Processing large-scale multi-dimensional data in parallel and distributed environments
Parallel Computing - Parallel data-intensive algorithms and applications
Efficient Manipulation of Large Datasets on Heterogeneous Storage Systems
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Active Proxy-G: optimizing the query execution process in the grid
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Batch mode scheduling in grid systems
International Journal of Web and Grid Services
Mapping pipeline skeletons onto heterogeneous platforms
Journal of Parallel and Distributed Computing
Multi-Criteria Scheduling of Pipeline Workflows (and Application To the JPEG Encoder)
International Journal of High Performance Computing Applications
Computational models and heuristic methods for Grid scheduling problems
Future Generation Computer Systems
Cooperative grid jobs scheduling with multi-objective genetic algorithm
ISPA'07 Proceedings of the 5th international conference on Parallel and Distributed Processing and Applications
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The ability to effectively use computational grids for data intensive applications is becoming increasingly important. The distributed, heterogeneous, shared nature of the computing resources provides a significant challenge in developing support for computationally demanding applications. In this paper we describe several performance optimization techniques we have developed for the filter-stream programming framework that we have designed and implemented for programming data intensive applications on the Grid. We present performance results for multiple versions of a medical imaging application on various distributed machine configurations that show the benefits of the optimizations, and also provide evidence that filter-stream programming can be implemented to both efficiently utilize available Grid resources and to provide scalable application performance as additional resources are made available.