NDLI: Flexible Transformations For Learning Big Data

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Flexible Transformations For Learning Big Data

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Flexible Transformations For Learning Big Data

Content Provider	ACM Digital Library
Author	Mirhoseini, Azalia Koushanfar, Farinaz Darvish Rouhani, Bita Songhori, Ebrahim M.
Abstract	This paper proposes a domain-specific solution for iterative learning of big and dense (non-sparse) datasets. A large host of learning algorithms, including linear and regularized regression techniques, rely on iterative updates on the data connectivity matrix in order to converge to a solution. The performance of such algorithms often severely degrade when it comes to large and dense data. Massive dense datasets not only induce obligatory large number of arithmetics, but they also incur unwanted message passing cost across the processing nodes. Our key observation is that despite the seemingly dense structures, in many applications, data can be transformed into a new space where sparse structures become revealed. We propose a scalable data transformation scheme that enables creating versatile sparse representations of the data. The transformation can be tuned to benefit the underlying platform's cost and constraints. Our evaluations demonstrate significant improvement in energy usage, runtime, and mem.
Starting Page	453
Ending Page	454
Page Count	2
File Format	PDF
ISBN	9781450334860
DOI	10.1145/2745844.2745889
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Big and dense data Subspace sampling Performance optimization Sparse factorization
Content Type	Text
Resource Type	Article

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