Abstract
P-3 is a next-generation Internet computing platform, building upon other experiments and implementing new ideas for high-performance parallel computing in the Internet environment. This paper describes its run-time system, programming model and how it compares to current state-of-the-art systems.

Abstract
Finding and counting the occurrences of a collection of subgraphs within another larger network is a computationally hard problem, closely related to graph isomorphism. The subgraph count is by itself a very powerful characterization of a network and it is crucial for other important network measurements. G-tries are a specialized data-structure designed to store and search for subgraphs. By taking advantage of subgraph common substructure, g-tries can provide considerable speedups over previously used methods. In this paper we present a parallel algorithm based precisely on gtries that is able to efficiently find and count subgraphs. The algorithm relies on randomized receiver-initiated dynamic load balancing and is able to stop its computation at any given time, efficiently store its search position, divide what is left to compute in two halfs, and resume from where it left. We apply our algorithm to several representative real complex networks from various domains and examine its scalability. We obtain an almost linear speedup up to 128 processors, thus allowing us to reach previously unfeasible limits. We showcase the multidisciplinary potential of the algorithm by also applying it to network motif discovery. © 2010 IEEE.

Abstract
We propose a simple model of distribution for mobile processes, independent of the underlying calculus. Conventional processes compute within sites; inter-site computation is achieved by message sending and object migration, both obeying a lexical scope. We focus on the semantics of networks, on programming practice, and on physical realization with current technology. ©1998 Published by Elsevier Science B.V.

Abstract
We address the problem of providing transparent, lightweight, fault-tolerance mechanisms for generic peer-to-peer middleware systems. The main idea is to use the peer-to-peer overlay to provide for fault-tolerance rather than support it higher up in the middleware architecture, e.g. in the form of services. To evaluate our approach we have implemented a fault-tolerant middleware prototype that uses a hierarchical peer-to-peer overlay in which the leaf peers connect to sensors that provide data streams. Clients connect to the root of the overlay and request streams that are routed upwards through intermediate peers in the overlay up to the client. We report encouraging preliminary results for latency, jitter and resource consumption for both the non-faulty and faulty cases.

Abstract
Many natural structures can be naturally represented by complex networks. Discovering network motifs, which are overrepresented patterns of inter-connections, is a computationally hard task related to graph isomorphism. Sequential methods are hindered by an exponential execution time growth when we increase the size of motifs and networks. In this article we study the opportunities for parallelism in existing methods and propose new parallel strategies that adapt and extend one of the most efficient serial methods known from the Fanmod tool. We propose both a master-worker strategy and one with distributed control, in which we employ a randomized receiver initiated methodology capable of providing dynamic load balancing during the whole computation process. Our strategies are capable of dealing both with exact and approximate network motif discovery. We implement and apply our algorithms to a set of representative networks and examine their scalability up to 128 processing cores. We obtain almost linear speedups, showcasing the efficiency of our proposed approach and are able to reach motif sizes that were not previously achievable using conventional serial algorithms.

Abstract
Run-time work distribution in parallel programming systems is usually accomplished through the use of dynamic scheduling heuristics. Their sensitivity to run-time information such as global work-load, task granularity, data dependencies, locality of information, among others, is essential when trying to optimize performance. Adaptive schedulers that base their decisions on feed-back from the system are therefore of special importance. We have developed and used a general purpose parallel programming system, the pSystem, that also served as a test-bed environment on which we have experimented and studied the performance of distinct scheduling heuristics. Currently, we have two versions of the system: one based on Unix processes; and the other on Solaris threads. Threads (particularly user-level threads) are usually associated with low execution overheads, since they require minimal interaction with the operating system kernel This suggests that lower grain parallelism may be more effectively exploited with a thread-based parallel programming system. Performance analysis of both implementations over a Set of well known benchmarks, with various schedulers, shows that threads scale better under higher system loads and/or when the granularity of the tasks being executed is below a given threshold value. This paper starts with a description of the design and implementation of the pSystem computational model, followed by a detailed description of several experiments and the analysis of their results. (C) 1997 John Wiley & Sons, Ltd.