Perhaps the most utilized and demanded task in data mining is classification. Most existing classification algorithms require all the data used for constructing the model for classification, or at least a good part of...
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Perhaps the most utilized and demanded task in data mining is classification. Most existing classification algorithms require all the data used for constructing the model for classification, or at least a good part of it, to be stored in the memory. This makes them limited by the availability of the memory. We present a parallel algorithm, based on the SPRINT decision tree, which eliminates the dependency on the memory available by storing the data to be processed in a database.
Current distributedcomputing systems comprising of commodity computers like Network of Workstations (NOW) are obliged to deploy multicore processors to raise their performance. However, because multicore processors w...
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Current distributedcomputing systems comprising of commodity computers like Network of Workstations (NOW) are obliged to deploy multicore processors to raise their performance. However, because multicore processors were absent when traditional standard programming models and APIs for distributedcomputing such as MPI and PVM were designed, traditional models are not suitable for programming multicore processors. In this paper, we argue in favor of a powerful programming model called the task-oriented programming model. This model is recently used for programming applications for both multicore processors and distributedcomputing systems such as computational grids. We argue that because of simplicity and the ability of automatic scaling of applications developed under this model, the task-oriented programming model fits the requirements of programming multicore enabled systems better than traditional models like message passing or multi-threading.
Optimization of the task scheduling represent one of the most important open issues of large scale distributed systems. Generally, the overall performance of a distributed system is highly influenced by the quality of...
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Optimization of the task scheduling represent one of the most important open issues of large scale distributed systems. Generally, the overall performance of a distributed system is highly influenced by the quality of the scheduling solution. This paper addresses the problem of dependent task scheduling, by proposing an innovative solution based on a memetic algorithm that combines the advantages of both imuune and genetic algorithm. The experiments proved that the proposed algorithm converges very fast and provides near-optimal solution by minimizing the make span (or schedule length).
Orchestrating composite applications inside distributed systems requires complex coordination. In this frame workflow orchestration engines provide a viable solution. Contrary to their centralized counterparts, decent...
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Orchestrating composite applications inside distributed systems requires complex coordination. In this frame workflow orchestration engines provide a viable solution. Contrary to their centralized counterparts, decentralized workflow engines allow better scalability, autonomy and increased fault-tolerance. However, most of these systems lack a self-healing mechanism in order to cope with engine failures. This paper presents a distributed workflow engine enhanced with self-healing capabilities and message based communication. The engine is validated against two real case scenarios. Some results regarding transfer time between components and recovery times are also given.
Formal verification is becoming a fundamental step of safety-critical and model-based software development. As part of the verification process, model checking is one of the current advanced techniques to analyze the ...
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Formal verification is becoming a fundamental step of safety-critical and model-based software development. As part of the verification process, model checking is one of the current advanced techniques to analyze the behavior of a system. In this paper, we examine an existing parallel model checking algorithm and we propose improvements to eliminate some computational bottlenecks. Our measurements show that the resulting new algorithm has better scalability and performance than both the former parallel approach and the sequential algorithm.
Copyright and Reprint Permissions: Abstracting is permitted with credit to the source. Libraries may photocopy beyond the limits of US copyright law, for private use of patrons, those articles in this volume that carr...
Copyright and Reprint Permissions: Abstracting is permitted with credit to the source. Libraries may photocopy beyond the limits of US copyright law, for private use of patrons, those articles in this volume that carry a code at the bottom of the first page, provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center. The papers in this book comprise the proceedings of the meeting mentioned on the cover and title page. They reflect the authors' opinions and, in the interests of timely dissemination, are published as presented and without change. Their inclusion in this publication does not necessarily constitute endorsement by the editors or the Institute of Electrical and Electronics Engineers, Inc.
Big data analysis is a main challenge we meet recently. Cloud computing is attracting more and more big data analysis applications, due to its well scalability and fault-tolerance. Some aggregation functions, like SUM...
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Big data analysis is a main challenge we meet recently. Cloud computing is attracting more and more big data analysis applications, due to its well scalability and fault-tolerance. Some aggregation functions, like SUM, can be computed in parallel, because they satisfy distributive law of addition. Unfortunately, some of statistical functions are not naturally parallelizable. That means they do not satisfy distributive law of addition. In this paper, we focus on percentile computing problem. We proposed an iterative-style prediction-based parallel algorithm in a distributed system. Prediction is done through a sampling technique. Experiment results verify the efficiency of our algorithm.
Sequence alignment is one of the most important applications in computational biology, and is used for such diverse tasks as identifying homologous proteins, analyzing gene expression, mapping variations between indiv...
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Summary form only given, as follows. parallelcomputing has become ubiquitous and relates to challenging computational problems in science via business-driven computing to mobile computing. The scope has widened drama...
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Summary form only given, as follows. parallelcomputing has become ubiquitous and relates to challenging computational problems in science via business-driven computing to mobile computing. The scope has widened dramatically over the last decade. This panel will debate and speculate on how the parallelcomputing landscape is expected to change in the years to come. Areas of focus will include: (1) computing platforms: How will we be able to maintain the performance growth of the past and what will be the major challenges in the next 10 years and beyond that? What technical barriers are anticipated and what disruptive technologies are behind the corner? (2) Software: How will software infrastructures evolve to meet performance requirements in the next 10 years and beyond? How will we ever be able to hide parallelism obstacles for the masses and what is the road forward towards that? (3) Algorithms: What will be the major computational problems to tackle in the next 10 years and beyond? What are the most challenging algorithmic problems to solve? (4) Applications: What will be the next wave of grand challenge problems to focus on in the next 10 years and beyond? What will be the major performance driving applications in the general and mobile computing domains? A record of the panel discussion was not made available for publication as part of the conference proceedings.
Our ongoing work is focused on the investigation of the suitability of many core and multicore systems to computational neuroscience simulations. We aim at providing a deeper understanding of the influence the various...
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Our ongoing work is focused on the investigation of the suitability of many core and multicore systems to computational neuroscience simulations. We aim at providing a deeper understanding of the influence the various parallelisation techniques have on the dynamics of spiking neural microcircuits -- as they show an almost chaotic behaviour. For that, we are developing a methodology for the evaluation of parallel implementations of biologically-based neural models and small world neural networks. Following our methodology, we designed a framework to assess the behaviour and performance of different parallel and sequential implementations of simulation models and techniques. We present first results obtained on multicore systems with OpenMP parallelisation of Hodgkin-Huxley based-models.
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