Tools and models for high level parallel and Grid programming

When algorithmic skeletons were first introduced by Cole in late 1980 (50) the idea had an almost immediate success. The skeletal approach has been proved to be effective when application algorithms can be expressed in terms of skeletons composition. However, despite both their effectiveness and the progress made in skeletal systems design and implementation, algorithmic skeletons remain absent from mainstream practice. Cole and other researchers, respectively in (51) and (19), focused the problem. They recognized the issues affecting skeletal systems and stated a set of principles that have to be tackled in order to make them more effective and to take skeletal programming into the parallel mainstream. In this thesis we propose tools and models for addressing some among the skeletal programming environments issues. We describe three novel approaches aimed at enhancing skeletons based systems from different angles. First, we present a model we conceived that allows algorithmic skeletons customization exploiting the macro data-flow abstraction. Then we present two results about the exploitation of metaprogramming techniques for the run-time generation and optimization of macro data-flow graphs. In particular, we show how to generate and how to optimize macro data-flow graphs accordingly both to programmers provided non-functional requirements and to execution platform features. The last result we present are the Behavioural Skeletons, an approach aimed at addressing the limitations of skeletal programming environments when used for the development of component-based Grid applications. We validated all the approaches conducting several test, performed exploiting a set of tools we developed

A Holistic Approach for High-level Programming of Next-generation Data-intensive Applications Targeting Distributed Heterogeneous Computing Environment

AbstractThe intrinsic richness and heterogeneity of large amount of data is paired with the extreme complexity in its storing and processing, as well as with the heterogeneity of their processing environments, ranging from super computers to federations of Cloud data-centres. This makes the conception, definition and implementation of software tools for programming applications dealing with very large amount of data really challenging from different perspectives, ranging from technological issues to economic concerns. We propose an approach focused on data-intensive applications that goes beyond the state of the art allowing a seamless exploitation of heterogeneous and distributed resources and satisfying users’ needs on data processing providing a dynamically determined set of features, depending on the running environment, the application, the user requirements

Distributed Current Flow Betweeness Centrality

—The computation of nodes centrality is of great importance for the analysis of graphs. The current flow betweenness is an interesting centrality index that is computed by considering how the information travels along all the possible paths of a graph. The current flow betweenness exploits basic results from electrical circuits, i.e. Kirchhoff’s laws, to evaluate the centrality of vertices. The computation of the current flow betweenness may exceed the computational capability of a single machine for very large graphs composed by millions of nodes. In this paper we propose a solution that estimates the current flow betweenness in a distributed setting, by defining a vertex-centric, gossip-based algorithm. Each node, relying on its local information, in a selfadaptive way generates new flows to improve the betweenness of all the nodes of the graph. Our experimental evaluation shows that our proposal achieves high correlation with the exact current flow betweenness, and provides a good centrality measure for large graphs

GROUP: A Gossip Based Building Community Protocol

The detection of communities of peers characterized by similar interests is currently a challenging research area. To ease the diffusion of relevant data to interested peers, similarity based overlays define links between similar peers by exploiting a similarity function. However, existing solutions neither give a clear definition of peer communities nor define a clear strategy to partition the peers into communities. As a consequence, the spread of the information cannot be confined within a well defined region of an overlay. This paper proposes a distributed protocol for the detection of communities in a P2P network. Our approach is based on the definition of a distributed voting algorithm where each peer chooses the more similar peers among those in a limited neighbourhood range. The identifier of the most representative peer is exploited to identify a community. The paper shows the effectiveness of our approach by presenting a set of experimental results

Behavioural skeletons for component autonomic management on grids

Abstract We present behavioural skeletons for the CoreGrid Component Model, which are an abstraction aimed at simplifying the development of GCMbased self-management applications. Behavioural skeletons abstract component self-man-agent in component-based design as design patterns abstract class design in classic OO development. As here we just want to introduce the behavioural skeleton framework, emphasis is placed on general skeleton structure rather than on their autonomic management policies