#PARAVIEW CLUSTER SOFTWARE#

We redesign DB4GeO, our service-based geo-spatio-temporal database architecture, on the way to the parallel management of massive geo-spatial data. In this paper we introduce a sound mathematical approach for a topologically consistent geo-spatio-temporal model based on the concept of the incidence graph. General challenges, concepts, and techniques of big geo-spatial data management are presented. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Representative trends of biomarker predictions in surgically injured vocal folds were observed. The proposed high-performance 3D ABM was verified through comparisons with empirical vocal fold data. The visualization component processes and renders all simulated biological cells and 154 million signaling chemical data points. This simulation tracks 17 million biological cells as well as a total of 1.7 billion signaling chemical and structural protein data points. The case study model was simulated at the physiological scale of a human vocal fold. The new framework is capable of completing the simulation, visualization and remote result delivery in under 7 s per iteration, where each iteration of the simulation represents 30 min in the real world. This high-resolution 3D ABM framework was used for a case study of surgical vocal fold injury and repair.

#PARAVIEW CLUSTER SOFTWARE#

The resulting simulation and visualization software enables users to interact with and steer the course of the simulation in real-time as needed. The scheme was implemented using a client-server protocol allowing the results of each iteration to be analyzed and visualized on the server (i.e., in-situ) while the simulation is running on the same server. Subtasks are further parallelized and convolution-based diffusion is used to enhance the performance of the ABM simulation. The computational scheme was designed to organize the 3D ABM sub-tasks to fully utilize the resources available on current heterogeneous platforms consisting of multi-core CPUs and many-core GPUs. A novel high-performance Agent-Based Model (ABM) was adopted to simulate and visualize multi-scale complex biological processes arising in vocal fold inflammation and repair. A tool capable of estimating treatment success can help prevent unnecessary and costly treatments and potential harmful side effects. Robustness to noise and temporal resolution downsampling is empirically demonstrated.įast and accurate computational biology models offer the prospect of accelerating the development of personalized medicine. We demonstrate the utility of our global framework by extracting critical point trajectories from various simulated time-varying datasets and compare it to the existing methods based on associated overlaps of volumes. Moreover, compared to a modern approximation method, our method provides competitive runtimes while yielding exact results. Extensive experiments on real-life datasets show that our matching approach is an order of magnitude faster than the seminal Munkres algorithm. Merging and splitting events are detected with a geometrical threshold in a post-processing stage. Critical trajectories are constructed by associating successively matched persistence pairs over time. The global framework implements a coarse-grained parallelism by computing persistence diagrams and finding optimal matchings in parallel for every couple of consecutive timesteps. We show that this geometrical lifting has the additional positive side-effect of improving the assignment matrix sparsity and therefore computing time. Second, we propose an extension of the Wasserstein metric that significantly improves the geometrical stability of the matching of domain-embedded persistence pairs. First, we revisit the seminal assignment algorithm by Kuhn and Munkres which we specifically adapt to the problem of matching persistence diagrams in an efficient way. Our approach relies on two main contributions. This fundamentally relies on solving the assignment problem, a special case of optimal transport, for all consecutive timesteps.

Structures are tracked based on the optimal matching between persistence diagrams with respect to the Wasserstein metric. This paper presents a robust and efficient method for tracking topological features in time-varying scalar data.

Paraview cluster