Parallel Computation Techniques Research Articles

An enhanced argument principle algorithm for exact complex transcendental eigenvalue analysis of damped structures

An efficient and reliable complex transcendental eigenvalue algorithm is proposed for exact modal analysis of built-up structures with a generalized damping model. First, the exact damped dynamic stiffness (DDS) formulations for structural elements with frequency-dependent generalized damping models are developed, which can be assembled directly to model complex built-up structures exactly in the frequency domain. An enhanced argument principle algorithm (EAPA) is then proposed to calculate all complex eigenvalues (either distinct or repeated, up to the arbitrarily required precision) within an interested region in the complex plane based on the DDS matrix. The EAPA is essentially enhanced by five newly proposed techniques to improve efficiency, reliability and robustness. In particular, the two-dimensional bisection method, adaptive step-size techniques, data reuse technique, and parallel computation technique improve the efficiency significantly, whereas the pole calculation technique, two-dimensional bisection method, and data reuse technique enhance the reliability greatly. The efficiency, reliability and robustness of the proposed method is demonstrated by finite element method and other algorithms. Moreover, the proposed enhanced argument principle algorithm can also be used as a reliable and efficient zero-finding tool for transcendental functions in general.

Parallel Computing Techniques for Solving Large-Scale Partial Differential Equation

Two-layered partial differential equations (PDEs) will be structured in this investigation using a variety of parallel mathematical techniques, such as Red Dull Gauss Seidel and Gauss Seidel. For the solution of this study, the two-layered PDEs of the elliptic and illustrated sorts were selected. Parallel Virtual Machine (PVM) is used on the correspondence among all chips of the Parallel Handling System. PVM is well-known for its item system, which enables several diverse computers to be used as flexible and easy-to-use concurrent handling resources. An evaluation of the parallel exhibition assessment including the Gauss Seidel and Red Gauss Seidel techniques in terms of execution time, speed, capability, ampleness, and transient execution will follow the graphical presentation of the mathematical results. Since the goal of this work was to create a successful Two-Dimensional PDE Solver (TDPDES), performance evaluations are simple. Numerous design and mathematical sectors will see an improvement in their examination and investigation systems thanks to this new, effective TDPDES technology..

Ultrasound normalized cumulative residual entropy imaging: Theory, methodology, and application

Background and Objective:Ultrasound information entropy imaging is an emerging quantitative ultrasound technique for characterizing local tissue scatterer concentrations and arrangements. However, the commonly used ultrasound Shannon entropy imaging based on histogram-derived discrete probability estimation suffers from the drawbacks of histogram settings dependence and unknown estimator performance. In this paper, we introduced the information-theoretic cumulative residual entropy (CRE) defined in a continuous distribution of cumulative distribution functions as a new entropy measure of ultrasound backscatter envelope uncertainty or complexity, and proposed ultrasound CRE imaging for tissue characterization. Methods:We theoretically analyzed the CRE for Rayleigh and Nakagami distributions and proposed a normalized CRE for characterizing scatterer distribution patterns. We proposed a method based on an empirical cumulative distribution function estimator and a trapezoidal numerical integration for estimating the normalized CRE from ultrasound backscatter envelope signals. We presented an ultrasound normalized CRE imaging scheme based on the normalized CRE estimator and the parallel computation technique. We also conducted theoretical analysis of the differential entropy which is an extension of the Shannon entropy to a continuous distribution, and introduced a method for ultrasound differential entropy estimation and imaging. Monte-Carlo simulation experiments were performed to evaluate the estimation accuracy of the normalized CRE and differential entropy estimators. Phantom simulation and clinical experiments were conducted to evaluate the performance of the proposed normalized CRE imaging in characterizing scatterer concentrations and hepatic steatosis (n = 204), respectively. Results:The theoretical normalized CRE for the Rayleigh distribution was π/4, corresponding to the case where there were ≥10 randomly distributed scatterers within the resolution cell of an ultrasound transducer. The theoretical normalized CRE for the Nakagami distribution decreased as the Nakagami parameter m increased, corresponding to that the ultrasound backscattered statistics varied from pre-Rayleigh to Rayleigh and to post-Rayleigh distributions. Monte-Carlo simulation experiments showed that the proposed normalized CRE and differential entropy estimators can produce a satisfying estimation accuracy even when the size of the test samples is small. Phantom simulation experiments showed that the proposed normalized CRE and differential entropy imaging can characterize scatterer concentrations. Clinical experiments showed that the proposed ultrasound normalized CRE imaging is capable to quantitatively characterize hepatic steatosis, outperforming ultrasound differential entropy imaging and being comparable to ultrasound Shannon entropy and Nakagami imaging. Conclusion:This study sheds light on the theory and methodology of ultrasound normalized CRE. The proposed ultrasound normalized CRE can serve as a new, flexible quantitative ultrasound envelope statistics parameter. The proposed ultrasound normalized CRE imaging may find applications in quantified characterization of biological tissues. Our code will be made available publicly at https://github.com/zhouzhuhuang.

