Problem In Computing Research Articles

A comparison of Algebraic Multigrid Bidomain solvers on hybrid CPU–GPU architectures

The numerical simulation of cardiac electrophysiology is a highly challenging problem in scientific computing. The Bidomain system is the most complete mathematical model of cardiac bioelectrical activity. It consists of an elliptic and a parabolic partial differential equation (PDE), of reaction–diffusion type, describing the spread of electrical excitation in the cardiac tissue. The two PDEs are coupled with a stiff system of ordinary differential equations (ODEs), representing ionic currents through the cardiac membrane. Developing efficient and scalable preconditioners for the linear systems arising from the discretization of such computationally challenging model is crucial in order to reduce the computational costs required by the numerical simulations of cardiac electrophysiology. In this work, focusing on the Bidomain system as a model problem, we have benchmarked two popular implementations of the Algebraic Multigrid (AMG) preconditioner embedded in the PETSc library and we have studied the performance on the calibration of specific parameters. We have conducted our analysis on modern HPC architectures, performing scalability tests on multi-core and multi-GPUs settings. The results have shown that, for our problem, although scalability is verified on CPUs, GPUs are the optimal choice, since they yield the best performance in terms of solution time.

Time-dependent reliability analysis of structural systems based on parallel active learning Kriging model

Time-dependent reliability analysis quantifies the failures of structural systems due to time-dependent uncertainties, such as material degradation and dynamic loads. The active learning Kriging model methods are widely used in structural reliability analysis to replace extensive time-consuming finite element simulations. However, they can only update one training sample and one failure mode per iteration, which limits their application to time-dependent, parallel computing, and multiple failure modes problems. In this study, we propose a new parallel active learning Kriging model for time-dependent reliability analysis, which can update multiple training samples and multiple failure modes per iteration. It includes the following strategies: (1) a novel parallel learning function is proposed, which combines the correlation function and U learning function to allow for the selection of multiple training samples per iteration; (2) an adaptive adjustment strategy for the number of parallel samples is proposed, which takes into account the prediction probability of parallel samples; (3) the proposed parallel learning function is integrated into time-dependent reliability analysis with multiple failure modes, enabling simultaneous updates of multiple training samples and failure modes, thus greatly reducing the number of iterations and computational time; and (4) a new stopping criterion is proposed to improve the efficiency of the estimation of failure probability. The proposed method can be applied to series or parallel time-dependent structural systems with multiple failure modes. We demonstrate the effectiveness of the proposed method through three examples, and the proposed method can achieve a balance between the computational time and function calls while maintaining a high level of accuracy in the estimation of time-dependent failure probability.

A modified Newton-based matrix splitting iteration method for generalized absolute value equations

Many problems in scientific computing and engineering fields lead to the solution of generalized absolute value equations (GAVE). In this paper, by simultaneously splitting both coefficient matrices in the differential and non-differential parts of the GAVE, a modified Newton-based matrix splitting (MNMS) iteration method is proposed. The MNMS iteration method not only covers the well-known generalized Newton iteration method as well as the recent proposed modified Newton-based iteration method and the Newton-based matrix splitting iteration method for solving the GAVE, but also results in a series of relaxation versions that are very flexible in real applications. Convergence properties of the MNMS iteration method are studied in detail when the coefficient matrices are positive definite matrices and H+-matrices. Finally, three numerical examples are presented to show the feasibility and effectiveness of the proposed MNMS iteration method.

Geoinformation technologies of computer-aided design of public places in urban areas: problems and experience (on the example of Saransk)

The paper considers the possibilities of using modern software products to solve the problems of computer (including geoinformation) design of public places in urban areas, first of all, the AutoCAD computer-aided design system. The identified features of program use, its advantages and disadvantages, strong features and problems are presented, proven ways to overcome the latter are indicated. Based on the analysis of the experience of several projects of designing public places in Saransk (the capital of the Republic of Mordovia), carried out by the authors on the basis of topographic surveys of different years accompished by employees of LLC "Geostroiziskaniya", LLC "Cadastral Center", LLC "RNITS", materials are presented that give an idea of the options for incorrect and correct topography and illustrate the most common disadvantages of topographic bases, main steps of their processing and creation of a two-dimensional and three-dimensional topographic basis are shown on the example of the object "Novotroitsky shopping and office center". A number of indirect problems is also identified, generally related to computer-aided design of public places in urban areas, including problems with the use of licensed software, and really proven ways to overcome them are shown. This allowed to develop and practically implement geoinformation technology for computer-aided design of public places in urban areas.

