Optimization Techniques for GPU-Based Parallel Programming Models in High-Performance Computing

This study embarks on a comprehensive examination of optimization techniques within GPU-based parallel programming models,pivotal for advancing high-performance computing(HPC).Emphasizing the transition of GPUs from graphic-centric processors to versatile computing units,it delves into the nuanced optimization of memory access,thread management,algorithmic design,and data structures.These optimizations are critical for exploiting the parallel processing capabilities of GPUs,addressingboth the theoretical frameworks and practical implementations.By integrating advanced strategies such as memory coalescing,dynamic scheduling,and parallel algorithmic transformations,this research aims to significantly elevate computational efficiency and throughput.The findings underscore the potential of optimized GPU programming to revolutionize computational tasks across various domains,highlighting a pathway towards achieving unparalleled processing power and efficiency in HPC environments.The paper not only contributes to the academic discourse on GPU optimization but also provides actionable insights for developers,fostering advancements in computational sciences and technology.

Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

Mathematical Model and Simulation of Harmful Invasive Plant Control

Plant invasion refers to the phenomenon that some plants grow too fast due to they are far away from the original living environment or predators, affecting the local environment. With the development of tourism and trade, the harm caused by invasive plants will be more and more serious. Therefore, it is necessary to ex- plore an effective method for controlling plant invasion through qualitative and quan- titative research. In this paper, the models were established for the early and late harmful plant invasion control. The huge computation was completed by the com- puter programming to obtain the optimal solutions of the models. The real meaning of the optimal solution was further discussed. Through numerical simulations and discussion, it could be concluded that the quantitative research on the invasive plant control had a certain application value.

SPICE model of trench-gate MOSFET device

A novel simulation program with an integrated circuit emphasis（SPICE） model developed for trench-gate metal-oxide-semiconductor field-effect transistor（M OSFET）devices is proposed. The drift region resistance was modeled according to the physical characteristics and the specific structure of the trench-gate MOSFET device. For the accurate simulation of dynamic characteristics, three important capacitances, gate-to-drain capacitance Cgd, gate-to-source capacitance Cgsand drain-to-source capacitance Cds, were modeled, respectively, in the proposed model. Furthermore,the self-heating effect, temperature effect and breakdown characteristic were taken into account; the self-heating model and breakdown model were built in the proposed model; and the temperature parameters of the model were revised. The proposed model is verified by experimental results, and the errors between measured data and simulation results of the novel model are less than 5%. Therefore, the model can give an accurate description for both the static and dynamic characteristics of the trench-gate MOSFET device.

On Enforcing Dyadic-type Homogeneous Binary Function Product Constraints in MatBase

Homogeneous binary function products are frequently encountered in the sub-universes modeled by databases,spanning from genealogical trees and sports to education and healthcare,etc.Their properties must be discovered and enforced by the software applications managing such data to guarantee plausibility.The(Elementary)Mathematical Data Model provides 17 types of dyadic-based homogeneous binary function product constraint categories.MatBase,an intelligent data and knowledge base management system prototype,allows database designers to simply declare them by only clicking corresponding checkboxes and automatically generates code for enforcing them.This paper describes the algorithms that MatBase uses for enforcing all 17 types of homogeneous binary function product constraint,which may also be employed by developers without access to MatBase.

Bi-level programming model for reconstruction of urban branch road network

Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level programming model for reconstructing the branch roads was set up. The upper level model was for determining the enlarged capacities of the branch roads, and the lower level model was for calculating the flows of road sections via the user equilibrium traffic assignment method. The genetic algorithm for solving the bi-level model was designed to obtain the reconstruction capacities of the branch roads. The results show that by the bi-level model and its algorithm, the optimum scheme of urban branch roads reconstruction can be gained, which reduces the saturation of arterial roads apparently, and alleviates traffic congestion. In the data analysis the arterial saturation decreases from 1.100 to 0.996, which verifies the micro-circulation transportation's function of urban branch road network.

Principal-subordinate hierarchical multi-objective programming model of initial water rights allocation

The principal-subordinate hierarchical multi-objective programming model of initial water rights allocation was developed based on the principle of coordinated and sustainable development of different regions and water sectors within a basin. With the precondition of strictly controlling maximum emissions rights, initial water rights were allocated between the first and the second levels of the hierarchy in order to promote fair and coordinated development across different regions of the basin and coordinated and efficient water use across different water sectors, realize the maximum comprehensive benefits to the basin, promote the unity of quantity and quality of initial water rights allocation, and eliminate water conflict across different regions and water sectors. According to interactive decision-making theory, a principal-subordinate hierarchical interactive iterative algorithm based on the satisfaction degree was developed and used to solve the initial water rights allocation model. A case study verified the validity of the model.

