Tissue growth kinetics and interface dynamics depend on the properties of the tissue environment and cell-cell interactions. In cellular environments, substrate heterogeneity and geometry arise from a variety factors, such as the structure of the extracellular matrix and nutrient concentration. We used the CellSim3D model, a kinetic cell division simulator, to investigate the growth kinetics and interface roughness dynamics of epithelial tissue growth on heterogeneous substrates with varying topologies. The results show that the presence of quenched disorder has a clear effect on the colony morphology and the roughness scaling of the interface in the moving interface regime. In a medium with quenched disorder, the tissue interface has a smaller interface roughness exponent, α, and a larger growth exponent, β. The scaling exponents also depend on the topology of the substrate and cannot be categorized by well-known universality classes.

The universality of interfacial roughness in growing epithelial tissue has remained a controversial issue. Kardar-Parisi-Zhang (KPZ) and molecular beam epitaxy (MBE) universality classes have been reported among other behaviors including a total lack of universality. Here, we simulate tissues using the cellsim3d kinetic division model for deformable cells to investigate cell-colony scaling. With seemingly minor model changes, it can reproduce both KPZ- and MBE-like scaling in configurations that mimic the respective experiments. Tissue growth with strong cell-cell adhesion in a linear geometry is KPZ like, while weakly adhesive tissues in a radial geometry are MBE like. This result neutralizes the apparent scaling controversy.

Universality of interfacial roughness in growing epithelial tissue has remained a controversial issue. Kardar-Parisi-Zhang (KPZ) and Molecular Beam Epitaxy (MBE) universality classes have been reported among other behaviors including total lack of universality. Here, we utilize a kinetic division model for deformable cells to investigate cell-colony scaling. With seemingly minor model changes, it can reproduce both KPZ- and MBE-like scaling in configurations that mimic the respective experiments. This result neutralizes the apparent scaling controversy. It can be speculated that this diversity in growth behavior is beneficial for efficient evolution and versatile growth dynamics.

Seawaters exhibit various types of cyclic and trend-like temporal alterations in their biological, physical, and chemical processes. Surface water dynamics may vary, for instance, when the timings, durations, or amplitudes of seasonal developments of water properties alter between years and locations. We introduce a workflow using remote sensing to identify surface waters undergoing similar dynamics. The method, called ocean surface dynamics partitioning, classifies pixels based on their temporal change patterns instead of their properties at successive time snapshots. We apply an efficient parallel computing method to calculate Dynamic Time Warping (DTW) time series distances of large datasets of Earth Observation MERIS-instrument reflectance data Rrs(510 nm) and Rrs(620 nm), and produce a matrix of time series distances between 12,252 locations/time series in the Baltic Sea, for both wavelengths. We define cluster prototypes by hierarchical clustering of distance matrices and use them as initial prototypes for an iterative process of partitional clustering in order to identify areas that have similar reflectance dynamics. Lastly, we compute distances from the time series of the reflectance data to selected physical factors (wind, precipitation, and changes in sea surface temperature) obtained from Copernicus data archives. The workflow is reproducible and capable of managing large datasets in reasonable computation times and identifying areas of distinctive dynamics. The results show spatially coherent and logical areas without a priori information about the locations of the satellite image time series. The alignments of the reflectance time series vs. the observational time series of the physical environment clarify the causalities behind the cluster formation. We conclude that following the changes in an aquatic realm by biogeochemical observations at certain temporal intervals alone is not sufficient to identify environmental shifts. We foresee that the changes in dynamics are a sensitive measure of environmental threats and therefore they will be important to follow in the future.

Considerable experimental and theoretical research has been dedicated to understanding the connection between the biochemical activity of cells and their mechanical environment. This is exemplified by the common structures of developing epithelial cells between various species and the decay of cell population growth rate over time. We study these two phenomena in a system of simulated cells with identical mechanical properties, and growth factors in both epithelial and 3D configurations embedded in viscous fluid. We demonstrate that the increase in the density of cellular systems and the consequential crowding of cells by their neighbors are crucial factors in causing the decay of tissue growth rate. We also show that tissue structure can be reproduced by a purely mechanical model with a more faithful treatment of inter-membrane interactions. Finally, we also show that, assuming all other factors constant, growth, and final structure depends on inter-membrane and medium friction coefficients. Of these two, the latter has a stronger influence on slowing down growth and disrupting structure.

We discuss quantum annealing of the two-dimensional transverse-field Ising model on a D-Wave device, encoded on L×L lattices with L≤32. Analyzing the residual energy and deviation from maximal magnetization in the final classical state, we find an optimal L dependent annealing rate v for which the two quantities are minimized. The results are well described by a phenomenological model with two powers of v and L-dependent prefactors to describe the competing effects of reduced quantum fluctuations (for which we see evidence of the Kibble-Zurek mechanism) and increasing noise impact when v is lowered. The same scaling form also describes results of numerical solutions of a transverse-field Ising model with the spins coupled to noise sources. We explain why the optimal annealing time is much longer than the coherence time of the individual qubits.

Bamboo-dominated forests are unusual and interesting because their structure and biomass fluctuate in decades-long cycles corresponding to the flowering and mortality rhythm of the bamboo. In southwestern Amazonia, these forests have been estimated to occupy an area of approximately 160 000 km(2), and a single reproductively synchronized patch can cover up to thousands of square kilometers. Accurate mapping of these forests is challenging, however: the forests are spatially heterogeneous, with bamboo densities varying widely among adjacent sites; much of the area is inaccessible, so field verification of bamboo presence is difficult to obtain and georeferenced records of past flowering events virtually non-existent; and detectability of the bamboo by remote sensing varies considerably during its life cycle. In this study, we develop a supervised time series segmentation approach that allows us to identify both the presence of bamboo forests and the years in which the bamboo flowering and subsequent mortality have occurred. We then apply the method to the entire Landsat TM/ETM+ archive from 1984 to the end of 2018 and validate the classification by visual interpretation of very high resolution imagery. Collecting accurate ground reference data of bamboo presence and bamboo mortality timing is notably difficult in these forests, and we therefore developed a methodology that takes advantage of imperfect reference data obtained from the Landsat time series itself. Our results show that bamboo forests can be differentiated from non-bamboo forests using any of the infrared bands, but band 5 produces the highest classification accuracy. Interestingly, there appears to be a temporal difference in the spectral responses of the three infrared bands to bamboo flowering and mortality: near infrared (band 4) reflectance reacts to the event earlier than shortwave infrared (bands 5 and 7) reflectance. The long Landsat TM/ETM+ archive allows our methodology to detect some areas with two mortality events, with a theoretical maximum interval of 29 years. Analysis of these pixels with repeated mortality confirms that the life cycles of the local bamboo species (Guadua sarcocarpa and G. weberbauerii) last typically 28 years.

We present a high-performance implementation of the lattice-Boltzmann method (LBM) on the Knights Landing generation of Xeon Phi. The Knights Landing architecture includes 16GB of high-speed memory (MCDRAM) with a reported bandwidth of over 400 GB/s, and a subset of the AVX-512 single instruction multiple data (SIMD) instruction set. We explain five critical implementation aspects for high performance on this architecture: (1) the choice of appropriate LBM algorithm, (2) suitable data layout, (3) vectorization of the computation, (4) data prefetching, and (5) running our LBM simulations exclusively from the MCDRAM. The effects of these implementation aspects on the computational performance are demonstrated with the lattice-Boltzmann scheme involving the D3Q19 discrete velocity set and the TRT collision operator. In our benchmark simulations of fluid flow through porous media, using double-precision floating-point arithmetic, the observed performance exceeds 960 million fluid lattice site updates per second.

We present a new open source software package CellSim3D for computer simulations of mechanical aspects (that is, biochemical details are not accounted for) of cell division in three dimensions. It is also possible to use the software in the mode with cell division and growth turned off which allows for simulations of soft colloidal matter. The code is based on a previously introduced two dimensional mechanical model for cell division which is extended to full 3D. CellSim3D is written in C/C++ and CUDA and allows for simulations of 100,000 cells using standard desktop computers. Program summary Program Title: CellSim3D version 1.0 Program Files doi: http://dx.doi.org/10.17632/9ffxhfdtzm.1 Licensing provisions: GPLv2 Programming language: C/C++, CUDA, Python Nature of problem: Mechanical 3-dimensional model for cell division and soft colloidal matter Solution method: Representation of cells as elastic three dimensional spheres with elastic forces, friction, repulsion, attraction and osmotic pressure. Integration of the equations of motion using the velocity- Verlet method from dissipative particle dynamics. Cells with volumes higher than a threshold can divide. Cell division can also be turned off thus allowing for simulations of soft colloidal matter. Additional comments: Software web site: https://github.com/SoftSimu/CellSim3D (C) 2018 Elsevier B.V. All rights reserved.

Computer architectures have evolved into parallel and heterogeneous systems with multi-core CPUs, many-core GPUs, and vector instructions. Meanwhile, advances in data collection technologies have led to a rapid increase in the spatial and temporal resolution of geographic data. Efficiently dealing with large volumes of geographic data demands, now more than ever, an effective use of modern parallel computers. However, parallel programming is distinctly more challenging than writing sequential scripts. Moreover, parallelism is not the only issue; data locality is critical, too. This work addresses the issues of data escalation and parallel transition using a compiler approach to map algebra. More specifically, we design and implement a framework that uses compiler techniques to automatically speed up raster spatial analysis. In this way, users simply write sequential map algebra scripts in Python, which are translated into a graph where optimizations are applied. Then the scripts are parallelized, reordered for locality, and executed on OpenCL devices such as multi-core CPUs and GPUs. The novelty of our approach resides in the efficient organization of the execution, which we achieve via compilation. Unlike interpreters, our framework reorders the raster operations to maximize data reuse and minimize memory movements. The reordering occurs at two hierarchical levels and is controlled by a scheduler and a fusion technique. This strategy targets data locality, which, as we show, is key to the performance of raster spatial analysis. The experiments report speed-ups of one to two orders of magnitude compared to traditional interpreters.

Many spatial phenomena are difficult, dangerous, costly or impossible to reproduce, making their simulation the only option. Wildfires, large floods or epidemics are examples in which computer models are fundamental for the scientific study of their dynamics. Today computer models enable us to perform simulations more accurately than ever before thanks to the increasing spatio-temporal resolution of data. However, more data entails more calculations, and too many calculations quickly become computationally prohibitive. While data and computational demands increase, sequential computers have stagnated and parallel computing seems the way forward. Unfortunately parallel codes are long, complex and not very reusable. Therefore, although modelers require parallel performance, they prefer not to give up the comfort of their sequential scripting languages. We address the parallel issue by leveraging compiler techniques that automatically transform sequential python scripts to parallel GPU codes. Our methodology can also process datasets larger than main memory. To make all this possible we restrict our domain to models based on local interactions, e.g. Cellular Automata. We test the automatic parallelization of three CA in land use, hydrology and pest dynamics. The first predicts future urban developments, the second models the flow of water and the third simulates an outbreak of olive fruit fly. The methodology reduces the execution times by harnessing the parallelism of the models and enables the processing of very large datasets, all of this without compromises in programming effort.

The lattice Boltzmann method is a well-established numerical approach for complex fluid flow simulations. Recently, general-purpose graphics processing units (GPUs) have become available as high-performance computing resources at large scale. We report on designing and implementing a lattice Boltzmann solver for multi-GPU systems that achieves 1.79 PFLOPS performance on 16,384 GPUs. To achieve this performance, we introduce a GPU compatible version of the so-called bundle data layout and eliminate the halo sites in order to improve data access alignment. Furthermore, we make use of the possibility to overlap data transfer between the host central processing unit and the device GPU with computing on the GPU. As a benchmark case, we simulate flow in porous media and measure both strong and weak scaling performance with the emphasis being on large-scale simulations using realistic input data.

Viewshed refers to the land area that is visible to an observer placed in a point of a terrain. Due to the advances in remote sensing technologies the volume of data is today beyond the capability of traditional GIS tools and therefore new and fast algorithms become essential. In this paper we present an efficient implementation of the XDRAW algorithm [5] to quickly compute viewsheds on very large digital elevation models. We redesign the algorithm to make it IO-efficient and compatible with modern SIMD architectures. Our implementation is able to compute viewsheds on digital elevation models at the rate of 10 points per second on an Intel quad-core CPU with AVX2 technology, which makes the algorithm suitable for real-time applications. Keywords— viewshed; gis; simd; parallel; real-time

Processing high-resolution digital elevation models (DEMs) can be tedious due to the large size of the data. In uncertainty-aware drainage basin delineation, we apply a Monte Carlo (MC) simulation that further increases the processing demand by two to three orders of magnitude. Utilizing graphics processing units (GPUs) can speed up the programs, but their on-chip random access memory (RAM) limits the size of the DEMs that can be processed efficiently on one GPU. Here, we present a parallel uncertainty-aware drainage basin delineation algorithm and a multinode GPU compute unified device architecture (CUDA) implementation along with scalability benchmarking. All of the computations are run on the GPUs, and the parallel processes communicate using a message-passing interface (MPI) via the host central processing units (CPUs). The implementation can utilize any number of nodes, with one or many GPUs per node. The performance and scalability of the program have been tested with a 10-m DEM covering 390,905 km2, i.e., the entire area of Finland. Performing the drainage basin delineation for the DEM with different numbers of GPUs shows a nearly linear strong scalability.

Quasi-static electromagnetic problems involving, e.g., inductors, are often solved numerically using the finite-element method with magnetic vector and electric scalar potentials. Coupling the inductors to external circuits may however, lead, to large matrix equations that could become bottlenecks in parallel computation systems, particularly for strong scaling when more processors are introduced to scale down the total computation time. It is argued and shown by numerical simulations that the use of reduced support in the finite-element model improves the strong scalability of multiprocessor simulations due to the reduced communication between the global constraint owner processes and the finite-element equation owner processes.

Digital elevation models (DEMs) are widely used in the modeling of surface hydrology, which typically includes the determination of flow directions and flow accumulation. The use of high-resolution DEMs increases the accuracy of flow accumulation computation, but as a drawback, the computational time may become excessively long if large areas are analyzed. In this paper we investigate the use of graphical processing units (GPUs) for efficient flow accumulation calculations. We present two new parallel flow accumulation algorithms based on dependency transfer and topological sorting and compare them to previously published flow transfer and indegree-based algorithms. We benchmark the GPU implementations against industry standards, ArcGIS and SAGA. With the flow-transfer D8 flow routing model and binary input data, a speed up of 19 is achieved compared to ArcGIS and 15 compared to SAGA. We show that on GPUs the topological sort-based flow accumulation algorithm leads on average to a speedup by a factor of 7 over the flow-transfer algorithm. Thus a total speed up of the order of 100 is achieved. We test the algorithms by applying them to the Revised Universal Soil Loss Equation (RUSLE) erosion model. For this purpose we present parallel versions of the slope, LS factor and RUSLE algorithms and show that the RUSLE erosion results for an area of 12km x 24km containing 72 million cells can be calculated in less than a second. Since flow accumulation is needed in many hydrological models, the developed algorithms may find use in many other applications than RUSLE modeling. The algorithm based on topological sorting is particularly promising for dynamic hydrological models where flow accumulations are repeatedly computed over an unchanged DEM.

Improvements in the models of simulating lossy inductive components in the quasi-static approximation in Elmer finite-element software is presented (Elmer, n.d.). Models include laminate stack models for core materials; massive, stranded, and foil winding models for inductors; and coupling to external circuits. The strong scaling parallel performances of the models with different mesh and solver settings are measured in a cluster environment. All models scale well up to at least 96 processes.

Utilization of movement data from mobile sports tracking applications is affected by its inherent biases and sensitivity, which need to be understood when developing value-added services for, e.g., application users and city planners. We have developed a method for generating a privacy-preserving heat map with user diversity (ppDIV), in which the density of trajectories, as well as the diversity of users, is taken into account, thus preventing the bias effects caused by participation inequality. The method is applied to public cycling workouts and compared with privacy-preserving kernel density estimation (ppKDE) focusing only on the density of the recorded trajectories and privacy-preserving user count calculation (ppUCC), which is similar to the quadrat-count of individual application users. An awareness of privacy was introduced to all methods as a data pre-processing step following the principle of k-Anonymity. Calibration results for our heat maps using bicycle counting data gathered by the city of Helsinki are good (R2>0.7) and raise high expectations for utilizing heat maps in a city planning context. This is further supported by the diurnal distribution of the workouts indicating that, in addition to sports-oriented cyclists, many utilitarian cyclists are tracking their commutes. However, sports tracking data can only enrich official in-situ counts with its high spatio-temporal resolution and coverage, not replace them.

View shed refers to the land area that is visible to an observer placed in a point of a terrain. Due to the advances in remote sensing technologies the volume of data is today beyond the capability of traditional GIS tools and therefore new and fast algorithms become essential. In this paper we present an efficient implementation of the XDRAW algorithm [5] to quickly compute view sheds on very large digital elevation models. We redesign the algorithm to make it IO-efficient and compatible with modern SIMD architectures. Our implementation is able to compute view sheds on digital elevation models at the rate of 109 points per second on an Intel quad-core CPU with AVX2 technology, which makes the algorithm suitable for real-time applications.