Optimal Computation Resource Allocation in Energy-Efficient Edge IoT Systems With Deep Reinforcement Learning

This paper investigates a computation resource optimization problem of mobile edge computing (MEC)-aided Internet-of-Things (IoT) devices with a reinforcement learning (RL) solution. Specifically, we leverage the stochastic optimization method and formulate the Lyapunov optimization technique to maximize the long-term energy efficiency, taking into account the transmission power, network stability, and transmission latency. Based on the Markov decision process and model-free deep RL (DRL) approach, we propose a double DRL-based online computation offloading method to implement a deep neural network that learns from interactions to solve the computation offloading and transmission latency problem in the dynamic MEC-aided IoT environments. Furthermore, we design an adaptive method for continuous action-state spaces to minimize the completion time and total energy consumption of the IoT devices for stochastic computation offloading tasks. The proposed real-time Lyapunov optimization and DRL algorithms achieve low computational complexity and optimal processing time. Simulation results demonstrate that the proposed algorithm can achieve near-optimal control performance with enhanced energy efficiency performance compared to the baseline policy control algorithms.

Deep Learning-Based Sentiment and Stance Analysis of Tweets About Vaccination

Sentiment analysis and stance detection are interrelated problems of affective computing, and their outputs commonly complement each other. The focus of this article is to determine sentiments and stances of Twitter users about vaccination. A tweet dataset on COVID-19 vaccination is compiled and jointly annotated with sentiment and stance. This deep learning approach employs BERT, which is a model based on pre-trained transformers. The generative deep learning model, ChatGPT, is also used for stance and sentiment analysis on the dataset. ChatGPT achieves the best performance for stance detection, while BERT is the best performer for sentiment analysis. This study is the first one to observe stance and sentiment detection performance of ChatGPT on health-related tweets. This article also includes a full-fledged system proposal based on automatic sentiment and stance analysis. COVID-19 pandemic is an impactful global public health phenomenon, and hence, joint extraction of sentiments and stances from health-related tweets can profoundly contribute to health-related decision-making processes.

Online User and Power Allocation in Dynamic NOMA-Based Mobile Edge Computing

This study tackles the online user allocation problem in mobile edge computing (MEC) systems powered by non-orthogonal multiple access. App vendors need to determine a proper wireless channel in a base station/edge server and sufficient transmit power for every user. We consider a stochastic MEC system where users arrive and depart over time. When an edge server runs out of computing resources, some users will have to wait until the resources become available again, which incurs an allocation delay cost. This cost is often not investigated in many studies, which also do not consider a multi-cell, multi-channel system as we do in this work, due to its complexity. We aim to minimize the allocation delay and transmit power costs, increasing the system’s energy efficiency. To achieve this objective while guaranteeing users’ data rate requirements over time, we adopt the Lyapunov framework to convert this long-term optimization problem into a series of subproblems to be solved in every time slot. To solve the aforementioned subproblems efficiently, we present a distributed game theory-based approach. The proposed algorithm is theoretically evaluated and experimentally demonstrated to outperform several baseline and state-of-the-art methods, highlighting the significance of systematic consideration for both computation and communication aspects of this problem.

