Large-scale Computational Experiments Research Articles

Understanding the Necessity and Economic Benefits of Lockdown Measures to Contain COVID-19

Since the outbreak of the coronavirus disease 2019 (COVID-19), the issue of how to maintain economic development while containing the epidemic has become a significant concern for decision-makers. Though lockdown measures are verified to be very effective in containing the epidemic, its economic costs and other influences have not been fully explored. As a result, decision-makers in many countries are still hesitant to include the lockdown measure in an intervention strategy in response to COVID-19. To address this issue, we propose a universal computational experiment approach for policy evaluation and adjustment based on the Artificial societies, Computational experiments, Parallel execution (ACP) concept. First, we innovatively construct a model via observable CO<inline-formula> <tex-math notation="LaTeX">$_2$</tex-math> </inline-formula> emissions, which is able to estimate the economic costs affected by nonpharmaceutical interventions. Furthermore, based on the population movement data, a risk source model is proposed to estimate the local transmission risk for any prefectures outside the epicenter. Finally, we integrate the data models in a high-resolution agent-based artificial society and carry out large-scale computational experiments supported by the Tianhe supercomputer. Policy adjustments and evaluations are carried out in four cities: Wenzhou, Guangzhou, Beijing, and Wuhan. Our research findings show important implications for policy-making: 1) the local transmission of a city can be almost contained if lockdowns are adopted immediately when the risk index is larger than 1.645, 1.960, or 2.576 at the 90%, 95%, or 99% confidence interval, respectively; 2) if lockdowns are required, in-advance lockdown measures facilitate mitigation efficacy and reduce economic loss; and 3) lockdowns lasting for 7&#x2013;14 days in a prefecture would be effective in controlling the spread of the epidemic. The duration of the measure should be prolonged with the increment of the initial transmission risk.

Computer simulation of the stress-strain state of thin plates and cylindrical shells with a circular hole reinforced by an inclusion from functionally graded material

Computer simulation and FEM analysis of the stress-strain state of thin plates and thin-walled cylindrical shells, weakened by a circular hole in the presence of an annular inclusion of a functionally graded material (FGM) surrounding it, have been carried out. The influence of the dimensions of the FGM-inclusion and the law of change of its elastic modulus on the concentration of the parameters of the stress-strain state of plates and shells in the vicinity of the hole is studied. The distribution of stress and strain intensities in the zones of local stress concentration is obtained. It has been established that when using a radially inhomogeneous FGM-inclusion with certain mechanical properties, it is possible to reduce the stress concentration factor by more than 35%. The law of change in the modulus of elasticity of the FGM-inclusion and the width of the inclusion have a significant effect not only on the concentration of the parameters of the stress-strain state of the plate and shell, but also on the nature of the stress distribution over their surfaces. The results of a series of large-scale computational experiments show that the use of an FGM annular inclusion makes it possible to reduce the intensity of both stresses and deformations around the hole.

Simulation of COVID-19 Outbreak in Nanjing Lukou Airport Based on Complex Dynamical Networks

The Corona Virus Disease 2019 (COVID-19) pandemic is still imposing a devastating impact on public health, the economy, and society. Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years. However, the spread simulation of COVID-19 in the dynamic social system is relatively unexplored. To address this issue, considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021, we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies, Computational experiments, and Parallel execution (ACP) approach. Specifically, the artificial society includes an environmental model, population model, contact networks model, disease spread model, and intervention strategy model. To reveal the dynamic variation of individuals in the airport, we first modeled the movement of passengers and designed an algorithm to generate the moving traces. Then, the mobile contact networks were constructed and aggregated with the static networks of staff and passengers. Finally, the complex dynamical network of contacts between individuals was generated. Based on the artificial society, we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies. Learned from the reproduction of the outbreak, it is found that the increase in cumulative incidence exhibits a linear growth mode, different from that (an exponential growth mode) in a static network. In terms of mitigation measures, promoting unmanned security checks and boarding in an airport is recommended, as to reduce contact behaviors between individuals and staff.

Modified benders decomposition and metaheuristics for multi-machine parallel-batch scheduling and resource allocation under deterioration effect

Batch scheduling problem is a common difficulty in mass production, which includes many decisions such as grouping, batch ordering, and resource allocation. Motivated by the semiconductor burn-in test process, this paper investigates a combinatorial optimization problem considering the deterioration effect and resource allocation. The objective is to design an appropriate job sequencing and resource allocation scheme to achieve an overall minimization of makespan and resource costs. The problem, subject to parallel-batch, multi-machine, deterioration effect, and resource constraints, is NP-hard. We decompose it into a master problem and a sub-problem based on Benders Decomposition (BD). A cut filtering rule that limits the number of cuts is proposed to control the complexity of the problem. Meanwhile, some structural properties are deduced to guide the design of two algorithms combined with Variable Neighborhood Search (VNS). We conduct extensive simulation experiments on instances of different scales, and the results demonstrate that the cut filtering method can effectively reduce the solution time. In large-scale computational experiments, it is more obvious that the proposed algorithms have good convergence and robustness.

