Publication | Closed Access
Implicit Parallelism through Deep Language Embedding
54
Citations
38
References
2015
Year
Unknown Venue
Cluster ComputingEngineeringCross-lingual RepresentationComputer ArchitectureMap-reduceWord EmbeddingsNatural Language ProcessingSyntaxData ScienceComputational LinguisticsData-intensive PlatformData IntegrationMassive Data ProcessingLanguage StudiesParallel ComputingData ManagementMachine TranslationParallel CollectionComputer ScienceData-intensive ComputingDeep Language EmbeddingCloud ComputingParallel ProgrammingComplex DataData-level ParallelismLinguisticsControl Flow StructureBig Data
The appeal of MapReduce has spawned a family of systems that implement or extend it. In order to enable parallel collection processing with User-Defined Functions (UDFs), these systems expose extensions of the MapReduce programming model as library-based dataflow APIs that are tightly coupled to their underlying runtime engine. Expressing data analysis algorithms with complex data and control flow structure using such APIs reveals a number of limitations that impede programmer's productivity.
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