Parallel Computing Research Articles

Theoretical and practical bases of application of parallel computing when solving problems of the numerical method in the educational process

Relevance. The rapid development of the information society highlights the need for high-quality training of computer specialists in Kazakhstan's higher education institutions. Establishing a modern system for teaching numerical methods and parallel computing is crucial for preparing future specialists to be competitive in the evolving labor market. Purpose. The research aims to justify the need for a methodological system to develop the readiness of future computer specialists in Kazakhstan's higher education institutions. This system is essential for ensuring their ability to use parallel computing to solve numerical methods problems. Methodology. The study involved analysing, comparing, systematising, classifying, and generalising theoretical data, as well as using questionnaires to evaluate opinions on the research level and the significance of the problems studied. The practical stage included preparing and implementing experiment materials, and evaluating results using mathematical statistics to verify the relevance of the author's methodology. Results. During the experiment the content of the concepts �willingness of the future specialist to use parallel computing in solving problems of numerical methods�, �hardware technologies of software computing�, and �software technologies of parallel computing� is defined. The signs and features of developing future specialists' willingness to use parallel computing in solving numerical methods problems are revealed. A methodology for this development was created and implemented. During experimental research at Nazarbayev University (Astana), a methodological toolkit was developed to enhance specialists' readiness for parallel computing. This toolkit includes the implementation of components, criteria, and indicators of readiness, along with selected methods for their development. Conclusions. The research results identify key areas for improving the methodology for developing future specialists' readiness to apply parallel computing in solving numerical methods problems. This involves enhancing the components of students' willingness to engage in such activities. The practical value of the work lies in the creation of a methodological system to improve the training of students and future computer technology specialists in Kazakhstan's higher education institutions. Keywords: informatisation of society; computer technologies; engineering education; professional training; methodological tools

GREAT: A scientific software platform for satellite geodesy and multi-source fusion navigation

The software platform named GNSS + REsearch, Application and Teaching (GREAT) has been designed and developed by Wuhan University for satellite geodesy and multi-source fusion navigation. GREAT is a comprehensive software platform based on C++ language, with modular structure, cross-platform capabilities and efficient performance. The software mainly focuses on space geodetic data processing, precise positioning and orbit determination, and multi-source fusion navigation. With the integration of multiple space geodetic techniques at observation level, GREAT can achieve the determination of global geodetic parameters. Meanwhile, GREAT can also achieve real-time precise orbit and clock determination by processing GNSS data from globally distributed stations, and the accuracies are about 3–6 cm for orbit and 0.1–0.4 ns for clock. Based on high-precision orbit and clock products, centimeter-level onboard positioning accuracies can be achieved with PPP-RTK in open-sky scenarios. Furthermore, centimeter-level multi-source fusion navigation can be achieved in urban scenarios though the integrated system of PPP-RTK/MEMS/VIS/LiDAR. Additionally, efficiency optimization algorithms such as parallel computing are applied to enable the software to handle the data-intensive and calculation-intensive situations.

Multi-scale study on the mechanical properties of concrete based on crack propagation under hygrothermal coupling

During the long-term service of structures, the environmental temperature and humidity fields can lead to the evolution of the pore structure within concrete materials, thereby affecting changes in mechanical properties. However, the mechanisms of their influence still need further clarification and are challenging to correlate for prediction. Addressing this specific issue, the current study focuses on evaluating 78 cubic concrete specimens with C30 and C40 strength grades. These specimens were subjected to a 95 % humidity environment at temperatures of 40 °C, 60 °C, and 80 °C for durations of 30, 60, and 90 days. The research involved conducting both microstructural and macroscopic mechanical tests to quantify the evolution of internal hydrates, pore structures, and pore distribution, as well as to assess the changes in the macroscopic mechanical properties of the specimens. To accurately predict the process, a multiscale finite element model (FEM) based on the bilinear continuum cohesive damage model was established. This model quantifies the changes in mechanical properties of mesoscopic three-phase concrete due to microcrack evolution under hygrothermal coupling. To facilitate multiscale simulations between the Representative Volume Element (RVE) and macroscopic scale models, relevant subroutines were written using Umath, and parallel computing was employed to reduce the computational burden. For the macroscopic model in this study, a homogenized concrete cube model with dimensions identical to the experimental specimens was selected. The effectiveness of the parallel computational model was validated by comparing and analyzing the simulation results under uniaxial compression with the experimental data. The findings indicate that hygrothermal action leads to the initiation and expansion of microcracks within the concrete, thereby impacting the alteration of its mechanical properties. In addition, the parallel computing-based multiscale FEM can more accurately quantify the typical damage process of crack propagation while reducing the computational load and may provide relatively accurate predictions of the mechanical characteristics of concrete under the influence of hygrothermal coupling, ensuring effective modeling of macroscopic structural specimens in various temperature and humidity environments.