Adaptive Gaussian process regression for efficient building of surrogate models in inverse problems

In a task where many similar inverse problems must be solved, evaluating costly simulations is impractical. Therefore, replacing the model y with a surrogate model ys that can be evaluated quickly leads to a significant speedup. The approximation quality of the surrogate model depends strongly on the number, position, and accuracy of the sample points. With an additional finite computational budget, this leads to a problem of (computer) experimental design. In contrast to the selection of sample points, the trade-off between accuracy and effort has hardly been studied systematically. We therefore propose an adaptive algorithm to find an optimal design in terms of position and accuracy. Pursuing a sequential design by incrementally appending the computational budget leads to a convex and constrained optimization problem. As a surrogate, we construct a Gaussian process regression model. We measure the global approximation error in terms of its impact on the accuracy of the identified parameter and aim for a uniform absolute tolerance, assuming that ys is computed by finite element calculations. A priori error estimates and a coarse estimate of computational effort relate the expected improvement of the surrogate model error to computational effort, resulting in the most efficient combination of sample point and evaluation tolerance. We also allow for improving the accuracy of already existing sample points by continuing previously truncated finite element solution procedures.

Soft Actor–Critic-Based Computation Offloading in Multiuser MEC-Enabled IoT—A Lifetime Maximization Perspective

This paper studies the network lifetime optimization problem in a multi-user mobile edge computing (MEC)-enabled Internet of things (IoT) system comprising an access point (AP), a MEC server, and a set of K mobile devices (MDs) with limited battery capacity. Considering the residual battery energy at the MDs, stochastic task arrivals, and time-varying wireless fading channels, a soft actor-critic (SAC)-based deep reinforcement learning (DRL) lifetime maximization, called DeepLM, is proposed to jointly optimize the task splitting ratio, the local CPU-cycle frequencies at the MDs, the bandwidth allocation, and the CPU-cycle frequency allocation at the MEC server subject to the task queuing backlogs constraint, the bandwidth constraint, and maximum CPU-cycle frequency constraints at the MDs and the MEC server. Our results reveal that DeepLM enjoys a fast convergence rate and a small oscillation amplitude. We also compare the performance of DeepLM with three benchmark offloading schemes, namely, fully edge computing (FEC), fully local computing (FLC), and random computation offloading (RCO). DeepLM increases the network lifetime by 496% and 229% compared to the FLC and RCO schemes. Interestingly, it achieves such a colossal lifetime improvement when its non-backlog probability is 0.99, while that of FEC, FLC, and RCO is 0.69, 0.53, and 0.25, respectively, showing a significant performance gain of 30%, 46%, and 74%.

Topological classification of the intersection curves of two quadrics using a set of discriminants

Computing the intersection curves of two quadrics (QSIC) is a fundamental problem in computer aided design system, where the topology analysis of the QSIC is an essential task to realize the robust computation. In this paper, a new method is proposed to classify the topology of the QSIC in three dimensional projective space by using a set of discriminants associated with the two quadrics. The new topology classification method presents explicit representations and can be applied even when the coefficients of the two quadrics contain symbols. Some examples are provided to illustrate the usage of the new classification method.

A hybrid stochastic interpolation and compression method for kernel matrices

Kernel functions play a pivotal role in a wide range of scientific computing and machine learning problems, but they ofter result in dense kernel matrices that impose great challenges in computational costs at large scale. To address this issue, we develop a set of fast kernel matrix compressing algorithms, which can reduce computation cost of matrix operations in the related applications. The foundation of these algorithms is the polyharmonic spline interpolation, which encompass a set of radial basis functions that allow flexible choices of interpolating nodes, and a set of polynomial basis functions that guarantee the solvability and convergence of the interpolation. With these properties, original data points in the interacting kernel function can be randomly sampled with great flexibility, so the proposed method is suitable for complicated data structures, such as high-dimensionality, random distribution, or manifold. To further boost the algorithm accuracy and efficiency, our scheme incorporates a QR sampling strategy, and combined with a recently developed fast stochastic SVD to form a hybrid method. If the overall number of degree of freedom is N, then the compressing algorithm has complexity of O(N) for low-rank matrices, and O(Nlog⁡N) for general matrices with a hierarchical structure. Numerical results for data on various domains and different kernel functions validate the accuracy and efficiency of the proposed method.

