Multiple Programming Model Research Articles

Dynamic feedback algorithm based on spatial corner fitness for solving the three-dimensional multiple bin-size bin packing problem

To improve cargo loading efficiency and achieve diverse needs of companies for the loading process, this paper innovatively establishes a multiple objective mixed integer programming model for the three-dimensional multiple bin-size bin packing problem (3D-MBSBPP). The model is designed to maximize container space utilization rate and cargo load balance, minimize container usage costs, and incorporates some practical constraints. On this basis, we propose a novel dynamic feedback algorithm based on spatial corner fitness (SCF_DFA) to solve this model, which consists of three stages. Specifically, Stage 1 employs a heuristic algorithm based on spatial corner fitness to optimize the search of the remaining spaces. Stage 2 employs a container type selection algorithm to dynamically adjust and optimize container types. Stage 3 uses an improved genetic algorithm to improve the quality of the solutions of the first two stages. We demonstrate the effectiveness of the proposed algorithm through comparative experiments on benchmark instances, and apply it to solve the real-life instances for the 3D-MBSBPP. The results show that the proposed algorithm can make the average container space utilization rate reach 85.38%, which is 1.48% higher than that of baseline method, while the loading results obtained are more balanced, indicating the advantages of the SCF_DFA in solving 3D-MBSBPP. Furthermore, we conduct ablation experiments to confirm the effectiveness of each component within the algorithm.

Manipulating hidden-Markov-model inferences by corrupting batch data

Time-series models typically assume untainted and legitimate streams of data. However, a self-interested adversary may have incentive to corrupt this data, thereby altering a decision maker’s inference. Within the broader field of adversarial machine learning, this research provides a novel, probabilistic perspective toward the manipulation of hidden Markov model inferences via corrupted data. In particular, we provision a suite of corruption problems for filtering, smoothing, and decoding inferences leveraging an adversarial risk analysis approach. Multiple stochastic programming models are set forth that incorporate realistic uncertainties and varied attacker objectives. Three general solution methods are developed by alternatively viewing estimation from frequentist and Bayesian perspectives. The efficacy of each method is illustrated via extensive, empirical testing. The developed methods are characterized by their solution quality and computational effort, resulting in a stratification of techniques across varying problem-instance architectures. This research highlights the weaknesses of hidden Markov models under adversarial activity, thereby motivating the need for robustification techniques to ensure their security.

An Architecture for a Tri-Programming Model-Based Parallel Hybrid Testing Tool

As the development of high-performance computing (HPC) is growing, exascale computing is on the horizon. Therefore, it is imperative to develop parallel systems, such as graphics processing units (GPUs) and programming models, that can effectively utilise the powerful processing resources of exascale computing. A tri-level programming model comprising message passing interface (MPI), compute unified device architecture (CUDA), and open multi-processing (OpenMP) models may significantly enhance the parallelism, performance, productivity, and programmability of the heterogeneous architecture. However, the use of multiple programming models often leads to unexpected errors and behaviours during run-time. It is also difficult to detect such errors in high-level parallel programming languages. Therefore, this present study proposes a parallel hybrid testing tool that employs both static and dynamic testing techniques to address this issue. The proposed tool was designed to identify the run-time errors of C++ and MPI + OpenMP + CUDA systems by analysing the source code during run-time, thereby optimising the testing process and ensuring comprehensive error detection. The proposed tool was able to identify and categorise the run-time errors of tri-level programming models. This highlights the need for a parallel testing tool that is specifically designed for tri-level MPI + OpenMP + CUDA programming models. As contemporary parallel testing tools cannot, at present, be used to test software applications produced using tri-level MPI + OpenMP + CUDA programming models, this present study proposes the architecture of a parallel testing tool to detect run-time errors in tri-level MPI + OpenMP + CUDA programming models.

Application Experiences on a GPU-Accelerated Arm-based HPC Testbed.

This paper assesses and reports the experience of ten teams working to port, validate, and benchmark several High Performance Computing applications on a novel GPU-accelerated Arm testbed system. The testbed consists of eight NVIDIA Arm HPC Developer Kit systems, each one equipped with a server-class Arm CPU from Ampere Computing and two data center GPUs from NVIDIA Corp. The systems are connected together using InfiniBand interconnect. The selected applications and mini-apps are written using several programming languages and use multiple accelerator-based programming models for GPUs such as CUDA, OpenACC, and OpenMP offloading. Working on application porting requires a robust and easy-to-access programming environment, including a variety of compilers and optimized scientific libraries. The goal of this work is to evaluate platform readiness and assess the effort required from developers to deploy well-established scientific workloads on current and future generation Arm-based GPU-accelerated HPC systems. The reported case studies demonstrate that the current level of maturity and diversity of software and tools is already adequate for large-scale production deployments.

