Accelerating Large-Scale Simulations of Cortical Neuronal Network Development
Authors: Fumitaka Kawasaki & Michael Stiber
Date: March 2012
Report No.: UWB-CSS-12-01
Abstract: Cultured dissociated cortical cells grown into networks on multi-electrode arrays are used to investigate neuronal network development, activity, plasticity, response to stimuli, the effects of pharmacological agents, etc. We made a computational model of such a neuronal network and studied the interplay of individual neuron activity, cell culture development, and network behavior. For small networks (100 neurons in a 10 10 arrangement), we concluded that our simulations' behaviors were dominated by their limited size. However, increasing network size required huge computational resources: for a single-threaded simulator, a 100 x 100 neuron simulation would take at least 2,000 hours (83 days). To tackle this problem, we ported the network simulator to the GPU. A first, naive implementation performed about 2.4 times faster than the single thread simulator. By progressively modifying the simulator structure, we achieved about 23 times performance gain compared with the single threaded simulator, bringing large-scale simulations into the realm of feasibility. Through this experience, we identified factors that limit performance improvement for this type of application.
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In Situ Data Provenance Capture in Spreadsheets
Author: Hazeline Asuncion
Date: August 2011
Report No.: UWB-CSS-11-01
Abstract: The capture of data provenance is a fundamentally important task in eScience. While provenance can be captured using techniques such as scientific workflows, typically these techniques do not trace internal data manipulations that occur within off-the-shelf analysis tools. Yet it is still essential to capture data provenance within such environments. This paper discusses an in situ provenance approach for spreadsheet data in MS Excel, a commonly used analysis environment among scientists. We describe the design and implementation of an Excel tool that captures provenance unobtrusively in the background, allows for user annotations, provides undo/redo functionality at various levels of task granularity, and presents the captured provenance in an accessible format to support a range of provenance queries for analysis. We also present several motivating use case scenarios and a user evaluation which suggests that our approach is both efficient and useful to scientists.
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