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

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.