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Apache Spark, a cluster computing frameworks to efficiently support interactive, and iterative computations.
While repeating full computation of the entire datasets is feasible with distributed computing frameworks such as Hadoop, it is obviously inefficient and wastes resources.
RDDs addresses the problem of data reuse of intermediate results among multiple computations, which cannot be handled efficiently by current proposed cluster computing frameworks such as MapReduce and Dryad [25].
Unlike other parallel computing frameworks, which require application developers explicitly manage inter-process communication, computation in MapReduce is divided into two major phases called map and reduce, separated by an internal shuffle phase of the intermediate results (Fig. 1), and the framework automatically executes those functions in parallel over any number of processors.
The success of MapReduce has sparked the development of a diverse array of cluster computing frameworks.
The success of MapReduce has sparked many efforts to design cluster computing frameworks.
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To achieve the most optimal simulation performance, a parallel computing framework known as Computer Unified Device Architecture (CUDA), introduced by NVIDIA Corporation, was employed.
Furthermore, the ARWS focus on the individual, fitting to the decentralized computing framework, such as distributed computation and cloud computing.
The implementation of the ConvNets classifier was performed using Torch 7, a scientific computing framework for machine learning54.
In this lab, we will be introuducing you to a cluster computing framework called Spark.
About: Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first.
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