A Parallel Visualization Pipeline for Terascale Earthquake Simulations
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
A study of I/O methods for parallel visualization of large-scale data
Parallel Computing - Parallel graphics and visualization
Time Dependent Processing in a Parallel Pipeline Architecture
IEEE Transactions on Visualization and Computer Graphics
Extreme Scaling of Production Visualization Software on Diverse Architectures
IEEE Computer Graphics and Applications
Accelerating data-intensive science with Gordon and Dash
Proceedings of the 2010 TeraGrid Conference
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
Toward a General I/O Layer for Parallel-Visualization Applications
IEEE Computer Graphics and Applications
Parallel high-resolution climate data analysis using swift
Proceedings of the 2011 ACM international workshop on Many task computing on grids and supercomputers
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
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For many years now, I/O read time has been recognized as the primary bottleneck for parallel visualization and analysis of large-scale data. In this paper, we introduce a model that can estimate the read time for a file stored in a parallel filesystem when given the file access pattern. Read times ultimately depend on how the file is stored and the access pattern used to read the file. The file access pattern will be dictated by the type of parallel decomposition used. We employ spatio-temporal parallelism, which combines both spatial and temporal parallelism, to provide greater flexibility to possible file access patterns. Using our model, we were able to configure the spatio-temporal parallelism to design optimized read access patterns that resulted in a speedup factor of approximately 400 over traditional file access patterns.