Center for Computing Research (CCR)
Center for Computing Research (CCR)

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Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation and Completion of Episodic Information

Aimone, James B.; Bernard, Michael; Vineyard, Craig M.; Verzi, Stephen J.

Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach by which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.

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TYPE SAND Report YEAR 2014
DOIOSTI

LDRD Report: Scheduling Irregular Algorithms

Boman, Erik G.

This LDRD project was a campus exec fellowship to fund (in part) Donald Nguyen’s PhD research at UT-Austin. His work has focused on parallel programming models, and scheduling irregular algorithms on shared-memory systems using the Galois framework. Galois provides a simple but powerful way for users and applications to automatically obtain good parallel performance using certain supported data containers. The naïve user can write serial code, while advanced users can optimize performance by advanced features, such as specifying the scheduling policy. Galois was used to parallelize two sparse matrix reordering schemes: RCM and Sloan. Such reordering is important in high-performance computing to obtain better data locality and thus reduce run times.

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TYPE Other Report YEAR 2014
DOIOSTI

Stochastic Particle Real Time Analyzer (SPARTA) Validation and Verification Suite

Gallis, Michael A.; Koehler, Timothy; Plimpton, Steven J.

This report presents the test cases used to verify, validate and demonstrate the features and capabilities of the first release of the 3D Direct Simulation Monte Carlo (DSMC) code SPARTA (Stochastic Real Time Particle Analyzer). The test cases included in this report exercise the most critical capabilities of the code like the accurate representation of physical phenomena (molecular advection and collisions, energy conservation, etc.) and implementation of numerical methods (grid adaptation, load balancing, etc.). Several test cases of simple flow examples are shown to demonstrate that the code can reproduce phenomena predicted by analytical solutions and theory. A number of additional test cases are presented to illustrate the ability of SPARTA to model flow around complicated shapes. In these cases, the results are compared to other well-established codes or theoretical predictions. This compilation of test cases is not exhaustive, and it is anticipated that more cases will be added in the future.

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TYPE SAND Report YEAR 2014
DOIOSTI
Results 8026–8050 of 12,511
Results 8026–8050 of 12,511
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