Five ways to insert concurrency to a program written in C#

Nowadays processors working in personal computers and mobile devices allow for more and more effective parallel computing. Developers have at their disposal many different methods of implementing concurrency, but usually use the one, that they now best. It is beneficial to know, when a particular technique is good and when it is better to find an alternative. This paper presents different ways of implementing parallel mathematical calculations using threads, tasks, thread pool, task pool and parallel for loop. Each method was used in a C# application running on Windows Presentation Foundation engine on .NET platform. Implemented operation is calculation value of Pi using Leibnitz’s formula

Parallélisation massive des algorithmes de branchement

Les problèmes d'optimisation et de recherche sont souvent NP-complets et des techniques de force brute doivent généralement être mises en œuvre pour trouver des solutions exactes. Des problèmes tels que le regroupement de gènes en bio-informatique ou la recherche de routes optimales dans les réseaux de distribution peuvent être résolus en temps exponentiel à l'aide de stratégies de branchement récursif. Néanmoins, ces algorithmes deviennent peu pratiques au-delà de certaines tailles d'instances en raison du grand nombre de scénarios à explorer, pour lesquels des techniques de parallélisation sont nécessaires pour améliorer les performances. Dans des travaux antérieurs, des techniques centralisées et décentralisées ont été mises en œuvre afin d'augmenter le parallélisme des algorithmes de branchement tout en essayant de réduire les coûts de communication, qui jouent un rôle important dans les implémentations massivement parallèles en raison des messages passant entre les processus. Ainsi, notre travail consiste à développer une bibliothèque entièrement générique en C++, nommée GemPBA, pour accélérer presque tous les algorithmes de branchement avec une parallélisation massive, ainsi que le développement d'un outil novateur et simpliste d'équilibrage de charge dynamique pour réduire le nombre de messages transmis en envoyant les tâches prioritaires en premier. Notre approche utilise une stratégie hybride centralisée-décentralisée, qui fait appel à un processus central chargé d'attribuer les rôles des travailleurs par des messages de quelques bits, telles que les tâches n'ont pas besoin de passer par un processeur central. De plus, un processeur en fonctionnement génère de nouvelles tâches si et seulement s'il y a des processeurs disponibles pour les recevoir, garantissant ainsi leur transfert, ce qui réduit considérablement les coûts de communication. Nous avons réalisé nos expériences sur le problème de la couverture minimale de sommets, qui a montré des résultats remarquables, étant capable de résoudre même les graphes DIMACS les plus difficiles avec un simple algorithme MVC.Abstract: Optimization and search problems are often NP-complete, and brute-force techniques must typically be implemented to find exact solutions. Problems such as clustering genes in bioinformatics or finding optimal routes in delivery networks can be solved in exponential-time using recursive branching strategies. Nevertheless, these algorithms become impractical above certain instance sizes due to the large number of scenarios that need to be explored, for which parallelization techniques are necessary to improve the performance. In previous works, centralized and decentralized techniques have been implemented aiming to scale up parallelism on branching algorithms whilst attempting to reduce communication overhead, which plays a significant role in massively parallel implementations due to the messages passing across processes. Thus, our work consists of the development of a fully generic library in C++, named GemPBA, to speed up almost any branching algorithms with massive parallelization, along with the development of a novel and simplistic Dynamic Load Balancing tool to reduce the number of passed messages by sending high priority tasks first. Our approach uses a hybrid centralized-decentralized strategy, which makes use of a center process in charge of assigning worker roles by messages of a few bits of size, such that tasks do not need to pass through a center processor. Also, a working processor will spawn new tasks if and only if there are available processors to receive them, thus, guaranteeing its transfer, and thereby the communication overhead is notably decreased. We performed our experiments on the Minimum Vertex Cover problem, which showed remarkable results, being capable of solving even the toughest DIMACS graphs with a simple MVC algorithm

Esqueletos paralelos para la técnica de ramificación y acotación

En un gran número de problemas combinatorios, el tiempo empleado para obtener una solución usando un computador secuencial es muy alto. Una forma de solventar este inconveniente consiste en utilizar la computación paralela. En un computador paralelo, varios procesadores colaboran para resolver simultáneamente un problema en una fracción del tiemp requerido por un sólo procesador. Entre los componentes claves necesarios para que sea posible la aplicación de la computación paralela están la arquitectura, el sistema operativo, los compiladores de lenguajes de programación, y, el más importante de todos, el algoritmo paralelo. Ningún problema se puede resolver en paralelo sin un algoritmo paralelo, puesto que los algoritmos paralelos son el núcleo de la computación paralela. El objetivo de la memoria de tesis doctoral era el desarrollo de una metodología de trabajo para abordar la resolución de problemas de optimización combinatoria mediante la técnica de Ramificación y Acotación utilizando paralelismo. Partiendo de casos concretos se generalizó una forma de trabajar que dio lugar a la resolución de problemas diversos. Para ello, se utilizó el concepto de esqueleto presentado por Murray Cole en 1987