Simulation of the Magnetization Reversal Effect Depending on the Current Pulse Duration within the φ0 Josephson Junction Model Using MPI and OpenMP Parallel Computing Techniques

Simulation of the Magnetization Reversal Effect Depending on the Current Pulse Duration within the φ0 Josephson Junction Model Using MPI and OpenMP Parallel Computing Techniques

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.

A GPU-accelerated 3dBEM supporting the optimization of actively morphing flapping-foil thrusters with application to AUV propulsion

In recent years, flapping thrusters have been getting attention as an eco-friendly alternative to conventional propellers for autonomous underwater vehicles (AUVs) mainly due to their low-frequency operation, advanced maneuverability, and high efficiency. Their operating principle mimics the thrust-producing kinematics found among efficient swimmers in the aquatic environment. The present work focuses on the analysis and optimization of flapping systems with active morphing features for increased performance. The two optimization studies refer to a realistic AUV propulsion scenario for a wing thruster with active hydrofoil-section adjustment, spanwise bend and twist morphing capabilities. A gradient descent approach based on sequential quadratic programming (SQP) is used for optimal tuning of design variables (planform shape, kinematics) targeting efficiency maximization under thrust and effective angle of attack constraints. For the evaluation of each candidate thruster the unsteady boundary element solver 3dBEM is developed and used for wing performance prediction. The solver exploits GPU parallel computation techniques to reduce computational time and enable fast optimization. Results indicate that the optimal thruster with chordline/spanwise bending is 25% more efficient, whereas the same result for the wing with spanwise bending and twist profiles is up to 8.8%. The 3dBEM solver can support the preliminary design process of morphing flapping-foil thrusters.

Ion Optical Optimization Method for an Ultrahigh Resolution Planar Multireflection Time-of-Flight Mass Analyzer Using the Hill Climbing Algorithm.

A novel ion optical optimization method for planar multireflection time-of-flight mass spectrometry (MR-TOFMS) is introduced in this paper. The multiparameters of the gridless mirror model, including geometric and voltage parameters, are automatically optimized using a self-made program created in SIMION 8.1. Combining with the hill climbing algorithm and parallel computing technique, this method substantially enhances optimization efficiency and accuracy. The fitting results demonstrated that the ion optical performance of the gridless mirror reached up to fourth-order isochronicity with respect to the energy spread and third-order isochronicity with respect to the spatial and angular spread. As a result, the gridless mirror model achieved an aberration limit resolution of 1.7 million under realistic ion beam conditions. Due to constraints of periodic lenses, the aberration limit resolution of the planar MR-TOFMS was optimized to 600k. These results indicate that the hill climbing algorithm is an effective method to search the optimal solutions in complex ion optical systems.

