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Shirish Tatikonda
Researcher at IBM
Publications - 43
Citations - 1805
Shirish Tatikonda is an academic researcher from IBM. The author has contributed to research in topics: Data structure & Data stream mining. The author has an hindex of 19, co-authored 43 publications receiving 1641 citations. Previous affiliations of Shirish Tatikonda include Yahoo! & Ohio State University.
Papers
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Journal ArticleDOI
From "think like a vertex" to "think like a graph"
TL;DR: This work proposes a new "think like a graph" programming paradigm, and implements this model in a new system, called Giraph++, based on Apache Giraph, an open source implementation of Pregel.
Proceedings ArticleDOI
SystemML: Declarative machine learning on MapReduce
Amol Ghoting,Rajasekar Krishnamurthy,Edwin P. D. Pednault,Berthold Reinwald,Vikas Sindhwani,Shirish Tatikonda,Yuanyuan Tian,Shivakumar Vaithyanathan +7 more
TL;DR: This paper proposes SystemML in which ML algorithms are expressed in a higher-level language and are compiled and executed in a MapReduce environment and describes and empirically evaluate a number of optimization strategies for efficiently executing these algorithms on Hadoop, an open-source mapReduce implementation.
Journal ArticleDOI
SystemML: declarative machine learning on spark
Matthias Boehm,Michael W. Dusenberry,Deron Eriksson,Alexandre V. Evfimievski,Faraz Makari Manshadi,Niketan Pansare,Berthold Reinwald,Frederick Reiss,Prithviraj Sen,Arvind C. Surve,Shirish Tatikonda +10 more
TL;DR: This paper describes SystemML on Apache Spark, end to end, including insights into various optimizer and runtime techniques as well as performance characteristics.
Journal ArticleDOI
Hybrid parallelization strategies for large-scale machine learning in SystemML
Matthias Boehm,Shirish Tatikonda,Berthold Reinwald,Prithviraj Sen,Yuanyuan Tian,Douglas Burdick,Shivakumar Vaithyanathan +6 more
TL;DR: A systematic approach for combining task and data parallelism for large-scale machine learning on top of MapReduce and a novel cost-based optimization framework for automatically creating optimal parallel execution plans are presented.
Patent
Systems and methods for processing machine learning algorithms in a MapReduce environment
Douglas Burdick,Amol Ghoting,Rajasekar Krishnamurthy,Edwin P. D. Pednault,Berthold Reinwald,Vikas Sindhwani,Shirish Tatikonda,Yuanyuan Tian,Shivakumar Vaithyanathan +8 more
TL;DR: In this paper, the authors describe a method for processing Machine Learning (ML) algorithms in a MapReduce environment, which includes parsing the ML algorithm into a plurality of statement blocks in a sequence, and then automatically determining an execution plan for each statement block.