Flexible Transformations For Learning Big Data
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
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 15, 2015
- Source ID
- 10.1145/2796314.2745889
Entities
People
- Azalia Mirhoseini
- Bita Darvish Rouhani
- Ebrahim M. Songhori
- Farinaz Koushanfar
Organizations
- Office of Naval Research
- Rice University