Resource Planning for SPARQL Query Execution on Data Sharing Platforms

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Provided by: RWTH Aachen University
Topic: Data Management
Format: PDF
To increase performance, data sharing platforms often make use of clusters of nodes where certain tasks can be executed in parallel. Resource planning and especially deciding how many processors should be chosen to exploit parallel processing is complex in such a setup as increasing the number of processors does not always improve runtime due to communication overhead. Instead, there are usually an optimum number of processors for which using more or fewer processors leads to less efficient runtimes.
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