Future Generation Computer Systems
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
A Vision for Cyberinfrastructure for Coastal Forecasting and Change Analysis
GeoSensor Networks
Optimizing multiple queries on scientific datasets with partial replicas
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
A compile/run-time environment for the automatic transformation of linked list data structures
International Journal of Parallel Programming
Exploiting Latent I/O Asynchrony in Petascale Science Applications
International Journal of High Performance Computing Applications
LCPC'04 Proceedings of the 17th international conference on Languages and Compilers for High Performance Computing
Towards dynamic data-driven optimization of oil well placement
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Supporting User-Defined Subsetting and Aggregation over Parallel NetCDF Datasets
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
SDQuery DSI: integrating data management support with a wide area data transfer protocol
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Analysis of large and/or georgraphically 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.