Real-time analytics on large dynamic graphs

In today's fast-paced and interconnected digital world, the data generated by an increasing number of applications is being modeled as dynamic graphs. The graph structure encodes relationships among data items, while the structural changes to the graphs as well as the continuous stream of information produced by the entities in these graphs make them dynamic in nature. Examples include social networks where users post status updates, images, videos, etc.; phone call networks where nodes may send text messages or place phone calls; road traffic networks where the traffic behavior of the road segments changes constantly, and so on. There is a tremendous value in storing, managing, and analyzing such dynamic graphs and deriving meaningful insights in real-time. However, a majority of the work in graph analytics assumes a static setting, and there is a lack of systematic study of the various dynamic scenarios, the complexity they impose on the analysis tasks, and the challenges in building efficient systems that can support such tasks at a large scale. In this dissertation, I design a unified streaming graph data management framework, and develop prototype systems to support increasingly complex tasks on dynamic graphs. In the first part, I focus on the management and querying of distributed graph data. I develop a hybrid replication policy that monitors the read-write frequencies of the nodes to decide dynamically what data to replicate, and whether to do eager or lazy replication in order to minimize network communication and support low-latency querying. In the second part, I study parallel execution of continuous neighborhood-driven aggregates, where each node aggregates the information generated in its neighborhoods. I build my system around the notion of an aggregation overlay graph, a pre-compiled data structure that enables sharing of partial aggregates across different queries, and also allows partial pre-computation of the aggregates to minimize the query latencies and increase throughput. Finally, I extend the framework to support continuous detection and analysis of activity-based subgraphs, where subgraphs could be specified using both graph structure as well as activity conditions on the nodes. The query specification tasks in my system are expressed using a set of active structural primitives, which allows the query evaluator to use a set of novel optimization techniques, thereby achieving high throughput. Overall, in this dissertation, I define and investigate a set of novel tasks on dynamic graphs, design scalable optimization techniques, build prototype systems, and show the effectiveness of the proposed techniques through extensive evaluation using large-scale real and synthetic datasets

Discrete Optimization in Early Vision - Model Tractability Versus Fidelity

Early vision is the process occurring before any semantic interpretation of an image takes place. Motion estimation, object segmentation and detection are all parts of early vision, but recognition is not. Some models in early vision are easy to perform inference with---they are tractable. Others describe the reality well---they have high fidelity. This thesis improves the tractability-fidelity trade-off of the current state of the art by introducing new discrete methods for image segmentation and other problems of early vision. The first part studies pseudo-boolean optimization, both from a theoretical perspective as well as a practical one by introducing new algorithms. The main result is the generalization of the roof duality concept to polynomials of higher degree than two. Another focus is parallelization; discrete optimization methods for multi-core processors, computer clusters, and graphical processing units are presented. Remaining in an image segmentation context, the second part studies parametric problems where a set of model parameters and a segmentation are estimated simultaneously. For a small number of parameters these problems can still be optimally solved. One application is an optimal method for solving the two-phase Mumford-Shah functional. The third part shifts the focus to curvature regularization---where the commonly used length and area penalization is replaced by curvature in two and three dimensions. These problems can be discretized over a mesh and special attention is given to the mesh geometry. Specifically, hexagonal meshes in the plane are compared to square ones and a method for generating adaptive meshes is introduced and evaluated. The framework is then extended to curvature regularization of surfaces. Finally, the thesis is concluded by three applications to early vision problems: cardiac MRI segmentation, image registration, and cell classification

Models, services and security in modern online social networks

Modern online social networks have revolutionized the world the same way the radio and the plane did, crossing geographical and time boundaries, not without problems, more can be learned, they can still change our world and that their true worth is still a question for the future

