GPU accelerated maximum cardinality matching algorithms for bipartite graphs

We design, implement, and evaluate GPU-based algorithms for the maximum cardinality matching problem in bipartite graphs. Such algorithms have a variety of applications in computer science, scientific computing, bioinformatics, and other areas. To the best of our knowledge, ours is the first study which focuses on GPU implementation of the maximum cardinality matching algorithms. We compare the proposed algorithms with serial and multicore implementations from the literature on a large set of real-life problems where in majority of the cases one of our GPU-accelerated algorithms is demonstrated to be faster than both the sequential and multicore implementations.Comment: 14 pages, 5 figure

Multilevel Threshold Secret and Function Sharing based on the Chinese Remainder Theorem

A recent work of Harn and Fuyou presents the first multilevel (disjunctive) threshold secret sharing scheme based on the Chinese Remainder Theorem. In this work, we first show that the proposed method is not secure and also fails to work with a certain natural setting of the threshold values on compartments. We then propose a secure scheme that works for all threshold settings. In this scheme, we employ a refined version of Asmuth-Bloom secret sharing with a special and generic Asmuth-Bloom sequence called the {\it anchor sequence}. Based on this idea, we also propose the first multilevel conjunctive threshold secret sharing scheme based on the Chinese Remainder Theorem. Lastly, we discuss how the proposed schemes can be used for multilevel threshold function sharing by employing it in a threshold RSA cryptosystem as an example

Threshold cryptography with Chinese remainder theorem

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 84-91.Information security has become much more important since electronic communication is started to be used in our daily life. The content of the term information security varies according to the type and the requirements of the area. However, no matter which algorithms are used, security depends on the secrecy of a key which is supposed to be only known by the agents in the first place. The requirement of the key being secret brings several problems. Storing a secret key on only one person, server or database reduces the security of the system to the security and credibility of that agent. Besides, not having a backup of the key introduces the problem of losing the key if a software/hardware failure occurs. On the other hand, if the key is held by more than one agent an adversary with a desire for the key has more flexibility of choosing the target. Hence the security is reduced to the security of the least secure or least credible of these agents. Secret sharing schemes are introduced to solve the problems above. The main idea of these schemes is to share the secret among the agents such that only predefined coalitions can come together and reveal the secret, while no other coalition can obtain any information about the secret. Thus, the keys used in the areas requiring vital secrecy like large-scale finance applications and commandcontrol mechanisms of nuclear systems, can be stored by using secret sharing schemes. Threshold cryptography deals with a particular type of secret sharing schemes. In threshold cryptography related secret sharing schemes, if the size of a coalition exceeds a bound t, it can reveal the key. And, smaller coalitions can reveal no information about the key. Actually, the first secret sharing scheme in the literature is the threshold scheme of Shamir where he considered the secret as the constant of a polynomial of degree t − 1, and distributed the points on the polynomial to the group of users. Thus, a coalition of size t can recover the polynomial and reveal the key but a smaller coalition can not. This scheme is widely accepted by the researchers and used in several applications. Shamir’s secret sharing scheme is not the only one in the literature. For example, almost concurrently, Blakley proposed another secret sharing scheme depending on planar geometry and Asmuth and Bloom proposed a scheme depending on the Chinese Remainder Theorem. Although these schemes satisfy the necessary and sufficient conditions for the security, they have not been considered for the applications requiring a secret sharing scheme. Secret sharing schemes constituted a building block in several other applications other than the ones mentioned above. These applications simply contain a standard problem in the literature, the function sharing problem. In a function sharing scheme, each user has its own secret as an input to a function and the scheme computes the outcome of the function without revealing the secrets. In the literature, encryption or signature functions of the public key algorithms like RSA, ElGamal and Paillier can be given as an example to the functions shared by using a secret sharing scheme. Even new generation applications like electronic voting require a function sharing scheme. As mentioned before, Shamir’s secret sharing scheme has attracted much of the attention in the literature and other schemes are not considered much. However, as this thesis shows, secret sharing schemes depending on the Chinese Remainder Theorem can be practically used in these applications. Since each application has different needs, Shamir’s secret sharing scheme is used in applications with several extensions. Basically, this thesis investigates how to adapt Chinese Remainder Theorem based secret sharing schemes to the applications in the literature. We first propose some modifications on the Asmuth-Bloom secret sharing scheme and then by using this modified scheme we designed provably secure function sharing schemes and security extensions.Kaya, KamerM.S

Two approximation algorithms for bipartite matching on multicore architectures

International audienceWe propose two heuristics for the bipartite matching problem that are amenable to shared-memory parallelization. The first heuristic is very intriguing from a parallelization perspective. It has no significant algorithmic synchronization overhead and no conflict resolution is needed across threads. We show that this heuristic has an approximation ratio of around 0.632 under some common conditions. The second heuristic is designed to obtain a larger matching by employing the well-known Karp-Sipser heuristic on a judiciously chosen subgraph of the original graph. We show that the Karp-Sipser heuristic always finds a maximum cardinality matching in the chosen subgraph. Although the Karp-Sipser heuristic is hard to parallelize for general graphs, we exploit the structure of the selected subgraphs to propose a specialized implementation which demonstrates very good scalability. We prove that this second heuristic has an approximation guarantee of around 0.866 under the same conditions as in the first algorithm. We discuss parallel implementations of the proposed heuristics on a multicore architecture. Experimental results, for demonstrating speed-ups and verifying the theoretical results in practice, are provided

