Flow fields are an important product of scientific simulations. One popular flow visualization technique is particle advection, in which seeds are traced through the flow field. One use of these traces is to compute a powerful analysis tool called the Finite-Time Lyapunov Exponent (FTLE) field, but no existing particle tracing algorithms scale to the particle injection frequency required for high-resolution FTLE analysis. In this paper, a framework to trace the massive number of particles necessary for FTLE computation is presented. A new approach is explored, in which processes are divided into groups, and are responsible for mutually exclusive spans of time. This pipelining over time intervals reduces overall idle time of processes and decreases I/O overhead. Our parallel FTLE framework is capable of advecting hundreds of millions of particles at once, with performance scaling up to tens of thousands of processes.

Particle tracing for streamline and path line generation is a common method of visualizing vector fields in scientific data, but it is difficult to parallelize efficiently because of demanding and widely varying computational and communication loads. In this paper we scale parallel particle tracing for visualizing steady and unsteady flow fields well beyond previously published results. We configure the 4D domain decomposition into spatial and temporal blocks that combine in-core and out-of-core execution in a flexible way that favors faster run time or smaller memory. We also compare static and dynamic partitioning approaches. Strong and weak scaling curves are presented for tests conducted on an IBM Blue Gene/P machine at up to 32 K processes using a parallel flow visualization library that we are developing. Datasets are derived from computational fluid dynamics simulations of thermal hydraulics, liquid mixing, and combustion.

Because of the ever increasing size of output data from scientific simulations, supercomputers are increasingly relied upon to generate visualizations. One use of supercomputers is to generate field lines from large scale flow fields. When generating field lines in parallel, the vector field is generally decomposed into blocks, which are then assigned to processors. Since various regions of the vector field can have different flow complexity, processors will require varying amounts of computation time to trace their particles, causing load imbalance, and thus limiting the performance speedup. To achieve load-balanced streamline generation, we propose a workload-aware partitioning algorithm to decompose the vector field into partitions with near equal workloads. Since actual workloads are unknown beforehand, we propose a workload estimation algorithm to predict the workload in the local vector field. A graph-based representation of the vector field is employed to generate these estimates. Once the workloads have been estimated, our partitioning algorithm is hierarchically applied to distribute the workload to all partitions. We examine the performance of our workload estimation and workload-aware partitioning algorithm in several timings studies, which demonstrates that by employing these methods, better scalability can be achieved with little overhead.

Abstract Classically, analysis of the time‐varying data acquired during fMRI experiments is done using static activation maps obtained by testing voxels for the presence of significant activity using statistical methods. The models used in these analysis methods have a number of parameters, which profoundly impact the detection of active brain areas. Also, it is hard to study the temporal dependencies and cascading effects of brain activation from these static maps. In this paper, we propose a methodology to visually analyze the time dimension of brain function with a minimum amount of processing, allowing neurologists to verify the correctness of the analysis results, and develop a better understanding of temporal characteristics of the functional behaviour. The system allows studying time‐series data through specific volumes‐of‐interest in the brain‐cortex, the selection of which is guided by a hierarchical clustering algorithm performed in the wavelet domain. We also demonstrate the utility of this tool by presenting results on a real data‐set.

Abstract Neuronal dendrites and their spines affect the connectivity of neural networks, and play a significant role in many neurological conditions. Neuronal function is observed to be closely correlated with the appearance, disappearance and morphology of the spines. Automatic 3‐D reconstruction of neurons from light microscopy images, followed by the identification, classification and visualization of dendritic spines is therefore essential for studying neuronal physiology and biophysical properties. In this paper, we present a method to reconstruct dendrites using a surface representation of the dendrite. The 1‐D skeleton of the dendritic surface is then extracted by a medial geodesic function that is robust and topologically correct. This is followed by a Bayesian identification and classification of the spines. The dendrite and spines are visualized in a manner that displays the spines' types and the inherent uncertainty in identification and classification. We also describe a user study conducted to validate the accuracy of the classification and the efficacy of the visualization.