SUCOLA: Self-adaptive structure refinement unsupervised contrastive learning framework for food safety risk early warning

Food safety is a global issue; risk prevention and control of food physical and chemical testing (PCT) data are critical. However, existing studies have not considered the risk status of qualified products near the detection limit. Therefore, this paper presents a data-driven paradigm to detect the combined risk of multiple indicators across products in real-world food safety datasets for the first time. The following challenges exist to achieve this goal. (1) In real-world scenarios, neighboring samples are somewhat correlated; however, capturing and quantifying the actual testing sample correlation network remains an open problem in food computing. (2) The low-frequency nature of the unqualified samples leads to label imbalance in the data. (3) Labeling dependency limits the application scenarios. This article proposes a novel self-supervised framework (SUCOLA) to address the abovementioned challenges. That jointly learns the optimal graph topology and also inferences the connections between every product node and its well-designed subgraph, helping to resist label imbalance. We applied it to a real-world sterilized PCT dataset from a province in China. Extensive experiments show that SUCOLA performance is outstandingly better than several baselines of the current graph self-supervision field. Meanwhile, we conducted generalizability experiments on five well-known benchmark datasets. The proposed SUCOLA surpasses the current state-of-the-art by a considerable margin (0.39%–8.7% AUC) on public datasets. Thus, our study provides a new idea for early warning research on PCT data in food computing and fills the gap in the study of structural learning under category imbalance.

Federated Learning Assisted Deep Q-Learning for Joint Task Offloading and Fronthaul Segment Routing in Open RAN

Offloading computation-intensive tasks to edge clouds has become an efficient way to support resource constraint edge devices. However, task offloading delay is an issue largely due to the networks with limited capacities between edge clouds and edge devices. In this paper, we consider task offloading in Open Radio Access Network (O-RAN), which is a new 5G RAN architecture allowing Open Central Unit (O-CU) to be co-located with Open Distributed Unit (DU) at the edge cloud for low-latency services. O-RAN relies on fronthaul network to connect O-RAN Radio Units (O-RUs) and edge clouds that host O-DUs. Consequently, tasks are offloaded onto the edge clouds via wireless and fronthaul networks, which requires routing. Since edge clouds do not have the same available computation resources and tasks’ computation deadlines are different, we need a task distribution approach to multiple edge clouds. Prior work has never addressed this joint problem of task offloading, fronthaul routing, and edge computing. To this end, using segment routing, O-RAN intelligent controllers, and multiple edge clouds, we formulate an optimization problem to minimize offloading, fronthaul routing, and computation delays in O-RAN. To determine the solution of this NP-hard problem, we use Deep Q-Learning assisted by federated learning with a reward function that reduces the Cost of Delay (CoD). The simulation results show that our solution maximizes the reward in minimizing CoD.

Physics-Inspired Optimization in the QUBO Framework: Key Concepts and Approaches

Quantum computing promises to have a tremendous advantage over its classical counterpart for solving computationally hard problems, yet remains in a relatively early stage for practical applications owing to the limited capabilities of today’s quantum computers. The approach to the special purposes of quantum computers by exploiting the special-purpose physics-inspired or quantum-inspired computers is emerging as a novel alternative to its quantum counterpart in tackling hard problems in high-performance computing. Inspired by physics, the Ising machine — a type of special-purpose computer that implements or emulates physics or quantum effects of the Ising model to speed up finding solutions to optimization problems — has recently become an active research area in the field of combinatorial optimization. This paper is to address the key enabling software and hardware technology underlying physics-inspired optimization using Ising machines in the unified quadratic unconstrained binary optimization (QUBO) framework for modeling and solving computationally hard combinatorial optimization problems, and with an aim to shed some light on the challenges and opportunities associated with the ever-growing landscape of this novel high-performance computing.