Estimation of the Maximum Allowable Loading Amount of COD in Luoyuan Bay by a 3-D COD Transport and Transformation Model

The rapid economic and social developments in the Luoyuan and Lianjiang counties of Fujian Province,China,raise certain environment and ecosystem issues.The unusual phytoplankton bloom and eutrophication,for example,have increased in severity in Luoyuan Bay （LB）.The constant increase of nutrient loads has largely caused the environmental degradation in LB.Several countermeasures have been implemented to solve these environmental problems.The most effective of these strategies is the reduction of pollutant loadings into the sea in accordance with total pollutant load control （TPLC） plans.A combined three-dimensional hydrodynamic transport-transformation model was constructed to estimate the marine environmental capacity of chemical oxygen demand （COD）.The allowed maximum loadings for each discharge unit in LB were calculated with applicable simulation results.The simulation results indicated that the environmental capacity of COD is approximately 11×l0^4tyear^-1 when the water quality complies with the marine functional zoning standards for LB.A pollutant reduction scheme to diminish the present levels of mariculture-and domestic-based COD loadings is based on the estimated marine COD environmental capacity.The obtained values imply that the LB waters could comply with the targeted water quality criteria.To meet the revised marine functional zoning standards,discharge loadings from discharge units 1 and 11 should be reduced to 996 and 3236t year^-1,respectively.

Tri-level programming model for combined urban traffic signal control and traffic flow guidance

In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm(HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages(MSA), the middle level model is solved by non-dominated sorting genetic algorithm II(NSGA II), and the upper level model is solved by genetic algorithm(GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method.

A Quadratic Programming Model for Blast Scheduling

A quadratic programming model is established to choose the blocks to be blasted in a given period. The length of this period depends on the production planning requirements. During the given period, the blocks' parameters are available from the geological database of the mine. The objective is to minimize the deviation of the average ore grade of blasted blocks from the standard ore grade required by the mill. Transportation ability constraint. production quantity demand constraint. minimum safety bench constraint. block size constraint and block, bench precedence constraints are considered in forming the programming model. This model has more practical objective function and reasonable constraints compared with the existing model for this kind of problems.

Genetic programming-based chaotic time series modeling

This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.

Weekly Fleet Assignment Model and Algorithm

A 0-1 integer programming model for weekly fleet assignment was put forward based on linear network and weekly flight scheduling in China. In this model, the objective function is to maximize the total profit of fleet assignment, subject to the constraints of coverage, aircraft flow balance, fleet size, aircraft availability, aircraft usage, flight restriction, aircraft seat capacity, and stopover. Then the branch-and-bound algorithm based on special ordered set was applied to solve the model. At last, a real- wofld case study on an airline with 5 fleets, 48 aircrafts and 1 786 flight legs indicated that the profit increase was ￥ 1 591276 one week and the running time was no more than 4 rain, which shows that the model and algorithm are fairly good for domestic airline.

HEURISTIC MODELING FOR A DYNAMIC AND GOAL PROGRAMMING IN PRODUCTION PLANNING OF CONTINUOUS MANUFACTURING SYSTEMS

At the first sight it seems that advanced operation research is not used enough in continuous production systems as comparison with mass production, batch production and job shop systems, but really in a comprehensive evaluation the advanced operation research techniques can be used in continuous production systems in developing countries very widely, because of initial inadequate plant layout, stage by stage development of production lines, the purchase of second hand machineries from various countries, plurality of customers. A case of production system planning is proposed for a chemical company in which the above mentioned conditions are almost presented. The goals and constraints in this issue are as follows： （1） Minimizing deviation of customer＇s requirements. （2） Maximizing the profit. （3） Minimizing the frequencies of changes in formula production. （4） Minimizing the inventory of final products. （5） Balancing the production sections with regard to rate in production. （6） Limitation in inventory of raw material. The present situation is in such a way that various techniques such as goal programming, linear programming and dynamic programming can be used. But dynamic production programming issues are divided into two categories, at first one with limitation in production capacity and another with unlimited production capacity. For the first category, a systematic and acceptable solution has not been presented yet. Therefore an innovative method is used to convert the dynamic situation to a zero- one model. At last this issue is changed to a goal programming model with non-linear limitations with the use of GRG algorithm and that＇s how it is solved.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

