Relevant Publications

Sivaramakrishnan Narayanan, Tahsin Kurc, Umit Catalyurek, Joel Saltz,
"Database Support for Data-driven Scientific Applications in the Grid",
Parallel Processing Letters, Vol. 13, No. 2, 245-271, 2003
Abstract: In this paper we describe a services oriented software system to provide basic database support for efficient execution of applications that make use of scientific datasets in the Grid. This system supports two core operations: efficient selection of the data of interest from distributed databases and efficient transfer of data from storage nodes to compute nodes for processing. We present its overall architecture and main components and describe preliminary experimental results.
[get from WorldSciNet] [download pre-publication version as Tech Report]
Sivaramakrishnan Narayanan, Umit Catalyurek, Tahsin Kurc, Xi Zhang, Joel Saltz,
"Applying Database Support for Large Scale Data Driven Science in Distributed Environments",
Proceedings of the Fourth International Workshop on Grid Computing (Grid 2003), 141-148, 2003
Abstract: There is a rapidly growing set of applications, referred to as data driven applications, in which analysis of large amounts of data drives the next steps taken by the scientist, e.g., running new simulations, doing additional measurements, extending the analysis to larger data collections. Critical steps in data analysis are to extract the data of interest from large and potentially distributed datasets and to move it from storage clusters to compute clusters for processing. We have developed a middleware framework, called GridDB-Lite, that is designed to efficiently support these two steps. In this paper, we describe the application of GridDB-Lite in large scale oil reservoir simulation studies and experimentally evaluate several optimizations that can be employed in the GridDB-Lite runtime system.
[get from IEEE]
Li Weng, Gagan Agrawal, Umit Catalyurek, Tahsin Kurc, Sivaramakrishnan Narayanan, Joel Saltz,
"An Approach for Automatic Data Virtualization",
Proceedings of the 13th IEEE International Symposium on High-Performance Distributed Computing (HPDC-13), 24-33, June 2004

Abstract: Analysis of large and/or geographically distributed scientific datasets is emerging as a key component of grid computing. One challenge in this area is that scientific datasets are typically stored as binary or character flat-files, which makes specification of processing much harder. In view of this, there has been recent interest in data virtualization and data services to support such virtualization.

This paper presents an approach for automatically creating data services to support data virtualization. Specifically, we show how a relational table like data abstraction can be supported for complex multi-dimensional scientific datasets that are resident on a cluster. We have designed and implemented a tool that processes SQL queries (with select and where statements) on multi-dimensional datasets. We have designed a meta-data description language that is used for specifying the data layout. From such description, our tool automatically generates efficient data subsetting and access functions.

We have extensively evaluated our system. The key observations from our experiments are as follows. First, our tool can correctly and efficiently handle a variety of different data layouts. Second, our system scales well as the number of nodes or the amount of data is scaled. Third, the performance of the automatically generated code for indexing and extracting functions is quite comparable to the performance of hand-written codes.

[download from IEEE]