Department of Computer Science

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https://hdl.handle.net/10919/19035
The Department of Computer Science has an accredited undergraduate program that offers specialized ‘tracks’ of study in key areas. Undergraduates are prepared by graduation for pursuing a computing career or for graduate study. Our active corporate partners program offers internships and permanent employment to our students. Students are encouraged to participate in research experiences during their studies. Capstone courses provide significant team project experiences. The graduate program offers M.S. and Ph.D. degrees, emphasizing thesis work both at the main campus in Blacksburg and at the Northern Virginia Center. About two-thirds of the graduate students are pursuing the Ph.D. degree. The faculty, among whom there are 12 NSF or DOE CAREER Award winners, are active researchers who are visible contributors to the profession and have achieved significant honors.

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Browsing Department of Computer Science by Department "Aerospace and Ocean Engineering"

Now showing 1 - 5 of 5
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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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    Predicting network modules of cell cycle regulators using relative protein abundance statistics
    Oguz, Cihan; Watson, Layne T.; Baumann, William T.; Tyson, John J. (2017-02-28)
    Background Parameter estimation in systems biology is typically done by enforcing experimental observations through an objective function as the parameter space of a model is explored by numerical simulations. Past studies have shown that one usually finds a set of “feasible” parameter vectors that fit the available experimental data equally well, and that these alternative vectors can make different predictions under novel experimental conditions. In this study, we characterize the feasible region of a complex model of the budding yeast cell cycle under a large set of discrete experimental constraints in order to test whether the statistical features of relative protein abundance predictions are influenced by the topology of the cell cycle regulatory network. Results Using differential evolution, we generate an ensemble of feasible parameter vectors that reproduce the phenotypes (viable or inviable) of wild-type yeast cells and 110 mutant strains. We use this ensemble to predict the phenotypes of 129 mutant strains for which experimental data is not available. We identify 86 novel mutants that are predicted to be viable and then rank the cell cycle proteins in terms of their contributions to cumulative variability of relative protein abundance predictions. Proteins involved in “regulation of cell size” and “regulation of G1/S transition” contribute most to predictive variability, whereas proteins involved in “positive regulation of transcription involved in exit from mitosis,” “mitotic spindle assembly checkpoint” and “negative regulation of cyclin-dependent protein kinase by cyclin degradation” contribute the least. These results suggest that the statistics of these predictions may be generating patterns specific to individual network modules (START, S/G2/M, and EXIT). To test this hypothesis, we develop random forest models for predicting the network modules of cell cycle regulators using relative abundance statistics as model inputs. Predictive performance is assessed by the areas under receiver operating characteristics curves (AUC). Our models generate an AUC range of 0.83-0.87 as opposed to randomized models with AUC values around 0.50. Conclusions By using differential evolution and random forest modeling, we show that the model prediction statistics generate distinct network module-specific patterns within the cell cycle network.
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    Predicting the combined effect of multiple genetic variants
    Liu, Mingming; Watson, Layne T.; Zhang, Liqing (2015-07-30)
    Background Many genetic variants have been identified in the human genome. The functional effects of a single variant have been intensively studied. However, the joint effects of multiple variants in the same genes have been largely ignored due to their complexity or lack of data. This paper uses HMMvar, a hidden Markov model based approach, to investigate the combined effect of multiple variants from the 1000 Genomes Project. Two tumor suppressor genes, TP53 and phosphatase and tensin homolog (PTEN), are also studied for the joint effect of compensatory indel variants. Results Results show that there are cases where the joint effect of having multiple variants in the same genes is significantly different from that of a single variant. The deleterious effect of a single indel variant can be alleviated by their compensatory indels in TP53 and PTEN. Compound mutations in two genes, β-MHC and MyBP-C, leading to severer cardiovascular disease compared to single mutations, are also validated. Conclusions This paper extends the functionality of HMMvar, a tool for assigning a quantitative score to a variant, to measure not only the deleterious effect of a single variant but also the joint effect of multiple variants. HMMvar is the first tool that can predict the functional effects of both single and general multiple variations on proteins. The precomputed scores for multiple variants from the 1000 Genomes Project and the HMMvar package are available at https://bioinformatics.cs.vt.edu/zhanglab/HMMvar/
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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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    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.