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Moreover, since there are massive independent approximation processes in the polynomial approximation-based approach, a distributed computing framework is designed to carry out the computation for this method.
As an open source project, Hadoop's distributed computing framework can be used to construct cloud computing environment (distributed computing).
Apache Hadoop project1 is a Java based distributed computing framework built for applications implemented using the MapReduce2 programming model [8].
Although Spark is gaining a lot of attention, we must keep in mind that any open, distributed computing framework remains a complex beast.
To support concurrent mobile applications on the device cloud, a robust distributed computing framework, called Maestro, is proposed.
The goal of this study was to develop a distributed computing framework and system for reverse-engineering of GRN models.
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While repeating full computation of the entire datasets is feasible with distributed computing frameworks such as Hadoop, it is obviously inefficient and wastes resources.
As such, it does not lend itself directly to distributed computing frameworks.
In the past years, many distributed computing frameworks [1 6] have been developed to perform large-scale data processing.
The storage infrastructures are managed by core networking services, embarrassingly parallel distributed computing frameworks, such as Hadoop map-reduce implementations and large-scale virtualization technologies [36].
In this work, we explore and com- pare two distributed computing frameworks implemented on commodity cluster architectures: MPI/OpenMP on Beowulf that is high-performance oriented and exploits multi-machine/multi core infrastructures, and Apache Spark on Hadoop which targets iterative algorithms through in-memory computing.
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