Вплив включення із функціонально-градієнтного матеріалу на концентра-цію напружень в тонких пластинах та циліндричних оболонках з круговим отвором

ology, in particular, space-rocket engineering, oil-and-gas engineering, power engineering, construction, etc. The presence of openings in their plate and shell components leads to a sharp increase in local stresses, which, under certain conditions, may trigger destructive processes. The use of functionally graded materials (FGMs) with certain mechanical properties can significantly reduce the stress concentration in the vicinity of local concentrators in the form of openings, cutouts, fillets, grooves, etc. This paper presents the results of computer simulation and finite element analysis of the stress and strain fields of thin plates and thin-walled cylindrical shells with a circular opening and an annular FGM inclusion surrounding it. The effect of the dimensions of the FGM inclusion and the law of variation of its elastic modulus on the stress and strain concentration in the vicinity of the opening was studied. The stress and strain intensity distribution in local stress concentration zones was obtained. It was found that an annular FGM inclusion with certain mechanical properties can reduce the stress concentration factor by more than 30%. In this case, a proportional decrease in strain intensity in the vicinity of the opening is also observed. The law of variation of the elastic modulus of the FGM inclusion and the inclusion width have a significant effect not only on the level of stress and strain concentration, but also on the stress and strain pattern. The results of the large-scale computational experiments show that an FGM annular inclusion reduces both the stress and the strain intensity around the opening. Therefore, the use of annular FGM reinforcements in plates and cylindrical shells with openings makes it possible to control the distribution and magnitude of the stress and strain intensities in local stress and strain concentration zones.

Визуальное представление многомерных задач линейного программирования

The article proposes an n-dimensional mathematical model of the visual representation of a linear programming problem. This model makes it possible to use artificial neural networks to solve multidimensional linear optimization problems, the feasible region of which is a bounded non-empty set. To visualize the linear programming problem, an objective hyperplane is introduced, the orientation of which is determined by the gradient of the linear objective function: the gradient is the normal to the objective hyperplane. In case of searching a maximum, the objective hyperplane is positioned in such a way that the value of the objective function at all its points exceeds the value of the objective function at all points of the feasible region, which is a bounded convex polytope. For an arbitrary point of the objective hyperplane, the objective projection onto the polytope is determined: the closer the objective projection point is to the objective hyperplane, the greater the value of the objective function at this point. Based on the objective hyperplane, a finite regular set of points is constructed, called the retina. Using objective projections, an image of a polytope is constructed. This image includes the points of the retina and the distances to the corresponding points of the polytope surface. Based on the proposed model, parallel algorithms for visualizing a linear programming problem are constructed. An analytical estimation of its scalability is performed. Information about the software implementation and the results of largescale computational experiments confirming the efficiency of the proposed approaches are presented.

Evaluating the mitigation strategies of COVID-19 by the application of the CO2 emission data through high-resolution agent-based computational experiments.

The negative consequences, such as healthy and environmental issues, brought by rapid urbanization and interactive human activities result in increasing social uncertainties, unreliable predictions, and poor management decisions. For instance, the Coronavirus Disease (COVID-19) occurred in 2019 has been plaguing many countries. Aiming at controlling the spread of COVID-19, countries around the world have adopted various mitigation and suppression strategies. However, how to comprehensively eva luate different mitigation strategies remains unexplored. To this end, based on the Artificial societies, Computational experiments, Parallel execution (ACP) approach, we proposed a system model, which clarifies the process to collect the necessary data and conduct large-scale computational experiments to evaluate the effectiveness of different mitigation strategies. Specifically, we established an artificial society of Wuhan city through geo-environment modeling, population modeling, contact behavior modeling, disease spread modeling and mitigation strategy modeling. Moreover, we established an evaluation model in terms of the control effects and economic costs of the mitigation strategy. With respect to the control effects, it is directly reflected by indicators such as the cumulative number of diseases and deaths, while the relationship between mitigation strategies and economic costs is built based on the emission. Finally, large-scale simulation experiments are conducted to evaluate the mitigation strategies of six countries. The results reveal that the more strict mitigation strategies achieve better control effects and less economic costs.

