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All Faculty Deposits

Permanent URI for this collection

https://hdl.handle.net/10919/70873

The "All Faculty Deposits" collection contains works deposited by faculty and appointed delegates from the Elements (EFARs) system. For help with Elements, see Frequently Asked Questions on the Provost's website. In general, items can only be deposited if the item is a scholarly article that is covered by Virginia Tech's open access policy, or the item is openly licensed or in the public domain, or the item is permitted to be posted online under the journal/publisher policy, or the depositor owns the copyright. See Right to Deposit on the VTechWorks Help page. If you have questions email us at vtechworks@vt.edu.

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Browsing All Faculty Deposits by Department "Aerospace and Ocean Engineering"

Now showing 1 - 4 of 4
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    Directive-based GPU programming for computational fluid dynamics
    Pickering, Brent P.; Jackson, Ccharles W.; Scogland, Thomas R. W.; Feng, Wu-chun; Roy, Christopher J. (Pergamon-Elsevier, 2015-07-02)
    Directive-based programming of graphics processing units (GPUs) has recently appeared as a viable alternative to using specialized low-level languages such as CUDA C and OpenCL for general-purpose GPU programming. This technique, which uses ‘‘directive’’ or ‘‘pragma’’ statements to annotate source codes written in traditional high-level languages, is designed to permit a unified code base to serve multiple computational platforms. In this work we analyze the popular OpenACC programming standard, as implemented by the PGI compiler suite, in order to evaluate its utility and performance potential in computational fluid dynamics (CFD) applications. We examine the process of applying the OpenACC Fortran API to a test CFD code that serves as a proxy for a full-scale research code developed at Virginia Tech; this test code is used to asses the performance improvements attainable for our CFD algorithm on common GPU platforms, as well as to determine the modifications that must be made to the original source code in order to run efficiently on the GPU. Performance is measured on several recent GPU architectures from NVIDIA and AMD (using both double and single precision arithmetic) and the accelerator code is benchmarked against a multithreaded CPU version constructed from the same Fortran source code using OpenMP directives. A single NVIDIA Kepler GPU card is found to perform approximately 20! faster than a single CPU core and more than 2! faster than a 16-core Xeon server. An analysis of optimization techniques for OpenACC reveals cases in which manual intervention by the programmer can improve accelerator performance by up to 30% over the default compiler heuristics, although these optimizations are relevant only for specific platforms. Additionally, the use of multiple accelerators with OpenACC is investigated, including an experimental high-level interface for multi-GPU programming that automates scheduling tasks across multiple devices. While the overall performance of the OpenACC code is found to be satisfactory, we also observe some significant limitations and restrictions imposed by the OpenACC API regarding certain useful features of modern Fortran (2003/8); these are sufficient for us to conclude that it would not be practical to apply OpenACC to our full research code at this time due to the amount of refactoring required.
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    Recent Remote Sensing Innovations and Future Direction
    Thomas, Valerie A.; Wynne, Randolph H.; Liknes, Greg C.; Derwin, Jill M.; Coulston, John W.; Brooks, Evan B.; Finco, Mark V.; Saxena, R.; Watson, Layne T.; Moisen, G. G.; Ruefenacht, Bonnie; Megown, Kevin (2017-10-25)
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    Reducing Model-Form Uncertainty in Simulations of Turbulent Flows: From Data Assimilation to Machine Learning
    Xiao, Heng (2016-12-07)
    ANASYS Uncertainty Quantification Workshop. (Invited Lecture, 1.5 hours).
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    A Stochastic Model Correctly Predicts Changes in Budding Yeast Cell Cycle Dynamics upon Periodic Expression of CLN2
    Oguz, Cihan; Palmisano, Alida; Laomettachit, Teeraphan; Watson, Layne T.; Baumann, William T.; Tyson, John J. (PLOS, 2014-05-09)
    In this study, we focus on a recent stochastic budding yeast cell cycle model. First, we estimate the model parameters using extensive data sets: phenotypes of 110 genetic strains, single cell statistics of wild type and cln3 strains. Optimization of stochastic model parameters is achieved by an automated algorithm we recently used for a deterministic cell cycle model. Next, in order to test the predictive ability of the stochastic model, we focus on a recent experimental study in which forced periodic expression of CLN2 cyclin (driven by MET3 promoter in cln3 background) has been used to synchronize budding yeast cell colonies. We demonstrate that the model correctly predicts the experimentally observed synchronization levels and cell cycle statistics of mother and daughter cells under various experimental conditions (numerical data that is not enforced in parameter optimization), in addition to correctly predicting the qualitative changes in size control due to forced CLN2 expression. Our model also generates a novel prediction: under frequent CLN2 expression pulses, G1 phase duration is bimodal among small-born cells. These cells originate from daughters with extended budded periods due to size control during the budded period. This novel prediction and the experimental trends captured by the model illustrate the interplay between cell cycle dynamics, synchronization of cell colonies, and size control in budding yeast.