An Overview of Algorithms for Solving Vehicle Routing Problems in the Quantum-Classical Cloud

Introduction. The hope of solving the problem of the avalanche-like growth of requirements for computing power, essential for solving complex routing problems and other problems of combinatorial optimization, relies on the latest quantum computers, in the development of which governments and corporations invest multi-billion investments. The article examines modern routing algorithms and performs their analysis and verification, if the authors of the algorithm provided appropriate test programs. The purpose of the article is to review the current state of development in the field of development of routing algorithms for hybrid quantum-classical clouds, analyze them and propose a classification of algorithms. Results. Modern quantum computers (QCs) make it possible to find approximate solutions to some of mathematical problems faster than classical computers. The inaccuracy of the solutions obtained by the QC is a consequence of physical and technological limitations: calculation errors are caused by thermal noise, a small number of computational elements - qubits and connections between them, which requires the decomposition of the problem and the use of heuristic algorithms. The analysis of approaches to the solution of optimization problems on QC allows us to single out: quantum response and variational search of eigenvalues based on quantum logic gates as the general directions of development of the vast majority of algorithms for solving routing problems. The considered algorithms reduce the vehicle routing problem to a quadratic unconstrained binary optimization problem, which is isomorphic to the Hamilton-Ising model. In this form, the problem is suitable for embedding in QC, which finds an approximate solution that has the best statistical reliability or corresponds to the quantum state with the lowest energy. As a separate class, vehicle routing algorithms for classical computers that use quantum computing to accelerate problem solving can be distinguished. For example, neural networks that calculate weighting factors using QC or an ant algorithm that calculates a pheromone trail in a hybrid cloud. It should be mentioned the quantum-inspired algorithms, which are based on software tools for the simulation of QC and the corresponding libraries and allow creating an effective class of algorithms for solving problems of vehicle routing. Conclusions. Combining hardware quantum annealing with a number of software tools for calculating optimization problems for classical computers in a hybrid quantum-classical cloud service allows to obtain advantages in speed and accuracy of some types of complex optimization problems of a commercial scale, in particular, routing vehicles, which is already bringing substantial profits to a number of corporations. Keywords: vehicle routing problem, quantum computer, annealing, combinatorial optimization, traveling salesman problem, clustering, qubit.

Development of Monte Carlo hybrid parallel algorithm and application in high resolution spatial-energy flux distribution calculation

Monte Carlo transport calculations for fusion reactors are very challenging due to factors such as large radiation decay gradients, complex geometric structures, and strong neutron anisotropy. For such deep penetration shielding calculation problems, in order to obtain accurate global flux distribution, variance reduction methods and parallel computing are necessary means. When using the global weight window variance reduction technique, the huge deviation of the particle arrival weight and the weight window can lead to an extreme number of splits, which is the long history problem. If a process encounters the long history problem in parallel computing and takes a lot of time to complete, other processes will stop computing because the histories allocated in the computing cycle have been completed, seriously degrading computing efficiency. In addition, the calculation of the shutdown dose rate, a key issue in fusion neutronics calculations, requires a high “space-energy” resolution, which brings huge challenges to computational memory, especially in parallel computing. In order to overcome the degradation of parallel efficiency caused by the long history problem and the memory limitation in large-scale shutdown dose rate calculation, a hybrid parallel algorithm based on shared memory was researched. Finally, the hybrid parallel architecture was built in the Monte Carlo code cosRMC to realize dynamic load balancing between threads. Through the memory sharing among all threads of a single node, the pressure of memory consumption can be effectively relieved. In order to verify the effectiveness of the hybrid parallel algorithm developed in this paper, it was applied to the calculation of CFETR. The results showed that the hybrid parallel algorithm can effectively alleviate the impact of the long history problem on the parallel efficiency. In terms of saving memory, the hybrid parallel algorithm also showed a significant effect, which provides a guarantee for large-scale shutdown dose rate calculation.

A comprehensive review on Internet of Things application placement in Fog computing environment

