Zobrazeno 1 - 10
of 789
pro vyhledávání: '"Kirby, Andrew"'
Turbine-wake and farm-atmosphere interactions influence wind farm power production. For large offshore farms, the farm-atmosphere interaction is usually the more significant effect. This study proposes an analytical model of the `momentum availabilit
Externí odkaz:
http://arxiv.org/abs/2306.08088
Autor:
Williams, Josh, Ahlqvist, Haavard, Cunningham, Alexander, Kirby, Andrew, Katz, Ira, Fleming, John, Conway, Joy, Cunningham, Steve, Ozel, Ali, Wolfram, Uwe
For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific fe
Externí odkaz:
http://arxiv.org/abs/2303.01036
Turbine wake and local blockage effects are known to alter wind farm power production in two different ways: (1) by changing the wind speed locally in front of each turbine; and (2) by changing the overall flow resistance in the farm and thus the so-
Externí odkaz:
http://arxiv.org/abs/2301.01699
Turbine wake and farm blockage effects may significantly impact the power produced by large wind farms. In this study, we perform Large-Eddy Simulations (LES) of 50 infinitely large offshore wind farms with different turbine layouts and wind directio
Externí odkaz:
http://arxiv.org/abs/2207.03148
Autor:
Hutchinson, Matthew, Samsi, Siddharth, Arcand, William, Bestor, David, Bergeron, Bill, Byun, Chansup, Houle, Micheal, Hubbell, Matthew, Jones, Micheal, Kepner, Jeremy, Kirby, Andrew, Michaleas, Peter, Milechin, Lauren, Mullen, Julie, Prout, Andrew, Rosa, Antonio, Reuther, Albert, Yee, Charles, Gadepally, Vijay
Over the past few years, there has been significant interest in video action recognition systems and models. However, direct comparison of accuracy and computational performance results remain clouded by differing training environments, hardware spec
Externí odkaz:
http://arxiv.org/abs/2008.09037
Autor:
Samsi, Siddharth, Prout, Andrew, Jones, Michael, Kirby, Andrew, Arcand, Bill, Bergeron, Bill, Bestor, David, Byun, Chansup, Gadepally, Vijay, Houle, Michael, Hubbell, Matthew, Klein, Anna, Michaleas, Peter, Milechin, Lauren, Mullen, Julie, Rosa, Antonio, Yee, Charles, Reuther, Albert, Kepner, Jeremy
Artificial Intelligence/Machine Learning applications require the training of complex models on large amounts of labelled data. The large computational requirements for training deep models have necessitated the development of new methods for faster
Externí odkaz:
http://arxiv.org/abs/2008.08057
Autor:
Byun, Chansup, Kepner, Jeremy, Arcand, William, Bestor, David, Bergeron, Bill, Gadepally, Vijay, Houle, Michael, Hubbell, Matthew, Jones, Michael, Kirby, Andrew, Klein, Anna, Michaleas, Peter, Milechin, Lauren, Mullen, Julie, Prout, Andrew, Rosa, Antonio, Samsi, Siddharth, Yee, Charles, Reuther, Albert
Rapid launch of thousands of jobs is essential for effective interactive supercomputing, big data analysis, and AI algorithm development. Achieving thousands of launches per second has required hardware to be available to receive these jobs. This pap
Externí odkaz:
http://arxiv.org/abs/2008.02223
Autor:
Kepner, Jeremy, Meiners, Chad, Byun, Chansup, McGuire, Sarah, Davis, Timothy, Arcand, William, Bernays, Jonathan, Bestor, David, Bergeron, William, Gadepally, Vijay, Harnasch, Raul, Hubbell, Matthew, Houle, Micheal, Jones, Micheal, Kirby, Andrew, Klein, Anna, Milechin, Lauren, Mullen, Julie, Prout, Andrew, Reuther, Albert, Rosa, Antonio, Samsi, Siddharth, Stetson, Doug, Tse, Adam, Yee, Charles, Michaleas, Peter
Our society has never been more dependent on computer networks. Effective utilization of networks requires a detailed understanding of the normal background behaviors of network traffic. Large-scale measurements of networks are computationally challe
Externí odkaz:
http://arxiv.org/abs/2008.00307
Autor:
Kirby, Andrew C., Samsi, Siddharth, Jones, Michael, Reuther, Albert, Kepner, Jeremy, Gadepally, Vijay
A Multigrid Full Approximation Storage algorithm for solving Deep Residual Networks is developed to enable neural network parallelized layer-wise training and concurrent computational kernel execution on GPUs. This work demonstrates a 10.2x speedup o
Externí odkaz:
http://arxiv.org/abs/2007.07336