VaLiPro: Linear Programming Validator for Cluster Computing Systems

The article presents and evaluates a scalable algorithm for validating solutions of linear programming problems on cluster computing systems. The main idea of the method is to generate a regular set of points (validation set) on a small-radius hypersphere centered at the point of the solution under validation. The objective function is calculated for each point of the validation set that belongs to the feasible region. If all these values are less than or equal to the value of the objective function at the point under validation, then this point is the correct solution. The parallel implementation of the VaLiPro algorithm is performed in C++ through the parallel BSF-skeleton, which encapsulates all aspects related to the MPI-based parallelization of the program. We provide the results of large-scale computational experiments on a cluster computing system to study the scalability of the VaLiPro algorithm.

О генерации случайных задач линейного программирования на кластерных вычислительных системах

The article presents and evaluates a scalable FRaGenLP algorithm for generating random linear programming problems of large dimension n on cluster computing systems. To ensure the consistency of the problem and the boundedness of the feasible region, the constraint system includes 2 n +1 standard inequalities, called support inequalities. New random inequalities are generated and added to the system in a manner that ensures the consistency of the constraints. Furthermore, the algorithm uses two likeness metrics to prevent the addition of a new random inequality that is similar to one already present in the constraint system. The algorithm also rejects random inequalities that cannot affect the solution of the linear programming problem bounded by the support inequalities. The parallel implementation of the FRaGenLP algorithm is performed in C++ through the parallel BSF-skeleton, which encapsulates all aspects related to the MPI-based parallelization of the program. We provide the results of large-scale computational experiments on a cluster computing system to study the scalability of the FRaGenLP algorithm.

BSF: A parallel computation model for scalability estimation of iterative numerical algorithms on cluster computing systems

This paper examines a novel parallel computation model called bulk synchronous farm (BSF) that focuses on estimating the scalability of compute-intensive iterative algorithms aimed at cluster computing systems. The main advantage of the proposed model is that it allows to estimate the scalability of a parallel algorithm before its implementation. Another important feature of the BSF model is the representation of problem data in the form of lists that greatly simplifies the logic of building applications. In the BSF model, a computer is a set of processor nodes connected by a network and organized according to the master/slave paradigm. A cost metric of the BSF model is presented. This cost metric requires the algorithm to be represented in the form of operations on lists. This allows us to derive an equation that predicts the scalability boundary of a parallel program: the maximum number of processor nodes after which the speedup begins to decrease. The paper includes examples of applying the BSF model to designing and analyzing parallel numerical algorithms. The large-scale computational experiments conducted on a cluster computing system confirm the adequacy of the analytical estimations obtained using the BSF model.

Approach to Constructing a Hydraulic Fracture Trajectory in a Rock Mass Near a Mine Working

In this paper, a model is developed for the geomechanical state of coal-rock mass containing the following: the seam and the hydraulic fracture for effectively applying a method of directional hydraulic fracturing of solid rocks. The model is based on the provisions of solid mechanics and Griffiths–Irwin linear fracture mechanics and is implemented using the method of boundary integral equations. Using the model, a large-scale computational experiment was carried out for specific mining and geological conditions of a coal deposit. Based on the analysis of the obtained results, a number of features in the propagation of the crack were identified.

Exploring the structural factors of resilience in urban drainage systems: a large-scale stochastic computational experiment

The focus of infrastructure design and management has turned from a reliability-based approach to a resilience-based one. Resilience is a system's ability to maintain its function and minimize failure consequences when faced with exceptional conditions. This study carries out a large-scale computational experiment to study how resilience is affected by system's structure in a combined sewer system. We build a stochastic generation model, involving a random sampling of facility locations and a graph-based random walk sampling algorithm to generate various layouts of pipelines. The performance of these virtual systems are assessed in the Storm Water Management Model. We apply statistical techniques on these samples to study the relation between resilience and system structure. Results show that the number of combined sewer overflow (CSO) outfalls is a more important factor of resilience compared to the number of wastewater treatment plants (WWTPs). Some locations are found more preferable for WWTP or CSO outfall placement, while adding WWTPs or outfalls at other locations might even lower the system's resilience. Size of the sub-catchments of the CSO outfalls also affects resilience. Although this effect is statistically significant, the extent is not remarkable compared to other factors. We further study the structural features of the cost-effective systems. The highest achievable resilience level increases as the number of CSO outfalls decreases and so does system's cost. This results from the difference in CSO quantity, therefore this dilemma can be cut off by end-of-pipe storage or treatment which specifically tackles CSO. The conclusion of this study provides an insight into the structural factors of combined sewer systems’ resilience and can provide guidance for system's planning.