MINIMIZATION OF RESOURCE CONSUMPTION THROUGH WORKLOAD CONSOLIDATION IN LARGE-SCALE DISTRIBUTED DATA PLATFORMS

The rapid increase in the data volumes encountered in many application domains has led to widespread adoption of parallel and distributed data management systems like parallel databases and MapReduce-based frameworks (e.g., Hadoop) in recent years. Use of such parallel and distributed frameworks is expected to accelerate in the coming years, putting further strain on already-scarce resources like compute power, network bandwidth, and energy. To reduce total execution times, there is a trend towards increasing execution parallelism by spreading out data across a large number of machines. However, this often increases the total resource consumption, and especially energy consumption, significantly because of process startup costs and other overheads (e.g., communication overheads). In this dissertation, we develop several data management techniques to minimize resource consumption through workload consolidation. In this dissertation, we introduce a key metric called query span, i.e., number of machines involved in the execution of a query or a job. In order to minimize the per query resource consumption we propose to minimize query span. To that end, we develop several workload-driven data partitioning and replica selection algorithms that attempt to minimize the average query span by exploiting the fact that most distributed environments need to use replication for fault tolerance. Extensive experiments on various datasets show that judicious data placement and replication can dramatically reduce the average query spans resulting in significant reductions in resource consumption. We show our results primarily on two applications, distributed data warehouse system and distributed information retrieval. In the first case, we show that minimizing average query spans can minimize overall resource consumption for a given workload and can also improve the performance of complex analytical queries. In the second case, our approach minimizes the overall search cost as well as effectively trades off search cost with load imbalance. The best case of resource efficiency for any underlying data processing system is achieved when the job or the query can be run efficiently on a single machine (i.e., query span=1). In the final part of dissertation, we discuss an in-memory MapReduce system optimized for performing complex analytics tasks on input data sizes that fit in a single machine's memory. We argue that systems like Hadoop that are designed to operate across a large number of machines are not optimal in performance for small and medium sized complex analytics tasks because of high startup costs, heavy disk activity, and wasteful checkpointing. We have developed a prototype runtime called HONE that is API compatible with standard (distributed) Hadoop. In other words, we can take existing Hadoop code and run it, without modification, on a multi-core shared memory machine. This allows us to take existing Hadoop algorithms and find the most suitable runtime environment for execution on datasets of varying sizes. Overall, in this dissertation, our key contributions in this work include identification of key metric query span and its relationship with overall resource consumption in scale-out architectures. We introduce several workload-aware techniques to optimize this key metric. We go on to demonstrate the effectiveness of query span minimization on different application scenarios. In order to take advantage of scale-up architectures effectively we develop novel in-memory MapReduce system HONE for single machine. Our thorough experiments on real and synthetic datasets demonstrate the efficacy of our proposed approaches

Towards Next Generation Sequential and Parallel SAT Solvers

This thesis focuses on improving the SAT solving technology. The improvements focus on two major subjects: sequential SAT solving and parallel SAT solving. To better understand sequential SAT algorithms, the abstract reduction system Generic CDCL is introduced. With Generic CDCL, the soundness of solving techniques can be modeled. Next, the conflict driven clause learning algorithm is extended with the three techniques local look-ahead, local probing and all UIP learning that allow more global reasoning during search. These techniques improve the performance of the sequential SAT solver Riss. Then, the formula simplification techniques bounded variable addition, covered literal elimination and an advanced cardinality constraint extraction are introduced. By using these techniques, the reasoning of the overall SAT solving tool chain becomes stronger than plain resolution. When using these three techniques in the formula simplification tool Coprocessor before using Riss to solve a formula, the performance can be improved further. Due to the increasing number of cores in CPUs, the scalable parallel SAT solving approach iterative partitioning has been implemented in Pcasso for the multi-core architecture. Related work on parallel SAT solving has been studied to extract main ideas that can improve Pcasso. Besides parallel formula simplification with bounded variable elimination, the major extension is the extended clause sharing level based clause tagging, which builds the basis for conflict driven node killing. The latter allows to better identify unsatisfiable search space partitions. Another improvement is to combine scattering and look-ahead as a superior search space partitioning function. In combination with Coprocessor, the introduced extensions increase the performance of the parallel solver Pcasso. The implemented system turns out to be scalable for the multi-core architecture. Hence iterative partitioning is interesting for future parallel SAT solvers. The implemented solvers participated in international SAT competitions. In 2013 and 2014 Pcasso showed a good performance. Riss in combination with Copro- cessor won several first, second and third prices, including two Kurt-Gödel-Medals. Hence, the introduced algorithms improved modern SAT solving technology

Programming Languages and Systems

This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems