Distributed Collaborative Filtering on a Single Chip Cloud Computer Conference Paper

abstract

• Many-cores on chip have now become a reality. They necessitate the revisit of several layers of a cloud infrastructure. For this to happen, parallel programming runtimes need to be designed for many-cores on chip as the target architecture. In this paper, we show that MapReduce programming paradigm can be adapted to run on Intel's experimental single chip cloud computer (SCC) with 48-cores on chip. We demonstrate this using a Collaborative Filtering (CF) recommender system as an application. CF is widely used in e-commerce deployments to predict user's preference towards an unknown item from their past ratings. We address scalability with data partitioning, combining and sorting algorithms, maximize data locality to minimize communication cost within the SCC cores. We demonstrate 2x speedup, 94% lower power consumption for benchmark workloads as compared to a distributed cluster multi-processor nodes in use today. 2013 IEEE.

name of conference

• 2013 IEEE International Conference on Cloud Engineering (IC2E)

authors

• Mahapatra, Rabinarayan

published proceedings

• PROCEEDINGS OF THE 2013 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING (IC2E 2013)

author list (cited authors)

• Tripathy, A., Patra, A., Mohan, S., & Mahapatra, R.

citation count

• 0

complete list of authors

• Tripathy, Aalap||Patra, Atish||Mohan, Suneil||Mahapatra, Rabi

publication date

• March 2013

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Collaborative Filtering
• Many-core
• Mapreduce
• Personalization
• Recommendation System
• Scalability
• Single Chip Cloud Computer

Digital Object Identifier (DOI)

• 10.1109/IC2E.2013.42

International Standard Book Number (ISBN) 13

• 978-1-4673-6473-7

start page

• 140

end page

• 145

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fic2e.2013.42