Multimode time-cost-robustness trade-off project scheduling problem under uncertainty

The time/cost trade-off problem is a well-known project scheduling problem that has been extensively studied. In recent years, many researchers have begun to focus on project scheduling problems under uncertainty to cope with uncertain factors, such as resource idleness, high inventory, and missing deadlines. To reduce the disturbance from uncertain factors, the aim of robust scheduling is to generate schedules with time buffers or resource buffers, which are capped by project makespan and project cost. This paper addresses a time-cost-robustness trade-off project scheduling problem with multiple activity execution modes under uncertainty. A multiobjective optimization model with three objectives (makespan minimization, cost minimization, and robustness maximization) is constructed and three propositions are proposed. An epsilon-constraint method-based genetic algorithm along with three improvement measures is designed to solve this NP-hard problem and to develop Pareto schedule sets, and a large-scale computational experiment on a randomly generated dataset is performed to validate the effectiveness of the proposed algorithm and the improvement measures. The final sensitivity analysis of three key parameters shows their distinctive influences on the three objectives, according to which several suggestions are given to project managers on the effective measures to improve the three objectives.

Scalable Parallel Algorithm for Solving Non-stationary Systems of Linear Inequalities

In this paper, a scalable iterative projection-type algorithm for solving non-stationary systems of linear inequalities is considered. A non-stationary system is understood as a large-scale system of inequalities in which coefficients and constant terms can change during the calculation process. The proposed parallel algorithm uses the concept of pseudo-projection which generalizes the notion of orthogonal projection. The parallel pseudo-projection algorithm is implemented using the parallel BSF-skeleton. An analytical estimation of the algorithm scalability boundary is obtained on the base of the BSF cost metric. The large-scale computational experiments were performed on a cluster computing system. The obtained results confirm the efficiency of the proposed approach.

Principal mode of Syndecan-4 mechanotransduction for the endothelial glycocalyx is a scissor-like dimer motion.

Endothelial glycocalyx (EG) plays a pivotal role in a plethora of diseases, like cardiovascular and renal diseases. One hallmark function of the EG as a mechanotransducer which transmits mechanical signals into cytoplasm has been documented for decades. However, the basic question - how the glycocalyx transmits the flow shear stress- is unanswered so far. Our aim is to shed light on the fundamental mode of signal transmission from flow to the endothelial cytoskeleton. We conduct a series of large-scale molecular dynamics computational experiments to investigate the dynamics of glycocalyx under varying conditions (changing blood flow velocities and shedding of glycocalyx sugar chains). We have identified that the main pathway of signal transmission in this system manifests as a scissors-like motion of the Syndecan-4 core protein. Results have suggested that the force transmitted into the cytoskeleton with an order of 10~100pN, and the main function of sugar chains of a glycocalyx element is to protect the core proteins from severe conformational changes thereby maintaining the functionality of the EG. This research provides a reconciling explanation for a longstanding debate about the force transmission threshold based on our findings. A new explanation has also been provided to relate the role of the EG as a mechanotransducer to its function as a microvascular barrier: the EG regulates the mechanotransduction by altering the median value and variation range of the scissor angle, and the EG governs the microvascular barrier via controlling the scissor angle which will affect the intercellular cleft.

Multiresolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments

The Gaussian process is a standard tool for building emulators for both deterministic and stochastic computer experiments. However, application of Gaussian process models is greatly limited in practice, particularly for large-scale and many-input computer experiments that have become typical. We propose a multiresolution functional ANOVA (MRFA) model as a computationally feasible emulation alternative. More generally, this model can be used for large-scale and many-input nonlinear regression problems. An overlapping group lasso approach is used for estimation, ensuring computational feasibility in a large-scale and many-input setting. New results on consistency and inference for the (potentially overlapping) group lasso in a high-dimensional setting are developed and applied to the proposed MRFA model. Importantly, these results allow us to quantify the uncertainty in our predictions. Numerical examples demonstrate that the proposed model enjoys marked computational advantages. Data capabilities, in terms of both sample size and dimension, meet or exceed best available emulation tools while meeting or exceeding emulation accuracy. Supplementary materials for this article are available online.

How to Reorganize Computational Science and Technologies?

The current era of the fourth industrial revolution is inevitably leading to a widening gap between actively and passively developing countries, and the strategy of scientific and technological breakthrough announced in the Russian Federation is the only way to achieve the status of a world power. Modern progress is based on infinite growth rates of computational and informational capacities of the post-petaflop scales, which provide previously unthinkable opportunities for acquiring new fundamental knowledge and introducing innovations in production, natural resource management, and the economic and social spheres. The key role here is played by predictive mathematical modeling of processes and phenomena, including theoretical and applied research, supercomputation, and large-scale computer experiments, operations with large data, and artificial intelligence. Emerging super problems require huge amounts of new-generation intelligent software, which is impossible without the formulation of concepts and paradigms of activity, architectural solutions, and constructive technologies. Global trends are leading to the development of integrated computing and information environments that form an instrumental environment for automating the construction of models and algorithms, their mapping onto supercomputer platforms, and the creation of comfortable interfaces for users with different professional backgrounds. Such an ecosystem is designed for a long life cycle with the continuous development and coordinated participation of various groups of developers, as well as widespread demand, thereby leading to the formation of a new industry with mass professions with a high level of supercomputer literacy. The listed scientific and technological challenges require competent organizational and infrastructural solutions, including the interdepartmental coordination of academic, educational, and production teams. Mathware and software of supercomputer modeling should permeate all spheres of human activity similarly to the circulatory or nervous system and transmit the value of new productive forces that improve the quality of life and ensure the sustainable development of society to become knowledgeable on intensive technologies.

How to reorganize computer science and technologies?

The fundamentals of modern progress are based on the infinite growth rates of the computational and informational capacities of a post-petaflop scale, which provide unthinkable opportunities for acquiring new knowledge and introducing innovation into production, environmental management, and the economic and social spheres. The key method here is based upon the predictive mathematical modeling of processes and phenomena, including theoretical and applied research, super-computations and large-scale computer experiments, operations with mega-data sets, and artificial intelligence. The solution to emerging supertasks requires huge amounts of new generation intelligent software, which is impossible without the formation of new concepts and paradigms of activity, architectural solutions, and constructive technologies. The world is embracing the creation of integrated computing and information environments, forming an instrumental context for automating the construction of models and algorithms, their mapping onto supercomputer platforms, and the creation of comfortable interfaces for users with different professional backgrounds. Such an ecosystem is designed for a long life cycle with continuous development and the coordinated participation of various groups of developers, as well as widespread demand. In fact, this is leading to the formation of a new industry with mass professions requiring high supercomputer literacy. The identified scientific and technological challenges require competent organizational and infrastructural solutions, including the interdepartmental coordination of academic, educational, and production teams

Исследование масштабируемости алгоритма Чиммино для решения систем линейных неравенств на кластерных вычислительных системах

The paper is devoted to a scalability study of Cimmino algorithm for linear inequality systems. This algorithm belongs to the class of iterative projection algorithms. For the analytical analysis of the scalability, the BSF (Bulk Synchronous Farm) parallel computation model is used. An implementation of the Cimmino algorithm in the form of operations on lists using higher-order functions Map and Reduce is presented. An analytical estimation of the upper scalability bound of the algorithm for cluster computing systems is derived. Information about the implementation of Cimmino algorithm on lists in C++ language using the BSF program skeleton and MPI parallel programming library is given. The results of large-scale computational experiments performed on a cluster computing system are demonstrated. A conclusion about the adequacy of the analytical estimations by comparing them with the results of computational experiments is made.

Непрерывная интеграция функционального наполнения распределенных пакетов прикладных программ

We propose a new approach to solving important practical problems of complex debugging, joint testing, and analysis of the execution time of software module versions in a heterogeneous distributed computing environment that integrating Grid and cloud computing. These problems arise in the process of supporting the continuous integration of modules of distributed applied software packages. The study focuses on the packages that are used to conduct large-scale computational experiments. The scientific novelty of the proposed approach is to combine the methodology for creating the packages with modern software development practices based on its continuous integration using knowledge about the specifics of the problems being solved. Our contribution is multifold. We expanded the capabilities of continuous integration tools by developing new additional tools for the markup and transformation of data from poorly structured sources and predicting modules execution time. In addition, we developed a technological scheme of the joint applying our developed tools and external systems for continuous integration. Therefore, we provide a more large range of capabilities of continuous integration in relation to the processes of creating and using the packages in comparison with the well-known tools. The fundamental basis of their functioning is a new conceptual model of the packages. This model supports the specification, planning, and execution of software continuous integration processes taking into account the specific subject data and problems being solved. Applying the developed tools in practice leads to a decrease in the number of errors and failures of applied software in the development and use of the packages. In turn, such decrease significantly reduces the time for large-scale computational experiments and increases the efficiency of using resources of the environment. The results of practical experiments on the use of system prototype for continuous integration of applied software show their high efficiency.