With the rise in Internet of Things (IoT) technology in recent years the amount of resource requirement of various IoT applications substantially increases. The data generated by various geo-distributed IoT devices is growing continuously. The existing IoT–Cloud paradigm limits the number of various emergent IoT applications as per the requirement. To utilize various IoT applications effectively and efficiently resource decentralization mechanism is a time of need. Fog computing is a novel computing approach for various IoT applications, specifically time-sensitive. Though, the resource-constrained nature of Fog devices in the fog computing model certainly fails to cater to multiple services for these IoT applications. The heterogeneity and dynamicity of the application request from the IoT devices need a quick decision regarding the placement of the application in the fog layer. Hence, optimal allocation of resources is quite essential for providing uninterrupted services to the end-users. This article comprehensively analyzes the different types of possible IoT application models and strategies for allocating resources to these applications. Since the resource allocation problem has already been proven to be a computationally NP-hard problem, finding a non-deterministic algorithm for allocating resources is our target. In this article, we have taken the IoT application placement problem in fog computing (APFC) as a single and multiple objective optimization problems. We have conducted a survey based on single and multiple objectives to address other possible issues in APFC. Finally, the challenges and promising directions for further research are presented.

Reliability-Aware and Energy-Efficient Workflow Scheduling in IaaS Clouds

Nowadays, more and more workflow applications with different computing requirements are migrated to clouds and executed with cloud resources. Workflow scheduling becomes a critical problem in the cloud environment, which focuses on meeting various quality of service (QoS) constraints. Workflow reliability and energy consumption are two essential parts in clouds and minimizing energy consumption for scheduling workflow with the reliability constraint is a challenging issue. In response to the challenge, we propose a workflow scheduling algorithm named REWS to reduce energy consumption and satisfy workflow reliability constraints. In REWS, a new sub-reliability constraint prediction strategy is adopted to break down the workflow reliability constraint to task sub-reliability constraints and the effectiveness of this strategy is proved. Moreover, an update method is adopted to adjust the task sub-reliability constraint for reducing energy consumption. In addition, a brief system framework which consists of five parts: workflow analyzer, reliability decomposer, resource manager, workflow scheduler and feedback processer is built to support the algorithm implementation of REWS. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed REWS approach. The results demonstrate the superiority of REWS as compared with the state-of-the-art algorithms. <i>Note to Practitioners</i>&#x2014;Workflow scheduling is a challenging issue in emerging trends of the cloud environment that focuses on satisfying various QoS constraints. In this paper, we investigate a reliability-aware and energy-efficient workflow scheduling problem in cloud computing. A novel workflow scheduling algorithm called REWS, is designed to reduce the energy consumption and meet the workfolw reliability constraint. The basic idea of REWS is to divide the workflow reliability constraint into task sub-reliability constraints and schedule tasks with an energy-efficient scheduling strategy. We conduct the experiments to evaluate the proposed REWS and the results demonstrate that REWS outperforms the state-of-the-art algorithms.

Globally certified [formula omitted] approximation of planar algebraic curves

Topology analysis and geometric feature determination for curves and surfaces are basic problems in geometric modeling and computer aided design. When the curves and surfaces are given in implicit expressions, the problems become more difficult. This paper presents a complete algorithm framework for the problem of certified G1 approximation of a planar algebraic curve under global error control. Compared to other work on topology and geometric analysis of algebraic curves, this method presents a new topology analysis as well as a geometric information computation. Moreover, this method uses generalized asymptotes as the approximation of the unbounded part, which makes it achieve global approximation with given precision.

Efficient federated learning on resource-constrained edge devices based on model pruning

Federated learning is an effective solution for edge training, but the limited bandwidth and insufficient computing resources of edge devices restrict its deployment. Different from existing methods that only consider communication efficiency such as quantization and sparsification, this paper proposes an efficient federated training framework based on model pruning to simultaneously address the problem of insufficient computing and communication resources. First, the framework dynamically selects neurons or convolution kernels before the global model release, pruning a current optimal subnet and then issues the compressed model to each client for training. Then, we develop a new parameter aggregation update scheme, which provides training opportunities for global model parameters and maintains the complete model structure through model reconstruction and parameter reuse, reducing the error caused by pruning. Finally, extensive experiments show that our proposed framework achieves superior performance on both IID and non-IID datasets, which reduces upstream and downstream communication while maintaining the accuracy of the global model and reducing client computing costs. For example, with accuracy exceeding the baseline, computation is reduced by 72.27% and memory usage is reduced by 72.17% for MNIST/FC; and computation is reduced by 63.39% and memory usage is reduced by 59.78% for CIFAR10/VGG16.