Introduction to Section of Dynamic Mathematics: Dynamic Sets Theory: Parallel Sprt-Elements and their Applications

There is a need to develop an instrumental mathematical base for new technologies. The task of the work is to create new approaches for this by introducing new concepts and methods. Our mathematics is unusual for a mathematician, because here the fulcrum is the action, and not the result of the action as in classical mathematics. Therefore, our mathematics is adapted not only to obtain results, but also to directly control actions, which will certainly show its benefits on a fundamentally new type of neural networks with directly parallel calculations, for which it was created. Any action has much greater potential than its result. Significance of the article: in a new qualitatively different approach to the study of complex processes through new mathematical hierarchical parallel dynamic structures, in particular those processes that are dealt with by Synergetics. Our approach is not based on deterministic equations that generate self-organization, which is very difficult to study and gives very small results for a very limited class of problems and does not provide the most important thing - the structure of self-organization. We are just starting from the assumed structure of self-organization, since we are interested not so much in the numerical calculation of this as in the structure of self-organization itself, its formation (construction) for the necessary purposes and its management. Although we are also interested in numerical calculations. Nobel laureates in physics 2023 Ferenc Kraus and his colleagues Pierre Agostini and Anna Lhuillier used a short-pulse laser to generate attosecond pulses of light to study the dynamics of electrons in matter. According to our Theory of singularities of the type synthesizing, its action corresponds to singularity ↑I↓ h q , which allows one to reach the upper level of subtle energies to manipulate lower levels. In April 2023, we proposed using a short-pulse laser to achieve the desired goals by a directly parallel neural network [1]. We then proposed the fundamental development of this directly parallel neural network. In the articles new mathematical structures and operators are constructed through one action - “containment” [2-9]. Here, the construction of new mathematical structures and operators is carried out with generalization to any actions. The significance of our articles is in the formation of the presumptive mathematical structure of subtle energies, this is being done for the first time in science, and the presumptive classification of the mathematical structures of subtle energies for the first time. The experiments of the 2022 Nobel laureates Asle Ahlen, John Clauser, Anton Zeilinger and the experiments in chemistry Nazhipa Valitov eloquently demonstrate that we are right and that these studies are necessary. Be that as it may, we created classes of new mathematical structures, new mathematical singularities, i.e., made a contribution to the development of mathematics

Sparse subspace clustering in diverse multiplex network model

The paper considers the DIverse MultiPLEx (DIMPLE) network model, where all layers of the network have the same collection of nodes and are equipped with the Stochastic Block Models. In addition, all layers can be partitioned into groups with the same community structures, although the layers in the same group may have different matrices of block connection probabilities. To the best of our knowledge, the DIMPLE model, introduced in Pensky and Wang (2021), presents the most broad SBM-equipped binary multilayer network model on the same set of nodes and, thus, generalizes a multitude of papers that study more restrictive settings. Under the DIMPLE model, the main task is to identify the groups of layers with the same community structures since the matrices of block connection probabilities act as nuisance parameters under the DIMPLE paradigm. The main contribution of the paper is achieving the strongly consistent between-layer clustering by using Sparse Subspace Clustering (SSC), the well-developed technique in computer vision. In addition, SSC allows to handle much larger networks than spectral clustering, and is perfectly suitable for application of parallel computing. Moreover, our paper is the first one to obtain precision guarantees for SSC when it is applied to binary data.

