Footprint evaluation for volume rendering
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
Modeling communication pipeline latency
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
VVS '89 Proceedings of the 1989 Chapel Hill workshop on Volume visualization
Future Generation Computer Systems - Special issue on metacomputing
Parallel Volume Rendering on a Network of Workstations
IEEE Computer Graphics and Applications
Improving the Throughput of Remote Storage Access through Pipelining
GRID '02 Proceedings of the Third International Workshop on Grid Computing
Image Processing or the Grid: A Toolkit or Building Grid-enabled Image Processing Applications.
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
A Performance Study of Monitoring and Information Services for Distributed Systems
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
Practical Resource Management for Grid-Based Visual Exploration
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
Global telescience featuring IPv6 at iGrid2002
Future Generation Computer Systems - iGrid 2002
Placing pipeline stages on a Grid: Single path and multipath pipeline execution
Future Generation Computer Systems
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In the bio-medical research area, image processing for observed specimen is quite important for effective analysis. For example, 3D reconstruction processing to analyze the specimen structure is required for high performance 2D image observation equipments. It is difficult to preserve enough observation time for researchers on such premium devices. So thus, real time image analysis plays important role. Furthermore, large size data analysis is also required,because data size becomes larger as the resolution of the camera furnished for each device improves. These features require much more processing power and storage space. To realize a real time image processing for the stream data from each device, effective data staging architecture is required. In this paper, we propose architecture to handle stream data processing in a grid environment. We also propose a data staging optimization method using data partitioning.