On partitioning problems with complex objectives

Hypergraph and graph partitioning tools are used to partition work for efficient parallelization of many sparse matrix computations. Most of the time, the objective function that is reduced by these tools relates to reducing the communication requirements, and the balancing constraints satisfied by these tools relate to balancing the work or memory requirements. Sometimes, the objective sought for having balance is a complex function of the partition. We describe some important class of parallel sparse matrix computations that have such balance objectives. For these cases, the current state of the art partitioning tools fall short of being adequate. To the best of our knowledge, there is only a single algorithmic framework in the literature to address such balance objectives. We propose another algorithmic framework to tackle complex objectives and experimentally investigate the proposed framework.Les outils de partitionnement de graphes et d'hypergraphes interviennent pour paralléliser efficacement de nombreux algorithmes liés aux matrices creuses. La plupart du temps, la fonction objectif minimisée par ces outils est liée au besoin de réduire les coûts de communication, tandis que les contraintes d'équilibre à satisfaire sont elles liées à l'équilibrage de la charge ou de la consommation mémoire. Parfois, l'objectif d'équilibre est une fonction complexe du partitionnement. Nous décrivons plusieurs applications majeures de calcul parallèle sur des matrices creuses où de telles contraintes d'équilibre apparaissent. Pour ces exemples, même les outils de partitionnement les plus pointus sont loin d'être adéquats. Pour autant que nous sachions, il n'existe dans la littérature qu'un seul cadre algorithmique qui traite ces problèmes. Nous proposons ici une nouvelle approche algorithmique et fournissons des résultats d'expériences la mettant en œuvre

Investigations on push-relabel based algorithms for the maximum transversal problem

We investigate the push-relabel algorithm for solving the problem of finding a maximum cardinality matching in a bipartite graph in the context of the maximum transversal problem. We describe in detail an optimized yet easy-to-implement version of the algorithm and fine-tune its parameters. We also introduce new performance-enhancing techniques. On a wide range of real-world instances, we compare the push-relabel algorithm with state-of-the-art augmenting path-based algorithms and the recently proposed pseudoflow approach. We conclude that a carefully tuned push-relabel algorithm is competitive with all known augmenting path-based algorithms, and superior to the pseudoflow-based ones.Nous étudions le problème de couplage maximum dans des graphes bipartis. Nous décrivons en détail une version optimisée de l'algorithme en ajustant ses paramètres. L'algorithme est facile à mettre en œuvre. Nous introduisons également de nouvelles techniques pour améliorer la performance de l'algorithme. Sur un large éventail de cas du monde réel, nous comparons l'algorithme Push-Relabel avec des algorithmes basés sur les concepts de chemins augmentants et de pseudoflot récemment proposés. Nous concluons qu'un algorithme de type Push-Relabel soigneusement réglé est en concurrence avec tous les algorithmes connus de type chemins augmentants, et supérieur à ceux de type pseudoflot

Enumerator: an efficient approach for enumerating all valid t-tuples

In this paper, we present an efficient approach for enumerating all valid t-tuples for a given configuration space model, which is an important task in computing covering arrays. The results of our experiments suggest that the proposed approach scales better than existing approaches

Understanding Coarsening for Embedding Large-Scale Graphs

A significant portion of the data today, e.g, social networks, web connections, etc., can be modeled by graphs. A proper analysis of graphs with Machine Learning (ML) algorithms has the potential to yield far-reaching insights into many areas of research and industry. However, the irregular structure of graph data constitutes an obstacle for running ML tasks on graphs such as link prediction, node classification, and anomaly detection. Graph embedding is a compute-intensive process of representing graphs as a set of vectors in a d-dimensional space, which in turn makes it amenable to ML tasks. Many approaches have been proposed in the literature to improve the performance of graph embedding, e.g., using distributed algorithms, accelerators, and pre-processing techniques. Graph coarsening, which can be considered a pre-processing step, is a structural approximation of a given, large graph with a smaller one. As the literature suggests, the cost of embedding significantly decreases when coarsening is employed. In this work, we thoroughly analyze the impact of the coarsening quality on the embedding performance both in terms of speed and accuracy. Our experiments with a state-of-the-art, fast graph embedding tool show that there is an interplay between the coarsening decisions taken and the embedding quality.Comment: 10 pages, 6 figures, submitted to 2020 IEEE International Conference on Big Dat

Fast and high quality topology-aware task mapping

Considering the large number of processors and the size of the interconnection networks on exascale capable supercomputers, mapping concurrently executable and communicating tasks of an application is a complex problem that needs to be dealt with care. For parallel applications, the communication overhead can be a significant bottleneck on scalability. Topology-aware task-mapping methods that map the tasks to the processors (i.e., cores) by exploiting the underlying network information are very effective to avoid, or at worst bend, this limitation. We propose novel, efficient, and effective task mapping algorithms employing a graph model. The experiments show that the methods are faster than the existing approaches proposed for the same task, and on 4096 processors, the algorithms improve the communication hops and link contentions by 16% and 32%, respectively, on the average. In addition, they improve the average execution time of a parallel SpMV kernel and a communication-only application by 9% and 14%, respectively