Speculative computing for AAFM solutions in large-scale product configurations.

Parallel computing is a current algorithmic approach to looking for efficient solutions; that is, to define a set of processes in charge of performing at the same time the same task. Advances in hardware permit the massification of accessibility to and applications of parallel computing. Nonetheless, some algorithms include steps that require or depend on the results of other steps that cannot be parallelized. Speculative computing allows parallelizing those tasks and reviewing different execution flows, which can involve executing invalid steps. Speculative computing solutions should reduce those invalid flows. Product configuration refers to selecting features from a set of available options respecting some configuration constraints; a not complex task for small configurations and models, but a complex one for large-scale scenarios. This article exemplifies a videogame product line feature model and a few configurations, valid and non-valid, respectively. Configuring products of large-scale feature models is a complex and time-demanding task requiring algorithmic solutions. Hence, parallel solutions are highly desired to assist the feature model product configuration tasks. Existing solutions follow a sequential computing approach and include steps that depend on others that cannot be parallelized at all, where the speculative computing approach is necessary. This article describes traditional sequential solutions for conflict detection and diagnosis, two relevant tasks in the automated analysis of feature models, and how to define their speculative parallel version, highlighting their computing improvements. Given the current parallel computing world, we remark on the advantages and current applicability of speculative computing for producing faster algorithmic solutions.

"Extending Amdahl's and Gustafson Baris's Laws by Adding Communication Overheads"

The extension of Amdahl's law and Gustafson-Barsis' law presented in this article provides insight into the relationship between communication costs and network topology in parallel computing. By considering communication latency in parallel computation execution time, these extensions can help researchers and developers optimize the interconnected network architecture of processing nodes and improve the performance of parallel computing systems. Today, parallel computer systems consisting of hundreds and thousands of processing nodes based on multiprocessor chip technology, high-speed optical transmission such as supercomputers are being researched, developed, and applied in many fields many areas. Although chip technology has progressed to the 3 nm process, the network architecture connecting processing nodes continues to be a problem that greatly affects the communication delay in the parallel computation time of the applications. In this paper, extensions of Amdahl's law and Gustafson-Barsis' law are presented with the addition of communication costs depending on the topology of the topology. These extensions provide insight into the relationship between communication costs and network topology in parallel computing. By considering communication latency in parallel computation execution time, these extensions can help researchers and developers optimize the interconnected network architecture of processing nodes and improve the performance of parallel computing systems.

Multifunctional Antimonene-Silver Nanocomposites for Ultra-Multi-Mode and Multi-Analyte Sensing, Parallel and Batch Logic Computing, Long-Text Information Protection.

To simulate life's emergent functions, mining the multiple sensing capabilities of nanosystems, and digitizing networks of transduction signals and molecular interactions, is an ongoing endeavor. Here, multifunctional antimonene-silver nanocomposites (AM-Ag NCs) are synthesized facilely and fused for molecular sensing and digitization applications (including ultra-multi-mode and multi-analyte sensing, parallel and batch logic computing, long-text information protection). By mixing surfactant, AM, Ag+ and Sodium borohydride (NaBH4) at room temperature for 5min, the resulting NCs are comprised of Ag nanoparticles scattered within AM nanosheets and protected by the surfactant. Interestingly, AM-Ag NCs exhibit ultra-multi-mode sensing ability for multiplex metal ions (Hg2+, Fe3+, or Al3+), which significantly improved selectivity (≈2 times) and sensitivity (≈400 times) when analyzing the combined channels. Moreover, multiple sensing capabilities of AM-Ag NCs enable diverse batch and parallel molecular logic computations (including advanced cascaded logic circuits). Ultra-multi-mode selective patterns of AM-Ag NCs to 18 kinds of metal ions can be converted into a series of binary strings by setting the thresholds, and realized high-density, long-text information protection for the first time. This study provides new ideas and paradigms for the preparation and multi-purpose application of 2D nanocomposites, but also offers new directions for the fusion of molecular sensing and informatization.