UP-DPC: Ultra-scalable parallel density peak clustering

Density Peak Clustering (DPC) is a highly effective density-based clustering algorithm, but its scalability is limited by the expensive Density Peak Estimation (DPE) step. To address this challenge, we propose UP-DPC: Ultra-Scalable Parallel Density Peak Clustering, a novel framework that employs approximate Density Peak Estimation and performs DPC on LDP-wise graphs. This approach enables UP-DPC to handle datasets of arbitrary scale without relying on spatial indexing for acceleration. Furthermore, we introduce a five-layer computational architecture and leverage parallel computation techniques to further enhance the speed and efficiency of UP-DPC.To evaluate the scalability and effectiveness of UP-DPC, we conduct extensive experiments on 14 datasets, including the large/web-scale datasets, and compare UP-DPC with 21 algorithms. Notably, on the MNIST8M dataset consisting of 8,000k data objects, UP-DPC achieves an NMI (Normalized Mutual Information) value of 0.6464 in just 35.41 seconds, outperforming the state-of-the-art GPU-based method, which only archives an NMI of 0.045 in 56.96 seconds. These results demonstrate the superior scalability and effectiveness of UP-DPC in handling large/web-scale datasets. The proposed framework offers significant improvements over existing methods and shows promise as a solution for density-based clustering tasks.

Ultrasound Entropy Imaging Based on the Kernel Density Estimation: A New Approach to Hepatic Steatosis Characterization.

In this paper, we present the kernel density estimation (KDE)-based parallelized ultrasound entropy imaging and apply it for hepatic steatosis characterization. A KDE technique was used to estimate the probability density function (PDF) of ultrasound backscattered signals. The estimated PDF was utilized to estimate the Shannon entropy to construct parametric images. In addition, the parallel computation technique was incorporated. Clinical experiments of hepatic steatosis were conducted to validate the feasibility of the proposed method. Seventy-two participants and 204 patients with different grades of hepatic steatosis were included. The experimental results show that the KDE-based entropy parameter correlates with log10 (hepatic fat fractions) measured by magnetic resonance spectroscopy in the 72 participants (Pearson's r = 0.52, p < 0.0001), and its areas under the receiver operating characteristic curves for diagnosing hepatic steatosis grades ≥ mild, ≥moderate, and ≥severe are 0.65, 0.73, and 0.80, respectively, for the 204 patients. The proposed method overcomes the drawbacks of conventional histogram-based ultrasound entropy imaging, including limited dynamic ranges and histogram settings dependence, although the diagnostic performance is slightly worse than conventional histogram-based entropy imaging. The proposed KDE-based parallelized ultrasound entropy imaging technique may be used as a new ultrasound entropy imaging method for hepatic steatosis characterization.

Sensitivity Study of Seismic Amplification Effect of Large-Scale Sichuan Basin on Key Parameters During the Great Wenchuan Earthquake

The effect of soil parameters on basin amplification effect has been widely investigated, especially for the small and medium-sized basins under plane wave incidence or point source ruptures. However, the sensitivities of seismic amplification effect of large-scale basins with respect to soil parameters during great earthquakes with tremendous rupture faults have obtained relatively less attention. In this paper, based on the established three-dimensional Sichuan basin model, the effects of four material parameters of the basin sediments, including the shear wave velocity, Poisson’s ratio, and the shear quality factor of the Quaternary sediment, as well as the velocity of the rock above the basin crystalline basement (which will be referred as SVQS, PRQS, QFQS, and VCBR, respectively), on the long-period wave amplification patterns of the Sichuan basin during the great Wenchuan earthquake are investigated by using the spectral element method and parallel computing technique. The results show the following: (1) SVQS has the greatest influence on the basin horizontal component ground motions, while the vertical component is comparably insensitive to it. (2) PRQS demonstrates comparable effects on the horizontal and vertical basin ground motions. With the increase of PRQS, the amplitudes and regions of the strong shakings both become smaller. However, the influence of PRQS is less significant compared with SVQS and VCBR. (3) QFQS mainly affects the relatively high-frequency motions inside the basin, and the low-frequency motions are generally insensitive to it. With the reduction of QFQS, the amplitudes and regions of the basin strong ground motions turn a little smaller, but the distribution features are generally unchanged. (4) Effects of VCBR on the horizontal and vertical component basin ground motions are opposite. With the growth of VCBR, the amplitudes and areas of strong horizontal motions increase consequently, but they decrease for the vertical component.

