High resolution disaster data clustering using Graphics Processing Units

2013 ◽  
Author(s):  
Kuldeep R. Kurte ◽  
Surya S. Durbha
Keyword(s):  
High Resolution ◽  
Data Clustering ◽  
Graphics Processing Units ◽  
Disaster Data ◽  
Graphics Processing

scholarly journals Data driven global weather predictions at high resolutions

2021 ◽  
Author(s):  
John Taylor ◽  
Pablo Larraonndo ◽  
Bronis de Supinski
Keyword(s):  
High Resolution ◽  
Graphics Processing Units ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Atmospheric Science ◽  
Data Driven ◽  
Research And Innovation ◽  
Future State ◽  
Graphics Processing ◽  
Quantities Of Interest

Abstract Society has benefited enormously from the continuous advancement in numerical weather prediction that has occurred over many decades driven by a combination of outstanding scientific, computational and technological breakthroughs. Here we demonstrate that data driven methods are now positioned to contribute to the next wave of major advances in atmospheric science. We show that data driven models can predict important meteorological quantities of interest to society such as global high resolution precipitation fields (0.25 degrees) and can deliver accurate forecasts of the future state of the atmosphere without prior knowledge of the laws of physics and chemistry. We also show how these data driven methods can be scaled to run on super-computers with up to 1024 modern graphics processing units (GPU) and beyond resulting in rapid training of data driven models, thus supporting a cycle of rapid research and innovation. Taken together, these two results illustrate the significant potential of data driven methods to advance atmospheric science and operational weather forecasting.

scholarly journals Data-driven global weather predictions at high resolutions

2021 ◽  
pp. 109434202110398
Author(s):  
John A Taylor ◽  
Pablo Larraondo ◽  
Bronis R de Supinski
Keyword(s):  
High Resolution ◽  
Graphics Processing Units ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Atmospheric Science ◽  
Data Driven ◽  
Research And Innovation ◽  
Future State ◽  
Graphics Processing ◽  
Quantities Of Interest

Society has benefited enormously from the continuous advancement in numerical weather prediction that has occurred over many decades driven by a combination of outstanding scientific, computational and technological breakthroughs. Here, we demonstrate that data-driven methods are now positioned to contribute to the next wave of major advances in atmospheric science. We show that data-driven models can predict important meteorological quantities of interest to society such as global high resolution precipitation fields (0.25°) and can deliver accurate forecasts of the future state of the atmosphere without prior knowledge of the laws of physics and chemistry. We also show how these data-driven methods can be scaled to run on supercomputers with up to 1024 modern graphics processing units and beyond resulting in rapid training of data-driven models, thus supporting a cycle of rapid research and innovation. Taken together, these two results illustrate the significant potential of data-driven methods to advance atmospheric science and operational weather forecasting.

scholarly journals Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units

2016 ◽  
Vol 41 ◽  
pp. 290-304 ◽  
Author(s):  
Adriane B.S. Serapião ◽  
Guilherme S. Corrêa ◽  
Felipe B. Gonçalves ◽  
Veronica O. Carvalho
Keyword(s):  
Data Clustering ◽  
Graphics Processing Units ◽  
Search Algorithm ◽  
Fish School ◽  
Graphics Processing

scholarly journals GPU-Enabled Shadow Casting for Solar Potential Estimation in Large Urban Areas. Application to the Solar Cadaster of Greater Geneva

2020 ◽  
Vol 10 (15) ◽  
pp. 5361
Author(s):  
Nabil Stendardo ◽  
Gilles Desthieux ◽  
Nabil Abdennadher ◽  
Peter Gallinelli
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Graphics Processing Units ◽  
Large Scale ◽  
View Factor ◽  
Direct Sunlight ◽  
Sky View Factor ◽  
Shadow Casting ◽  
Graphics Processing ◽  
Better Than

