Apache Spark

Results: 128



#Item
31Implicit Parallelism through Deep Language Embedding Alexander Alexandrov Asterios Katsifodimos Volker Markl

Implicit Parallelism through Deep Language Embedding Alexander Alexandrov Asterios Katsifodimos Volker Markl

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Source URL: www.user.tu-berlin.de

Language: English - Date: 2016-07-21 07:25:50
32Spark.jl: Resilient Distributed Datasets in Julia    Dennis Wilson, Martín Martínez Rivera, Nicole Power, Tim Mickel  [dennisw, martinmr, npower, tmickel]@mit.edu      

Spark.jl: Resilient Distributed Datasets in Julia    Dennis Wilson, Martín Martínez Rivera, Nicole Power, Tim Mickel  [dennisw, martinmr, npower, tmickel]@mit.edu      

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Source URL: css.csail.mit.edu

Language: English - Date: 2015-01-21 21:28:03
33The Stratosphere Platform: Big Data Analytics at Scale Database Systems and Information Management, Technische Universität Berlin Big Data Analysts Still Hard to Find Mathematical Programming

The Stratosphere Platform: Big Data Analytics at Scale Database Systems and Information Management, Technische Universität Berlin Big Data Analysts Still Hard to Find Mathematical Programming

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Source URL: www.user.tu-berlin.de

Language: English - Date: 2016-07-21 07:25:51
34Cask Data Application Platform (CDAP) CDAP is an open source, Apache 2.0 licensed, distributed, application framework for delivering Hadoop solutions. It integrates and abstracts the underlying Hadoop technologies to pr

Cask Data Application Platform (CDAP) CDAP is an open source, Apache 2.0 licensed, distributed, application framework for delivering Hadoop solutions. It integrates and abstracts the underlying Hadoop technologies to pr

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Source URL: customers.cask.co

Language: English - Date: 2016-07-31 17:55:06
35   GearPump	
  –	
  Real-­‐time	
  Streaming	
  Engine	
   Using	
  Akka*	
  	
   	
   Sean	
  Zhong,	
  Kam	
  Kasravi,	
  Huafeng	
  Wang,	
  Manu	
  Zhang,	
  Weihua	
  Jiang	
  

  GearPump  –  Real-­‐time  Streaming  Engine   Using  Akka*       Sean  Zhong,  Kam  Kasravi,  Huafeng  Wang,  Manu  Zhang,  Weihua  Jiang  

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Source URL: downloads.typesafe.com

Language: English - Date: 2014-12-15 11:49:41
36DE GRUYTER OLDENBOURG  it – Information Technology 2016; Galley Proof Editorial Erhard Rahm*

DE GRUYTER OLDENBOURG it – Information Technology 2016; Galley Proof Editorial Erhard Rahm*

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Source URL: dbs.uni-leipzig.de

Language: English - Date: 2016-05-20 03:21:43
37Big Data Meets HPC – Exploiting HPC Technologies for Accelerating Big Data Processing Keynote Talk at HPCAC-Switzerland (Marby Dhabaleswar K. (DK) Panda

Big Data Meets HPC – Exploiting HPC Technologies for Accelerating Big Data Processing Keynote Talk at HPCAC-Switzerland (Marby Dhabaleswar K. (DK) Panda

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Source URL: www.hpcadvisorycouncil.com

Language: English - Date: 2016-04-06 01:18:06
38NSF14start October 1, 2014  Datanet: CIF21 DIBBs: Middleware and High Performance Analytics Libraries for Scalable Data Science • 

NSF14start October 1, 2014 Datanet: CIF21 DIBBs: Middleware and High Performance Analytics Libraries for Scalable Data Science • 

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Source URL: www.nationaldataservice.org

Language: English - Date: 2016-04-06 11:00:39
39Parallel Discrete-Event Simulation on Data Processing Engines Kazuyuki Shudo, Yuya Kato∗ , Takahiro Sugino† , and Masatoshi Hanai‡ Tokyo Institute of Technology  Abstract—Development of a decent parallel simulato

Parallel Discrete-Event Simulation on Data Processing Engines Kazuyuki Shudo, Yuya Kato∗ , Takahiro Sugino† , and Masatoshi Hanai‡ Tokyo Institute of Technology Abstract—Development of a decent parallel simulato

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Source URL: www.shudo.net

Language: English - Date: 2016-07-21 20:34:37
40We explore the trade-offs of performing linear algebra in Apache Spark versus the traditional C and MPI approach by examining three widely-used matrix factorizations: NMF (for physical plausibility), PCA (for its ubiquit

We explore the trade-offs of performing linear algebra in Apache Spark versus the traditional C and MPI approach by examining three widely-used matrix factorizations: NMF (for physical plausibility), PCA (for its ubiquit

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Source URL: mmds-data.org

- Date: 2016-06-23 15:50:48