CONQUEST: A Coarse-Grained Algorithm for Constructing Summaries of Distributed Discrete Datasets

Abstract. In this paper we present a coarse-grained parallel algorithm, CONQUEST, for constructing boundederror summaries of high-dimensional binary attributed data in a distributed environment. Such summaries enable more expensive analysis techniques to be applied efficiently under constraints on computation, communication, and privacy with little loss in accuracy. While the discrete and high-dimensional nature of the dataset makes the problem difficult in its serial formulation, the loose-coupling of distributed servers hosting the data and the heterogeneity in network bandwidth present additional challenges. CONQUEST is based on a novel linear algebraic tool, PROXIMUS, which is shown to be highly effective on a serial platform. In contrast to traditional fine-grained parallel techniques that distribute the kernel operations, CONQUEST adopts a coarsegrained parallel formulation that relies on the principle of sampling to reduce communication overhead while maintaining high accuracy. ...

• Type: Journal
• Year: 2006
• Where: ALGORITHMICA
• Authors: Jie Chi, Mehmet Koyutürk, Ananth Grama
• Keywords: ALGORITHMICA 2006, Binary Attributed Data, Coarse-grained Parallel Algorithm, Parallel Formulation