In the context of encouraging the development of renewable energy, this paper deals with the description of a software solution for mapping out solar potential in a large scale and in high resolution. We leverage the performance provided by Graphics Processing Units (GPUs) to accelerate shadow casting procedures (used both for direct sunlight exposure and the sky view factor), as well as use off-the-shelf components to compute an average weather pattern for a given area. Application of the approach is presented in the context of the solar cadaster of Greater Geneva (2000 km2). The results show that doing the analysis on a square tile of 3.4 km at a resolution of 0.5 m takes up to two hours, which is better than what we were achieving with the previous work. This shows that GPU-based calculations are highly competitive in the field of solar potential modeling.

scholarly journals Simulations of co-axial jet flows on graphics processing units: the flow and noise analysis

2019 ◽  
Vol 377 (2159) ◽  
pp. 20190083 ◽  
Author(s):  
A. P. Markesteijn ◽  
S. A. Karabasov
Keyword(s):  
High Resolution ◽  
Graphics Processing Units ◽  
Stokes Equations ◽  
Noise Analysis ◽  
Jet Noise ◽  
Jet Flows ◽  
Noise Sources ◽  
Fine Grid ◽  
Graphics Processing ◽  
Noise Models

Large-eddy simulations (LES) are performed for a range of perfectly expanded co-axial jet cases corresponding to conditions of the computation of coaxial jet noise (CoJeN) experiment by QinetiQ. In all simulations, the high-resolution Compact Accurately Boundary-Adjusting high-REsolution Technique (CABARET) is used for solving the Navier–Stokes equations on unstructured meshes. The Ffowcs Williams–Hawkings method based on the penetrable integral surfaces is applied for far-field noise predictions. To correctly model the turbulent flow downstream of the complex nozzle that includes a central body, a wall modelled LES approach is implemented together with a turbulent inflow condition based on synthetic turbulence. All models are run on graphics processing units to enable a considerable reduction of the flow solution time in comparison with the conventional LES. The flow and noise solutions are validated against the experimental data available with 1–2 dB accuracy being reported for noise spectra predictions on the fine grid. To analyse the structure of effective noise sources of the jets, the covariance of turbulent fluctuating Reynolds stresses is computed and their characteristic scales are analysed in the context of the generalized acoustic analogy jet noise models. Motivated by self-similarity of single-stream axi-symmetric jet flows, a suitable non-dimensionalization of the effective jet noise sources of the CoJeN jets is tested, and its implications for low-order jet noise models are discussed. This article is part of the theme issue ‘Frontiers of aeroacoustics research: theory, computation and experiment’.

scholarly journals Extremely fast and accurate open modification spectral library searching of high-resolution mass spectra using feature hashing and graphics processing units

2019 ◽  
Author(s):  
Wout Bittremieux ◽  
Kris Laukens ◽  
William Stafford Noble
Keyword(s):  
High Resolution ◽  
Graphics Processing Units ◽  
Protein Modification ◽  
Large Scale ◽  
Computational Cost ◽  
Selection Procedure ◽  
Spectral Library ◽  
Data Set ◽  
Graphics Processing ◽  
Feature Hashing

AbstractOpen modification searching (OMS) is a powerful search strategy to identify peptides with any type of modification. OMS works by using a very wide precursor mass window to allow modified spectra to match against their unmodified variants, after which the modification types can be inferred from the corresponding precursor mass differences. A disadvantage of this strategy, however, is the large computational cost, because each query spectrum has to be compared against a multitude of candidate peptides.We have previously introduced the ANN-SoLo tool for fast and accurate open spectral library searching. ANN-SoLo uses approximate nearest neighbor indexing to speed up OMS by selecting only a limited number of the most relevant library spectra to compare to an unknown query spectrum. Here we demonstrate how this candidate selection procedure can be further optimized using graphics processing units. Additionally, we introduce a feature hashing scheme to convert high-resolution spectra to low-dimensional vectors. Based on these algorithmic advances, along with low-level code optimizations, the new version of ANN-SoLo is up to an order of magnitude faster than its initial version. This makes it possible to efficiently perform open searches on a large scale to gain a deeper understanding about the protein modification landscape. We demonstrate the computational efficiency and identification performance of ANN-SoLo based on a large data set of the draft human proteome.ANN-SoLo is implemented in Python and C++. It is freely available under the Apache 2.0 license at https://github.com/bittremieux/ANN-SoLo.

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