Extraction of Lagrangian Coherent Structures in the framework of the Lagrangian–Eulerian Stabilized Collocation Method (LESCM)

In recent years, the investigations on Lagrangian Coherent Structures (LCS) in complex flows have attracted increasing attention due to their ability to accurately describe flow field details. In this study, we propose a novel and accurate numerical technique based on the Lagrangian–Eulerian Stabilized Collocation Method (LESCM) for computing the Finite Time Lyapunov Exponents (FTLEs), which is essential for extracting LCSs in viscous incompressible flows. LESCM, previously employed for simulating fluid–structure interaction problems, was developed based on the Material Point Method (MPM). The hybrid Lagrangian–Eulerian​ description in LESCM enables explicit tracking of fluid flow trajectories throughout the simulation and direct evaluation of the FTLE field. Furthermore. The errors in FTLEs caused by the Particle Shifting Technique (PST) are completely avoided due to the Eulerian characteristic of LESCM as the deformation gradient is calculated based on fixed Eulerian nodes rather than fluid particles with unphysical shifting. Consequently, the novel technique based on LESCM surpasses the accuracy of pure Lagrangian particle methods and provides an accurate way of detecting complex LCSs in flow fields. Additionally, By harnessing the remarkable efficiency of LESCM, MATLAB can now handle up to 16 million particles with ease, eliminating the need for parallel computation techniques. Several numerical examples, such as 2D Taylor-Green​ vortices, flow passing a circular cylinder and a 3-dimensional shear driven cavity problem, have been tested to demonstrate the effectiveness of this novel approach under various conditions.

Dynamics of a self-propelled capsule robot in contact with different folds in the small intestine

Considering the anatomy of small intestine involving lesions, circular folds and tumours are the major sources resisting the locomotion of capsule robots. By mimicking the small-bowel tumours as cone folds, this paper presents a comparative study on the dynamics of a vibro-impact capsule robot in contact with different circular and cone folds. With the aid of GPU parallel computing and path-following techniques, extensive bifurcation and basin stability analyses are performed to identify different capsule-fold interactions and unravel the parametric influences on the robot, such as fold shape, Young’s modulus and robot’s control parameters (e.g., excitation period and amplitude). It is found that fold shape and Young’s modulus may only affect capsule’s dynamics significantly when robot’s excitation period is large. Two essential locomotion modes, a period-one motion with capsule-fold contact in the small region of excitation amplitude and a fold crossing motion in the large region of excitation amplitude, dominating the dynamics of the robot regardless of fold shape and Young’s modulus are observed. In addition, the instability mechanism of this period-one motion is revealed in detail. The numerical study presented in this work will provide a solid basis for the locomotion control of the robot when encountering different types of circular folds and small-bowel tumours. It also offers the potential of utilising robot’s dynamics for bowel cancer detection.

POETS: An Event-driven Approach to Dissipative Particle Dynamics

HPC clusters have become ever more expensive, both in terms of capital cost and energy consumption; some estimates suggest that competitive installations at the end of the next decade will require their own power station. One way around this looming problem is to design bespoke computing engines, but while the performance benefits are good, the design costs are huge and cannot easily be amortized. Partially Ordered Event Triggered System (POETS)—the focus of this article—seeks to exploit a middle way: The architecture is tuned to a specific algorithmic pattern but, within that constraint, is fully programmable. POETS software is quasi-imperative: The user defines a set of sequential event handlers, defines the topology of a (typically large) concurrent ensemble of these, and lets them interact. The “solution” may be exfiltrated from the emergent behaviour of the ensemble. In this article, we describe (briefly) the architecture, and an example computational chemistry application, dissipative particle dynamics (DPD). The DPD algorithm is traditionally implemented using parallel computational techniques, but we re-cast it as a concurrent compute problem that is then ideally suited to POETS. Our prototype system is realised on a cluster of 48 FPGAs providing 50K concurrent hardware threads, and we report performance speedups of over two orders of magnitude better than a single thread baseline comparator and scaling behaviour that is almost constant. The results are validated against a “conventional” implementation.

Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework

Deep reinforcement learning (DRL) has become the mainstream method to design intelligent energy management strategies (EMSs) for fuel cell hybrid electric vehicles with the prosperity of artificial intelligence in recent years. Conventional DRL algorithms are suffering from low sampling efficiency and unsatisfactory utilization of computing resources. Combined with distributed architecture and parallel computation, DRL algorithms can be more efficient. Given that, this paper proposes a novel distributed DRL-based energy management framework for a fuel cell hybrid electric bus (FCHEB) to shorten the development cycle of DRL-based EMSs while reducing the total operation cost of the FCHEB. To begin, to make full use of the limited computing resources, a novel asynchronous advantage actor-critic (A3C)-based energy management framework is designed by innovatively integrating with the multi-process parallel computation technique. Then, a promising EMS considering the extra operation cost caused by fuel cell degradation and battery aging is designed based on this novel framework. Furthermore, EMSs based on a conventional DRL algorithm, advantage actor-critic (A2C), and another conventional distributed DRL framework, multi-thread A3C, are employed as baselines, and the performance of the proposed EMS is evaluated by training and testing using different driving cycles. Simulation results indicate that compared to EMSs based on A2C and multi-thread A3C, the proposed EMS can efficiently accelerate the convergence speed respectively by 87.46% and 88.92%, and reduce the total operation cost respectively by 44.83% and 41.19%. The main contribution of this article is to explore the integration of multi-process parallel computation in a distributed DRL-based EMS for a fuel cell vehicle for more efficient utilization of hydrogen energy in the transportation sector.

Performance Investigation of the Conjunction Filter Methods and Enhancement of Computation Speed on Conjunction Assessment Analysis with CUDA Techniques

The growing number of space objects leads to increases in the potential risks of damage to satellites and generates space debris after colliding. Conjunction assessment analysis is the one of keys to evaluating the collision risk of satellites and satellite operators require the analyzed results as fast as possible to decide and execute collision maneuver planning. However, the computation time to analyze the potential risk of all satellites is proportional to the number of space objects. The conjunction filters and parallel computing techniques can shorten the computation cost of conjunction analysis to provide the analyzed results. Therefore, this paper shows the investigation of the conjunction filter performances (accuracy and computation speed): Smart Sieve, CSieve and CAOS-D (combination of both Smart Sieve and CSieve) in both a single satellite (one vs. all) and all space objects (all vs. all) cases. Then, all the screening filters are developed to implement an algorithm that executes General-purpose computing on graphics processing units (GPGPU) by using NVIDIAs Compute Unified Device Architecture (CUDA). The analyzed results show the comparison results of the accuracy of conjunction screening analysis and computation times of each filter when implemented with the parallel computation techniques.

LISC Catalog of Open Clusters III. 83 Newly Found Galactic Disk Open Clusters Using Gaia EDR3

As groups of coeval stars born from the same molecular cloud, an open cluster (OC) is an ideal laboratory for studying the structure and dynamical evolution of the Milky Way. The release of high-precision Gaia Early Data Release 3 (Gaia EDR3) and modern machine-learning methods offer unprecedented opportunities to identify OCs. In this study, we extended conventional HDBSCAN (e-HDBSCAN) for searching for new OCs in Gaia EDR3. A pipeline was developed based on the parallel computing technique to blindly search for OCs from Gaia EDR3 within Galactic latitudes < 25°. As a result, we obtained 3787 star clusters, of which 83 new OCs were reported after cross-match and visual inspection. At the same time, the main star cluster parameters are estimated by color–magnitude diagram fitting. The study significantly increases the sample size and physical parameters of OCs in the catalog of OCs. It shows the incompleteness of the census of OCs across our Galaxy.

