Search results for "Intensive Computing"

showing 3 items of 3 documents

Mapreduce in computational biology via hadoop and spark

2017

Bioinformatics has a long history of software solutions developed on multi-core computing systems for solving computational intensive problems. This option suffer from some issues solvable by shifting to Distributed Systems. In particular, the MapReduce computing paradigm, and its implementations, Hadoop and Spark, is becoming increasingly popular in the Bioinformatics field because it allows for virtual-unlimited horizontal scalability while being easy-to-use. Here we provide a qualitative evaluation of some of the most significant MapReduce bioinformatics applications. We also focus on one of these applications to show the importance of correctly engineering an application to fully exploi…

BioinformaticSparkSettore INF/01 - InformaticaExploitbusiness.industryComputer scienceBioinformaticsDistributed computingScalabilityAlgorithm engineeringField (computer science)Distributed computingSoftwareAlgorithm engineering; Bioinformatics; Distributed computing; Hadoop; MapReduce; Scalability; SparkHadoopSpark (mathematics)ScalabilityData-intensive computingMapReducebusinessImplementationAlgorithm engineering
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Accelerating bioinformatics applications via emerging parallel computing systems [Guest editorial]

2015

The papers in this issue focus on advanced parallel computing systems for bioinformatics applications. This papers provide a forum to publish recent advances in the improvement of handling bioinformatics problems on emerging parallel computing systems. These systems can be characterized by exploiting different types of parallelism, including fine-grained versus coarse-grained and thread-level parallelism versus datalevel parallelism versus request-level parallelism. Hence, parallel computing systems based on multi- and many-core CPUs, many-core GPUs, vector processors, or FPGAs offer the promise to massively accelerate many bioinformatics algorithms and applications, ranging from computeint…

Focus (computing)Parallelism (rhetoric)Computer sciencebusiness.industryApplied MathematicsCloud computingParallel computingBioinformaticsComputing MethodologiesGeneticsData-intensive computingUnconventional computingbusinessField-programmable gate arrayMassively parallelBiotechnologyIEEE/ACM Transactions on Computational Biology and Bioinformatics
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Security, QoS and self-management within an end-to-end Cloud Computing environment

2015

Today, Cloud Networking is one of the recent research areas within the Cloud Computing research communities. The main challenges of Cloud Networking concern Quality of Service (QoS) and security guarantee as well as its management in conformance with a corresponding Service Level Agreement (SLA). In this thesis, we propose a framework for resource allocation according to an end-to-end SLA established between a Cloud Service User (CSU) and several Cloud Service Providers (CSPs) within a Cloud Networking environment (Inter-Cloud Broker and Federation architectures). We focus on NaaS and IaaS Cloud services. Then, we propose the self-establishing of several kinds of SLAs and the self-managemen…

Videoconférence[INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI]Cloud NetworkingCalcul IntensifQuality of ServiceSécuritéCloud ComputingGestion AutonomeInter-CloudSecuritySelf-managementVideoconferencingService Level AgreementQualité de ServiceIntensive Computing
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