A dynamic food-chain model and program for predicting the consequences of nuclear accident

A dynamic food chain model and program, DYFOM 95, for predicting the radiological consequences of nuclear accident has been developed, which is not only suitable to the West food chain but also to Chinese food chain. The following processes, caused by accident release which will make an impact on radionuclide concentration in the edible parts of vegetable were considered: dry and wet deposition interception and initial retention, translocation, percolation, root uptake and tillage. Activity intake rate of animals, effects of processing and activity intake of human through ingestion pathway ware also considered in calculations. The effects of leaf area index LAI of vegetable were considered in dry deposition model. A method for calculating the contribution of rain with different period and different intensity to total wet deposition was established. The program contains 1 main code and 5 sub codes to calculate dry and wet deposition on surface of vegetable and soil, translocation of nuclides in vegetable, nuclide concentration in the edible parts of vegetable and in animal products and activity intake of human and so on.

Model and algorithm of optimizing alternate traffic restriction scheme in urban traffic network

An optimization model and its solution algorithm for alternate traffic restriction(ATR) schemes were introduced in terms of both the restriction districts and the proportion of restricted automobiles. A bi-level programming model was proposed to model the ATR scheme optimization problem by aiming at consumer surplus maximization and overload flow minimization at the upper-level model. At the lower-level model, elastic demand, mode choice and multi-class user equilibrium assignment were synthetically optimized. A genetic algorithm involving prolonging codes was constructed, demonstrating high computing efficiency in that it dynamically includes newly-appearing overload links in the codes so as to reduce the subsequent searching range. Moreover,practical processing approaches were suggested, which may improve the operability of the model-based solutions.

New Latency Model for Dynamic Frequency Scaling on Network-on-Chip

Modulating both the clock frequency and supply voltage of the network-on-chip （NoC） during runtime can reduce the power consumption and heat flux, but will lead to the increase of the latency of NoC. It is necessary to find a tradeoff between power consumption and communication latency. So we propose an analytical latency model which can show us the relationship of them. The proposed model to analyze latency is based on the M/G/1 queuing model, which is suitable for dynamic frequency scaling. The experiment results show that the accuracy of this model is more than 90%.

Parallel Machine Scheduling Models with Fuzzy Parameters and Precedence Constraints: A Credibility Approach

A method for modeling the parallel machine scheduling problems with fuzzy parameters and precedence constraints based on credibility measure is provided. For the given n jobs to be processed on m machines, it is assumed that the processing times and the due dates are nonnegative fuzzy numbers and all the weights are positive, crisp numbers. Based on credibility measure, three parallel machine scheduling problems and a goal-programming model are formulated. Feasible schedules are evaluated not only by their objective values but also by the credibility degree of satisfaction with their precedence constraints. The genetic algorithm is utilized to find the best solutions in a short period of time. An illustrative numerical example is also given. Simulation results show that the proposed models are effective, which can deal with the parallel machine scheduling problems with fuzzy parameters and precedence constraints based on credibility measure.

Medical images application of contour extraction based on Hermite splines contour model

Active Contour Model or Snake model is an efficient method by which the users can extract the object contour of Region Of Interest （ROI）. In this paper, we present an improved method combining Hermite splines curve and Snake model that can be used as a tool for fast and intuitive contour extraction. We choose Hermite splines curve as a basic function of Snake contour curve and present its energy function. The optimization of energy minimization is performed hy Dynamic Programming technique. The validation results are presented, comparing the traditional Snake model and the HSCM, showing the similar performance of the latter. We can find that HSCM can overcome the non-convex constraints efficiently. Several medical images applications illustrate that Hermite Splines Contour Model （HSCM） is more efficient than traditional Snake model.

Knowledge representation and rule-based solution system for dynamic programming model

A knowledge representation has been proposed using the state space theory of Artificial Intelligence for Dynamic Programming Model, in which a model can be defined as a six tuple M=(I,G,O,T,D,S). A building block modeling method uses the modules of a six tuple to form a rule based solution model. Moreover, a rule based system has been designed and set up to solve the Dynamic Programming Model. This knowledge based representation can be easily used to express symbolical knowledge and dynamic characteristics for Dynamic Programming Model, and the inference based on the knowledge in the process of solving Dynamic Programming Model can also be conveniently realized in computer.