This paper presents an efficient and highly scalable algorithm, designed from scratch, to calculate total-viewshed in large high-resolution digital elevation models (DEMs) without restrictions as to ...whether or not the observer is linked to the ground. The keys to the high efficiency of the proposed method are: 1) the selection of a reliable sampling to represent the subareas of study; 2) the use of a compact and stable data structure to store the calculated data; and 3) the high reutilization of data and calculation between the large number of viewpoints. The obtained results demonstrate that the proposed algorithm is the fastest over the most commonly used GIS-software showing very similar numerical accuracy.
The 3D perception of the human eye is more impressive in irregular land surfaces than in flat land surfaces. The quantification of this perception would be very useful in many applications. This ...article presents the first approach to determining the visible volume, which we call the 3D‐viewshed, in each and all the points of a DEM (Digital Elevation Model). Most previous visibility algorithms in GIS (Geographic Information Systems) are based on the concept of a 2D‐viewshed, which determines the number of points that can be seen from an observer in a DEM. Extending such a 2D‐viewshed to 3D space, then to all the DEM‐points, is too expensive computationally since the viewshed computation per se is costly. In this work, we propose the first approach to compute a new visibility metric that quantifies the visible volume from every point of a DEM. In particular, we developed an efficient algorithm with a high data and calculation re‐utilization. This article presents the first total‐3D‐viewshed maps together with validation results and comparative analysis. Using our highly scalable parallel algorithm to compute the total‐3D‐viewshed of a DEM with 4 million points on a Xeon Processor E5‐2698 takes only 1.3 minutes.
Full text
Available for:
BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
This work presents a high-performance algorithm to compute the horizon in very large high-resolution DEMs. We used Stewart's algorithm as the core of our implementation and considered that the ...horizon has three components: the ground, near, and far horizons. To eliminate the edge-effect, we introduced a multi-resolution halo method. Moreover, we used a new data partition approach, to substantially increase the parallelism in the algorithm. In addition, several optimizations have been applied to considerably reduce the number of arithmetical operations in the core of the algorithm. The experimental results have demonstrated that by applying the above-described contributions, the proposed algorithm is more than twice faster than Stewart's algorithm while maintaining the same accuracy.
Full text
Available for:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
44.
Analytical Modeling of Pipeline Parallelism Navarro, A.; Asenjo, R.; Tabik, S. ...
2009 18th International Conference on Parallel Architectures and Compilation Techniques,
2009-Sept.
Conference Proceeding
Open access
Parallel programming is a requirement in the multi-core era. One of the most promising techniques to make parallel programming available for the general users is the use of parallel programming ...patterns. Functional pipeline parallelism is a pattern that is well suited for many emerging applications, such as streaming and "recognition, mining and synthesis" (RMS) workloads. In this paper we develop an analytical model for pipeline parallelism based on queueing theory. The model is useful to both characterize the performance and efficiency of existing implementations and to guide the design of new pipeline algorithms. We demonstrate the usefulness of the model by characterizing and optimizing two of the PARSEC benchmarks, ferret and dedup. We identified two issues with these codes: load imbalance and I/O bottlenecks. We addressed load imbalance using two techniques: i) parallel pipeline stage collapsing; and ii) dynamic scheduling. We implemented these optimizations using pthreads and the threading building blocks (TBB) libraries. We compare the performance of different alternatives and we note that the TBB implementation based on work stealing outperforms all other variants.
Optimizing sophisticated PDE-based filtering methods, such as the Anisotropic Nonlinear Diffusion (AND), to GPUs is complicated and time consuming. In this work, we expressed AND as iterative ...multiple 3D-stencils, where each 3D-stencil is implemented into one kernel, and then we analyzed all possible kernel fusions on the GPU. We experimentally found that fusing dependent stencils with similar concurrency and lower on-chip pressure makes the optimal combination run 1, 52× faster than the next better one.