Adaptive density-based robust topology optimization under uncertain loads using parallel computing

This work presents an efficient parallel implementation of density-based robust topology optimization (RTO) using adaptive mesh refinement (AMR) schemes permitting us to address the problem with modest computational resources. We use sparse grid stochastic collocation methods (SCMs) for transforming the RTO problem into a weighted multiple-loading deterministic problem at the collocation points. The calculation of these deterministic problems and the functional sensitivity is computationally expensive. We combine distributed-memory parallel computing and AMR techniques to address the problem efficiently. The former allows us to exploit the computational resources available, whereas the latter permits us to increase performance significantly. We propose the parallel incremental calculation of the deterministic problems and the contribution to the functional sensitivity maintaining a similar memory allocation to the one used in the deterministic counterpart. The cumulative computing uses buffers to adapt the evaluation at the collocation points to the parallel computing resources permitting the exploitation of the embarrassing parallelism of SCMs. We evaluate the deterministic problems in a coarse mesh generated for each topology optimization iteration to increase the performance. We perform the regularization and design variable update in a fine mesh to obtain an equivalent design to the one generated in such a mesh. We evaluate the proposal in two- and three-dimensional problems to test its feasibility and scalability. We also check the performance improvement using computational buffers in parallel computing nodes. Finally, we compare the proposal to the same approach using different preconditioners without AMR schemes showing significant performance improvements.

Learning-based current estimation for power converters operating in continuous and discontinuous conduction modes

The problem of current estimation of switched power converters operating in continuous and discontinuous conduction modes is considered. A method is presented for the direct design of an estimator without exact knowledge of the mathematical model of the system. The structure of the proposed method is simpler than other approaches found in the literature, which use hybrid or averaged models to represent the dynamics of the power converter in each operating mode. An algorithm implementation using parallel computation and dimensionality reduction techniques for improving the execution performance is described. The method is demonstrated in the case of the pulse-width modulated SEPIC DC–DC converter, where simulation and experimental results are discussed. The proposed method shows better estimation results with respect to other well-known model-based and data-based approaches.

A Guided Evolutionary Strategy Based-Static Var Compensator Control Approach for Interarea Oscillation Damping

Static Var compensator (SVC) has often been used in the power system industries to regulate the bus voltage and reactive power. With an increasing concern of the power system transient stability, the SVC has also been used to dynamically regulate the power flow for oscillation dampings in recent years. A guided surrogate-gradient-based evolutionary strategy (GSES)-based SVC control approach is proposed in this article to damp power system interarea oscillations. The GSES algorithm is used to train a reinforcement learning agent to learn the best decisions that control the SVC, with an objective of quickly damping interarea oscillations. The GSES algorithm brings many benefits such as easier, more robust, and efficient training with less required hyperparameters. Because the GSES can engage with multiple individual workers during the training process, parallel computation techniques are implemented in this research to accelerate the computational speed. To test the performance of the proposed GSES-based SVC controller, we compared this novel method with a supplementary SVC damping controller on two different test systems. Results indicate that the proposed GSES-based SVC control approach can effectively damp power system interarea oscillations. Especially, in some case studies, the proposed approach successfully saved the test system from a transient-instability-caused breaking down.

Dynamic reliability analysis of stochastic structures under non-stationary random excitations based on an explicit time-domain method

For the analysis of the dynamic reliability of stochastic structures subjected to non-stationary random excitations, an effective hybrid approach is proposed. In this approach, the explicit time-domain method is employed to obtain the dynamic responses of each deterministic structure. Dynamic responses for the explicit time-domain method can be expressed as a series of products of deterministic coefficient matrices and random load vectors, which can significantly enhance computational efficiency. For the issue of stochastic structures, the double-hidden-layer backpropagation neural network is introduced as a surrogate model for reconstructing the coefficient matrices to avoid repeated construction of the coefficient matrices for each sample with different structural parameters using the extremely time-intensive regular method. For more effective training of the backpropagation neural network, the Latin hypercube sampling technique is introduced to generate representative samples of random structural parameters. In consideration of the intrinsic benefit of the explicit time-domain method, parallel computation is incorporated into the method. Finally, the explicit time-domain method is used to perform the Monte Carlo simulation in conjunction with the parallel computing technique to resolve the problem of small failure probabilities. To demonstrate the efficacy of the proposed method, numerical examples are presented.