evaluated using seven different search applications (and over 25 specific instances): Maximum Clique, k-Clique, Subgraph Isomorphism, Travelling Salesperson, Binary Knapsack, Enumerating Numerical Semigroups, and the Unbalanced Tree Search Benchmark. The search instances are evaluated at multiple scales from 1 to 255 workers, on a 17 host, 272 core Beowulf cluster. The overheads of the skeletons are low, with a mean 6.1% slowdown compared to hand-coded sequential implementation. Crucially, for all search applications YewPar reduces search times by an order of magnitude, i.e hours/minutes to minutes/seconds, and we commonly see greater than 60% (average) parallel efficiency speedups for up to 255 workers. Comparing skeleton performance reveals that no one skeleton is best for all searches, highlighting a benefit of a skeleton approach that allows multiple parallelisations to be explored with minimal refactoring. The Ordered skeleton avoids slowdown anomalies where, due to search knowledge being order dependent, a parallel search takes longer than a sequential search. Analysis of Ordered shows that, while being 41% slower on average (73% worse-case) than Depth-Bounded, in nearly all cases it maintains the following replicable performance properties: 1) parallel executions are no slower than one worker sequential executions 2) runtimes do not increase as workers are added, and 3) variance between repeated runs is low. In particular, where Ordered maintains a relative standard deviation (RSD) of less than 15%, Depth-Bounded suffers from an RSD greater than 50%, showing the importance of carefully controlling search orders for repeatability

Sur la conception de solveurs linéaires hybrides pour les architectures parallèles modernes

In the context of this thesis, our focus is on numerical linear algebra, more precisely on solution of large sparse systems of linear equations. We focus on designing efficient parallel implementations of MaPHyS, an hybrid linear solver based on domain decomposition techniques. First we investigate the MPI+threads approach. In MaPHyS, the first level of parallelism arises from the independent treatment of the various subdomains. The second level is exploited thanks to the use of multi-threaded dense and sparse linear algebra kernels involved at the subdomain level. Such an hybrid implementation of an hybrid linear solver suitably matches the hierarchical structure of modern supercomputers and enables a trade-off between the numerical and parallel performances of the solver. We demonstrate the flexibility of our parallel implementation on a set of test examples. Secondly, we follow a more disruptive approach where the algorithms are described as sets of tasks with data inter-dependencies that leads to a directed acyclic graph (DAG) representation. The tasks are handled by a runtime system. We illustrate how a first task-based parallel implementation can be obtained by composing task-based parallel libraries within MPI processes throught a preliminary prototype implementation of our hybrid solver. We then show how a task-based approach fully abstracting the hardware architecture can successfully exploit a wide range of modern hardware architectures. We implemented a full task-based Conjugate Gradient algorithm and showed that the proposed approach leads to very high performance on multi-GPU, multicore and heterogeneous architectures.Dans le contexte de cette thèse, nous nous focalisons sur des algorithmes pour l’algèbre linéaire numérique, plus précisément sur la résolution de grands systèmes linéaires creux. Nous mettons au point des méthodes de parallélisation pour le solveur linéaire hybride MaPHyS. Premièrement nous considerons l'aproche MPI+threads. Dans MaPHyS, le premier niveau de parallélisme consiste au traitement indépendant des sous-domaines. Le second niveau est exploité grâce à l’utilisation de noyaux multithreadés denses et creux au sein des sous-domaines. Une telle implémentation correspond bien à la structure hiérarchique des supercalculateurs modernes et permet un compromis entre les performances numériques et parallèles du solveur. Nous démontrons la flexibilité de notre implémentation parallèle sur un ensemble de cas tests. Deuxièmement nous considérons un approche plus innovante, où les algorithmes sont décrits comme des ensembles de tâches avec des inter-dépendances, i.e., un graphe de tâches orienté sans cycle (DAG). Nous illustrons d’abord comment une première parallélisation à base de tâches peut être obtenue en composant des librairies à base de tâches au sein des processus MPI illustrer par un prototype d’implémentation préliminaire de notre solveur hybride. Nous montrons ensuite comment une approche à base de tâches abstrayant entièrement le matériel peut exploiter avec succès une large gamme d’architectures matérielles. À cet effet, nous avons implanté une version à base de tâches de l’algorithme du Gradient Conjugué et nous montrons que l’approche proposée permet d’atteindre une très haute performance sur des architectures multi-GPU, multicoeur ainsi qu’hétérogène

Scalability Engineering for Parallel Programs Using Empirical Performance Models

Performance engineering is a fundamental task in high-performance computing (HPC). By definition, HPC applications should strive for maximum performance. As HPC systems grow larger and more complex, the scalability of an application has become of primary concern. Scalability is the ability of an application to show satisfactory performance even when the number of processors or the problems size is increased. Although various analysis techniques for scalability were suggested in past, engineering applications for extreme-scale systems still occurs ad hoc. The challenge is to provide techniques that explicitly target scalability throughout the whole development cycle, thereby allowing developers to uncover bottlenecks earlier in the development process. In this work, we develop a number of fundamental approaches in which we use empirical performance models to gain insights into the code behavior at higher scales. In the first contribution, we propose a new software engineering approach for extreme-scale systems. Specifically, we develop a framework that validates asymptotic scalability expectations of programs against their actual behavior. The most important applications of this method, which is especially well suited for libraries encapsulating well-studied algorithms, include initial validation, regression testing, and benchmarking to compare implementation and platform alternatives. We supply a tool-chain that automates large parts of the framework, thus allowing it to be continuously applied throughout the development cycle with very little effort. We evaluate the framework with MPI collective operations, a data-mining code, and various OpenMP constructs. In addition to revealing unexpected scalability bottlenecks, the results also show that it is a viable approach for systematic validation of performance expectations. As the second contribution, we show how the isoefficiency function of a task-based program can be determined empirically and used in practice to control the efficiency. Isoefficiency, a concept borrowed from theoretical algorithm analysis, binds efficiency, core count, and the input size in one analytical expression, thereby allowing the latter two to be adjusted according to given (realistic) efficiency objectives. Moreover, we analyze resource contention by modeling the efficiency of contention-free execution. This allows poor scaling to be attributed either to excessive resource contention overhead or structural conflicts related to task dependencies or scheduling. Our results, obtained with applications from two benchmark suites, demonstrate that our approach provides insights into fundamental scalability limitations or excessive resource overhead and can help answer critical co-design questions. Our contributions for better scalability engineering can be used not only in the traditional software development cycle, but also in other, related fields, such as algorithm engineering. It is a field that uses the software engineering cycle to produce algorithms that can be utilized in applications more easily. Using our contributions, algorithm engineers can make informed design decisions, get better insights, and save experimentation time