This paper presents a fast algorithm to compute the global clear-sky
irradiation, appropriate for extended high-resolution Digital Elevation Models (DEMs). The latest equations published in the ...European Solar Radiation Atlas (ESRA) have been used as a starting point for the proposed model and solved using a numerical method. A new calculation reordering has been performed to (1) substantially diminish the computational requirements, and (2) to reduce dependence on both, the DEM size and the simulated period, i.e., the period during which the
irradiation is calculated. All relevant parameters related to shadowing, atmospheric, and climatological factors have been considered. The computational results demonstrate that the obtained implementation is faster by many orders of magnitude than all existing advanced
irradiation models while maintaining accuracy. Although this paper focuses on the clear-sky
irradiation, the developed software also computes the global
irradiation applying a filter that considers the clear-sky index.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Broadband emission from relativistic outflows (jets) of active galactic nuclei (AGN) and gamma-ray bursts (GRBs) contains valuable information about the nature of the jet itself and about the central ...engine which launches it. Using special relativistic hydrodynamics and magnetohydrodynamics simulations we study the dynamics of the jet and its interaction with the surrounding medium. The observational signature of the simulated jets is computed using a radiative transfer code developed specifically for the purpose of computing multi-wavelength, time-dependent, non-thermal emission from astrophysical plasmas. We present results of a series of long-term projects devoted to understanding the dynamics and emission of jets in parsec-scale AGN jets, blazars and the afterglow phase of the GRBs.
Remotely sensed data are dominated by mixed Land Use and Land Cover (LULC) types. Spectral unmixing (SU) is a key technique that disentangles mixed pixels into constituent LULC types and their ...abundance fractions. While existing studies on Deep Learning (DL) for SU typically focus on single time-step hyperspectral (HS) or multispectral (MS) data, our work pioneers SU using MODIS MS time series, addressing missing data with end-to-end DL models. Our approach enhances a Long-Short Term Memory (LSTM)-based model by incorporating geographic, topographic (geo-topographic), and climatic ancillary information. Notably, our method eliminates the need for explicit endmember extraction, instead learning the input-output relationship between mixed spectra and LULC abundances through supervised learning. Experimental results demonstrate that integrating spectral-temporal input data with geo-topographic and climatic information significantly improves the estimation of LULC abundances in mixed pixels. To facilitate this study, we curated a novel labeled dataset for Andalusia (Spain) with monthly MODIS multispectral time series at 460m resolution for 2013. Named Andalusia MultiSpectral MultiTemporal Unmixing (Andalusia-MSMTU), this dataset provides pixel-level annotations of LULC abundances along with ancillary information. The dataset (https://zenodo.org/records/7752348) and code (https://github.com/jrodriguezortega/MSMTU) are available to the public.
It is unquestionable that time series forecasting is of paramount importance in many fields. The most used machine learning models to address time series forecasting tasks are Recurrent Neural ...Networks (RNNs). Typically, those models are built using one of the three most popular cells, ELMAN, Long-Short Term Memory (LSTM), or Gated Recurrent Unit (GRU) cells, each cell has a different structure and implies a different computational cost. However, it is not clear why and when to use each RNN-cell structure. Actually, there is no comprehensive characterization of all the possible time series behaviors and no guidance on what RNN cell structure is the most suitable for each behavior. The objective of this study is two-fold: it presents a comprehensive taxonomy of all-time series behaviors (deterministic, random-walk, nonlinear, long-memory, and chaotic), and provides insights into the best RNN cell structure for each time series behavior. We conducted two experiments: (1) The first experiment evaluates and analyzes the role of each component in the LSTM-Vanilla cell by creating 11 variants based on one alteration in its basic architecture (removing, adding, or substituting one cell component). (2) The second experiment evaluates and analyzes the performance of 20 possible RNN-cell structures. Our results showed that the MGU-SLIM3 cell is the most recommended for deterministic and nonlinear behaviors, the MGU-SLIM2 cell is the most suitable for random-walk behavior, FB1 cell is advocated for long-memory behavior, and LSTM-SLIM1 for chaotic behavior.
It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i.e., the network becomes highly biased to the data it has been trained on. This issue is often ...alleviated using transfer learning, regularization techniques and/or data augmentation. This work presents a new approach, independent but complementary to the previous mentioned techniques, for improving the generalization of DNNs on very small datasets in which the involved classes share many visual features. The proposed methodology, called FuCiTNet (Fusion Class inherent Transformations Network), inspired by GANs, creates as many generators as classes in the problem. Each generator, \(k\), learns the transformations that bring the input image into the k-class domain. We introduce a classification loss in the generators to drive the leaning of specific k-class transformations. Our experiments demonstrate that the proposed transformations improve the generalization of the classification model in three diverse datasets.
PDF
