Implementing data cube construction using a cluster middleware: Algorithms, implementation experience, and performance evaluation

Ge Yang, Ruoming Jin, Gagan Agrawal

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

With increases in the amount of data available for analysis in commercial settings, on line analytical processing (OLAP) and decision support have become important applications for high performance computing. Implementing such applications on clusters requires a lot of expertise and effort, particularly because of the sizes of input and output datasets. In this paper, we describe our experiences in developing one such application using a cluster middleware, called ADR. We focus on the problem of data cube construction, which commonly arises in multi-dimensional OLAP. We show how ADR, originally developed for scientific data intensive applications, can be used for carrying out an efficient and scalable data cube construction implementation. A particular issue with the use of ADR is tiling of output datasets. We present new algorithms that combine interprocessor communication and tiling within each processor. These algorithms preserve the important properties that are desirable from any parallel data cube construction algorithm. We have carried out a detailed evaluation of our implementation. The main results from our experiments are as follows: (1) high speedups are achieved on both dense and sparse datasets, even though we have used simple algorithms that sequentialize a part of the computation; (2) the execution time depends only upon the amount of computation, and does not increase in a super-linear fashion as the dataset size or the number of tiles increases; and (3) as the datasets become more sparse, sequential performance degrades, but the parallel speedups are still quite good. As part of our on-going work in this area, we are also looking at handling a larger number of dimensions and multi-dimensional partitionings. We describe our preliminary theoretical and experimental work in this direction.

Original languageEnglish (US)
Pages (from-to)533-550
Number of pages18
JournalFuture Generation Computer Systems
Volume19
Issue number4
DOIs
StatePublished - May 2003
Externally publishedYes
EventCCGrid 2002 - Berlin, Germany
Duration: May 21 2002May 24 2002

Keywords

  • Cluster middleware
  • Data cube construction
  • Data intensive computing
  • Performance evaluation

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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