Computer Algorithm Design and Linearity Analysis of Its Data Structures

Linearity optimization of data structure is in an important position in the design of computer algorithms, and the study of its optimization measures and strategies can help to promote the research of computer algorithms in depth. This paper introduces the multithreading technology, designs a multithreaded inverted sorted cited table on the basis of the data chain table structure, and prunes and optimizes the data in the table and its distribution repetitions, and puts forward the linearity optimization strategy. And the GaBP parallel computer algorithm for banded linear equations is designed by combining this strategy. The linearity analysis of the optimized data structure reveals that the linearity is significantly improved, and the overall linearity is smooth and reasonably skewed. In the calculation of large-scale numerical values, the running time of the Partition number of 30 is only 3756.9 s. The average linear solution speed is only 0.063 s when solving the satisfiable SAT instances, which is 4.631 s less than that of the Minisat algorithm, and the optimization strategy of the data structure linearity proposed in this paper provides a reference scheme to accelerate the speed of computer algorithms in solving the difficult problems, and the design of the computer parallel algorithm provides a reference scheme to accelerate the speed of computer algorithms in solving the difficult problems. The designed computer parallel algorithm provides an effective method for the field of large-scale numerical processing and SAT problem solving.

A Dynamic Programming Approach to the Collision Avoidance of Autonomous Ships

The advancement of autonomous capabilities in maritime navigation has gained significant attention, with a trajectory moving from decision support systems to full autonomy. This push towards autonomy has led to extensive research focusing on collision avoidance, a critical aspect of safe navigation. Among the various possible approaches, dynamic programming is a promising tool for optimizing collision avoidance maneuvers. This paper presents a DP formulation for the collision avoidance of autonomous vessels. We set up the problem framework, formulate it as a multi-stage decision process, define cost functions and constraints focusing on the actual requirements a marine maneuver must comply with, and propose a solution algorithm leveraging parallel computing. Additionally, we present a greedy approximation to reduce algorithm complexity. We put the proposed algorithms to the test in realistic navigation scenarios and also develop an extensive test on a large set of randomly generated scenarios, comparing them with the RRT* algorithm using performance metrics proposed in the literature. The results show the potential benefits of an autonomous navigation or decision support framework.

Approaching the Ideal Linearity in Epitaxial Crystalline-Type Memristor by Controlling Filament Growth.

Brain-inspired neuromorphic computing has attracted widespread attention owing to its ability to perform parallel and energy-efficient computation. However, the synaptic weight of amorphous/polycrystalline oxide based memristor usually exhibits large nonlinear behavior with high asymmetry, which aggravates the complexity of peripheral circuit system. Controllable growth of conductive filaments is highly demanded for achieving the highly linear conductance modulation. However, the stochastic behavior of the filament growth in commonly used amorphous/polycrystalline oxide memristor makes it very challenging. Here, the epitaxially grown Hf0.5Zr0.5O2-based memristor with the linearity and symmetry approaching ideal case is reported. A layer of Cu nanoparticles is inserted into epitaxially grown Hf0.5Zr0.5O2 film to form the grain boundaries due to the breaking of the epitaxial growth. By combining with the local electric field enhancement, the growth of filament is confined in the grain boundaries due to the fact that the diffusion of oxygen vacancy in crystalline lattice is more difficult than that in the grain boundaries. Furthermore, the decimal operation and high-accuracy neural network are demonstrated by utilizing the highly linear and multi-level conductance modulation capacity. This method opens an avenue to control the filament growth for the application of resistance random access memory (RRAM) and neuromorphic computing.

Graph‐based solution for smart grid real‐time operation and control

Graph‐based solution for smart grid real‐time operation and control

Incorporation of adaptive compression into a GPU parallel computing framework for analyzing large-scale vessel trajectories

Automatic Identification System (AIS) offers a wealth of vessel navigation data, which underpins research in maritime data mining, situational awareness, and knowledge discovery within the realm of intelligent transportation systems. The flourishing marine industry has prompted AIS satellites and base stations to generate massive amounts of vessel trajectory data, escalating both data storage and calculation costs. The conventional Douglas-Peucker (DP) algorithm used for trajectory compression sets a uniform threshold, which hampers effective compression. Additionally, compressing and accelerating the computation of large datasets poses a significant challenge in real-world applications. To address these limitations, this paper aims to develop a new Graphics Processing Unit (GPU) parallel computing and compression framework that enables the acceleration of the optimal threshold calculation for each trajectory automatically in maritime big data mining. It achieves this by incorporating a new Adaptive DP with Speed and Course (ADPSC) algorithm, which utilizes the dynamic navigation characteristics of different vessels. It can effectively solve the associated computational time cost concern when using the ADPSC algorithm to compress vast trajectory datasets in the real world. Additionally, this paper proposes a novel evaluation metric for assessing compression efficacy based on the Dynamic Time Warping (DTW) method. Comprehensive experiments encompass vessel trajectory datasets from three representative research areas: Tianjin Port, Chengshan Jiao Promontory, and Caofeidian Port. The experimental results demonstrate that 1) the newly developed ADPSC method outperforms in terms of compression, and 2) the designed GPU parallel computing framework can significantly shorten the compression time for extensive datasets. The GPU-accelerated compression methodology not only minimizes storage and transmission costs for data from both manned and unmanned vessels but also enhances data processing speed, supporting real-time decision-making. From a theoretical perspective, it provides the key to the puzzle of realizing the real-time anti-collision of manned and unmanned ships, particularly in complex waters. It hence makes significant contributions to maritime safety in the autonomous shipping era.

Simultaneous scheduling of jobs and transporters in a hybrid flow shop with collision-free transporter routing: A novel parallel heuristic

A computationally potent two-stage parallel heuristic (TSPH) and a mixed integer linear programming (MILP) are suggested to address the simultaneous scheduling of jobs and transporters in a hybrid flow shop system, wherein multiple transporters, stage omission, transporter eligibility, machine eligibility, and collision-free transporter routing are assumed. To the best of our knowledge, the significance of transporter collision-free routing has not been spotlighted in the literature on flow shop systems. Collision-free routing of transporters is a requisite technical attribute as transport means may collide on routes and break down the whole system. Our MILP and TSPH assume collision-free routing to impede the issue. Being equipped with parallel computing, TSPH can deal with large problems in a considerably short time. To support, TSPH is analogized against the suggested MILP, two-step MILP (TSMILP), and an efficient parallel meta-heuristic (i.e., parallel particle swarm optimization and genetic algorithm (PPSOGA)) that outperformed many literarily prominent meta-heuristics in the literature. The benchmark results uncover that TSPH outclasses both TSMILP and PPSOGA in the quality of solutions. Eventually, utilizing the convergence plot and Nemenyi’s post-hoc procedure for Friedman’s test, it is revealed that the outcomes of TSPH are remarkably more desirable than those of TSMILP and PPSOGA.

Surface Topography Induces and Orients Nematic Swarms of Active Filaments: Considerations for Lab-On-A-Chip Devices.

Surface-bound molecular motors can drive the collective motion of cytoskeletal filaments in the form of nematic bands and polar flocks in reconstituted gliding assays. Although these "swarming transitions" are an emergent property of active filament collisions, they can be controlled and guided by tuning the surface chemistry or topography of the substrate. To date, the impact of surface topography on collective motion in active nematics is only partially understood, with most experimental studies focusing on the escape of a single filament from etched channels. Since the late 1990s, significant progress has been made to utilize the nonequilibrium properties of active filaments and create a range of functional nanodevices relevant to biosensing and parallel computation; however, the complexity of these swarming transitions presents a challenge when attempting to increase filament surface concentrations. In this work, we etch shallow, linear trenches into glass substrates to induce the formation of swarming nematic bands and investigate the mechanisms by which surface topography regulates the two-dimensional (2D) collective motion of driven filamentous actin (F-actin). We demonstrate that nematic swarms only appear at intermediate trench spacings and vanish if the trenches are made too narrow, wide, or tortuous. To rationalize these results, we simulate the F-actin as self-propelled, semiflexible chains subject to a soft, spatially modulated potential that encodes the energetic cost of bending a filament along the edge of a trench. In our model, we hypothesize that an individual filament experiences a penalty when its projected end-to-end distance is smaller than the trench spacing ("bending and turning"). However, chains that span the channel width glide above the trenches in a force- and torque-free manner ("crowd-surfing"). Our simulations demonstrate that collections of filaments form nematic bands only at intermediate trench spacings, consistent with our experimental findings.

Computationally-efficient linear scheme for overlap time-gating spatial frequency domain diffuse optical tomography using an analytical diffusion model.

Time-domain (TD) spatial frequency domain (SFD) diffuse optical tomography (DOT) potentially enables laminar tomography of both the absorption and scattering coefficients. Its full time-resolved-data scheme is expected to enhance performances of the image reconstruction but poses heavy computational costs and also susceptible signal-to-noise ratio (SNR) limits, as compared to the featured-data one. We herein propose a computationally-efficient linear scheme of TD-SFD-DOT, where an analytical solution to the TD phasor diffusion equation for semi-infinite geometry is derived and used to formulate the Jacobian matrices with regard to overlap time-gating data of the time-resolved measurement for improved SNR and reduced redundancy. For better contrasting the absorption and scattering and widely adapted to practically-available resources, we develop an algebraic-reconstruction-technique-based two-step linear inversion procedure with support of a balanced memory-speed strategy and multi-core parallel computation. Both simulations and phantom experiments are performed to validate the effectiveness of the proposed TD-SFD-DOT method and show an achieved tomographic reconstruction at a relative depth resolution of ∼4 mm.

Rapid calculation for avalanche maps by GPGPU-based snow avalanche model

The required time for producing snow avalanche maps is influenced by computation speed of simulations. Commonly, integrating terrain assessment with dynamic flow simulation aids in mapping dangerous areas for human and structural threats. This approach enables the evaluation of avalanche paths, as well as the assessment of flow rate and thickness during avalanche movement. However, the substantial computational cost of the simulation results in long calculation times when using the Central Processing Unit (CPU). In this study, a new rapid snow avalanche simulator was developed by applying massively parallel computation with the General-Purpose computing on Graphics Processing Unit (GPGPU) technique. By avoiding slower data transfer and utilizing faster memory, computational speed could be accelerated up to 80 times faster than conventional simulation using a CPU. Additionally, the rapid calculation models were validated based on the Mt. Nasu event in 2017, and pilot studies of the avalanche map of Mt. Nasu in Japan demonstrated the usefulness of the developed model for vulnerability evaluation. A total of 123 simulations were conducted for each susceptible source area, and all simulations were completed within only 6.5 h. This high-performance calculation can significantly reduce the time cost of producing and expanding conventional avalanche maps.

Acceleration of computations in modelling of processes in complex objects and systems

The development of methods of parallelization of computing processes, which involve the decomposition of the computational domain, is an urgent task in the modeling of complex objects and systems. Complex objects and systems can contain a large number of elements and interactions. Decomposition allows you to break down a system into simpler subsystems, which simplifies the analysis and management of complexity. By dividing the calculation area of the part, it is possible to perform parallel calculations, which increases the efficiency of calculations and reduces simulation time. Domain decomposition makes it easy to scale the model to work with larger or more detailed systems. With the right choice of decomposition methods, the accuracy of the simulation can be improved, since different parts of the system may have different levels of detail and require appropriate methods of additional analysis. Decomposition allows the simulation to be distributed between different participants or devices, which is relevant for distributed systems or collaborative work on a project. In this work, mathematical models are built, which consist in the construction of iterative procedures for "stitching" several areas into a single whole. The models provide for different complexity of calculation domains, which makes it possible to perform different decomposition approaches, in particular, both overlapping and non-overlapping domain decomposition. The obtained mathematical models of subject domain decomposition can be applied to objects and systems that have different geometric complexity. Domain decomposition models that do not use overlap contain different iterative methods of "stitching" on a common boundary depending on the types of boundary conditions (a condition of the first kind is a Dirichlet condition, or a condition of the second year is a Neumann condition), and domain decomposition models with an overlap of two or more areas consist of the minimization problem for constructing the iterative condition of "stitching" areas. It should be noted that the obtained models will work effectively on all applied tasks that describe the dynamic behavior of objects and their systems, but the high degree of efficiency of one model may be lower than the corresponding the degree of effectiveness of another model, since each task is individual. Keywords: mathematical modelling, decomposition of the computational domain, parallelization, optimization, complex objects and systems.

Efficient computation of the geopotential gradient in graphic processing units

Efficient computation of the geopotential gradient is essential for numerical propagators, particularly in scenarios involving low Earth orbits. Conventional geopotential calculations are based on spherical harmonics series, which become computationally demanding as the degree/order increases. This computational burden can be mitigated by means of parallelized algorithms. Additionally, certain situations lend themselves to high parallelization, such as the propagation of space debris catalogs, satellite mega-constellations, or the dispersion of particles resulting from a space collision event. This paper introduces an optimized Graphics Processing Unit (GPU) implementation designed to facilitate extensive parallelization in the geopotential gradient calculation. The formulation developed in this study is not specific to any GPU. However, to illustrate the low-level optimizations necessary for an efficient implementation, we selected the Compute Unified Device Architecture (CUDA) as the dominant and de facto standard in parallel computing. Nevertheless, most of the concepts and optimizations presented in this paper are also valid for other GPU architectures. Built upon the spherical harmonic expansion using the Cunningham formulation, which is well-suited for GPU computations, our implementation offers several variants with different tradeoffs between speed and accuracy. Besides GPU double precision, we introduced a mixed precision arithmetic –a hybrid between single and double precision– that exploits GPU capabilities with a low penalty in accuracy. The proposed algorithm was implemented as a software reusable module, and its performance was evaluated against GMAT, GODOT, and Orekit astrodynamic codes. The algorithm’s accuracy in double precision is comparable to such codes. The mixed precision version showed enough accuracy for LEO satellite propagation, with around 1 m difference in four days. Testing across different CUDA architectures revealed very high speed-up factors compared to a single CPU, reaching a speed-up of 645 for the mixed precision variant and 450 for the double precision one in the propagation of about 3200 objects with a geopotential of degree/order 126 × 126 using an A100 GPU device.

Application of Parallel Computing on Hybrid Inundation Model, Case Study: Chiayi County Flood 23-24 August 2018

A robust 2D inundation model is needed to support flood warning systems in urban areas. The conventional 2D hydrodynamic model uses shallow water equations as the governing equation and is computationally expensive. Although the models have benefited from parallel computation techniques, some issues remain. As an alternative, many flood models have been developed using different approaches, such as Cellular Automata (CA), DEM-based (DBM), and data-driven models. The hybrid inundation model (HIM) was developed by combining the CA-DBM concepts. The purpose of this study is to implement the parallel computation technique to increase the efficiency of HIM. The model performance was evaluated using the historical flood event in Chiayi County, Taiwan. Results showed that there is no significant difference between HIM and TUFLOW in terms of flood depth estimation, even though TUFLOW included the drainage system within the analysis. These results proved that the drainage system was not working during the event. HIM and TUFLOW give underestimated flood depth prediction compared to the observed data. The main reason because the observed data was obtained from local community testimonies. Hence, there might be many uncertainties in the observed data value. Finally, the parallelization process successfully decreased the computation time of the original HIM. The computation was decreased from 450 to 11 minutes depending on the number of cores used in the simulation.