The design and implementation of an object-oriented toolkit for 3D graphics and visualization
Proceedings of the 7th conference on Visualization '96
Efficient input and output for scientific simulations
Proceedings of the sixth workshop on I/O in parallel and distributed systems
An Extended Data-Flow Architecture for Data Analysis and Visualization
VIS '95 Proceedings of the 6th conference on Visualization '95
Parallel netCDF: A High-Performance Scientific I/O Interface
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
An overview of the Trilinos project
ACM Transactions on Mathematical Software (TOMS) - Special issue on the Advanced CompuTational Software (ACTS) Collection
Fine-grained Visualization Pipelines and Lazy Functional Languages
IEEE Transactions on Visualization and Computer Graphics
From mesh generation to scientific visualization: an end-to-end approach to parallel supercomputing
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Flexible IO and integration for scientific codes through the adaptable IO system (ADIOS)
CLADE '08 Proceedings of the 6th international workshop on Challenges of large applications in distributed environments
Toward interoperable mesh, geometry and field components for PDE simulation development
Engineering with Computers
Overview of sciDB: large scale array storage, processing and analysis
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
HIO: A Library for High Performance I/O and Data Management
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
Parallel in situ coupling of simulation with a fully featured visualization system
EG PGV'11 Proceedings of the 11th Eurographics conference on Parallel Graphics and Visualization
Streaming-enabled parallel dataflow architecture for multicore systems
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
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When coupling two different mesh-based codes, for example with in situ analytics, the typical strategy is to explicitly copy data (deep copy) from one implementation to another, doing translation in the process. This is necessary because codes usually do not share data model interfaces or implementations. The drawback is that data duplication results in an increased memory footprint for the coupled code. An alternative strategy, which we study in this paper, is to share mesh data through on-demand, fine-grained, run-time data model translation. This saves memory, which is an increasingly scarce resource at exascale, for the increased use of in situ analysis and decreasing memory per core. We study the performance of our method compared against a deep copy with in situ analysis at scale.