Accelerating communication for parallel programming models on GPU systems

As an increasing number of leadership-class systems embrace GPU accelerators in the race towards exascale, efficient communication of GPU data is becoming one of the most critical components of high-performance computing. For developers of parallel programming models, implementing support for GPU-aware communication using native APIs for GPUs such as CUDA can be a daunting task as it requires considerable effort with little guarantee of performance. In this work, we demonstrate the capability of the Unified Communication X (UCX) framework to compose a GPU-aware communication layer that serves multiple parallel programming models of the Charm++ ecosystem: Charm++, Adaptive MPI (AMPI), and Charm4py. We demonstrate the performance impact of our designs with microbenchmarks adapted from the OSU benchmark suite, obtaining improvements in latency of up to 10.1x in Charm++, 11.7x in AMPI, and 17.4x in Charm4py. We also observe increases in bandwidth of up to 10.1x in Charm++, 10x in AMPI, and 10.5x in Charm4py. We show the potential impact of our designs on real-world applications by evaluating a proxy application for the Jacobi iterative method, improving the communication performance by up to 12.4x in Charm++, 12.8x in AMPI, and 19.7x in Charm4py.

Enhanced optimization assisted interference mitigation with vertical beamforming in 3D MIMO-OFDMA for 5G wireless communication network

PurposeMultiple input multiple-output (MIMO) has emerged as one among the many noteworthy technologies in recent wireless applications because of its powerful ability to improve bandwidth efficiency and performance, i.e. through developing its unique spatial multiplexing capability and spatial diversity gain. For carrying out an enhanced communication in next-generation networks, the MIMO and orthogonal frequency division multiple systems were combined that facilitate the spatial multiplexing on resource blocks (RBs) based on time-frequency. This paper aims to propose a novel approach for maximizing the throughput of cell-edge users and cell-center users.Design/methodology/approachIn this work, the specified multi-objective function is defined as the single objective function, which is solved by the introduction of a new improved algorithm as well. This optimization problem can be resolved by the fine-tuning of certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation. The fine-tuning of parameters is attained by a new improved Lion algorithm (LA), termed as Lion with new cub generation (LA-NCG) model. Finally, the betterment of the presented approach is validated over the existing models in terms of signal to interference plus noise ratio, throughput and so on.FindingsOn examining the outputs, the adopted LA-NCG model for 4BS was 66.67%, 66.67% and 20% superior to existing joint processing coordinated multiple point-based dual decomposition method (JC-DDM), fractional programming (FP) and LA models. In addition, the throughput of conventional JC-DDM, FP and LA models lie at a range of 10, 45 and 35, respectively, at the 100th iteration. However, the presented LA-NCG scheme accomplishes a higher throughput of 58. Similarly, the throughput of the adopted scheme observed for 8BS was 59.68%, 44.19% and 9.68% superior to existing JC-DDM, FP and LA models. Thus, the enhancement of the adopted LA-NCG model has been validated effectively from the attained outcomes.Originality/valueThis paper adopts the latest optimization algorithm called LA-NCG to establish a novel approach for maximizing the throughput of cell-edge users and cell-center users. This is the first that work uses LA-NCG-based optimization that assists in fine-tuning certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation.

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes. Therefore, an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development. This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network (BBN) approach based on an interpretive structural modeling technique. The BBN models are trained and tested using a wide-range case-history records database. The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions. The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models. The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause-effect relationships, with reasonable precision. This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.

Day surgery appointment scheduling with patient preferences and stochastic operation duration.

Due to its fast service and high utilization, day surgery is becoming more and more important in the medical system. As a result, an effective day surgery scheduling can reasonably release the supply and demand pressure. This paper aims to investigate the day surgery scheduling problem with patient preferences and limited operation room for the sake of increasing operation efficiency and further decreasing surgery costs. A multiple objective stochastic programming model is constructed to seek a satisfactory surgical scheduling for both patients and hospitals under different scenarios. Multi-objective genetic algorithm is designed to solve the model and different scales of scenarios are utilized to test the effectiveness of the algorithm and modeling process. Results show that the proposed model and algorithm can provide a feasible solution for maximizing individual preference of surgeons with surgery date and operation room utilization as well. Patient preference is proposed to be incorporated into day surgery scheduling, and the variability of surgery duration considered to seek a satisfactory surgery scheduling scheme for both patients and hospitals is more in line with the actual hospital situation.

Operation strategy of park microgrid with multi‐stakeholder based on bi‐level optimisation

The new reform of power system promotes the market-oriented operation of microgrids. The ubiquitous power internet of things provide support in information, data, and computation to microgrids in market operation, energy management, and coordination interaction. This study takes the park microgrid with multi-stakeholder as the object, establishes a two-level optimisation model of microgrid bidding transaction based on multi-agent system. In the lower level optimisation, considering the deviation penalty of power generation and the previous round bidding results, the optimal bidding strategy model is established by the bidding unit agent to maximise its benefit. In the upper-level model, bidding strategies of distributed energy resources (DERs) as constraints, a multiple objective mixed-integer linear programming model was built to optimise the overall objectives of clearing price and imbalanced deviation, searching for the optimal clearing price and the generation plan of DERs. The results were fed back to the DERs to help them form the next round of strategies until the result reaches equilibrium. The proposed optimised operation mode is compared with the traditional operation mode in the case study, verifying that the proposed method can realise the optimal operation of the microgrid and the coordinated interaction with the main grid, increasing the benefit of stakeholders.

Environmental evaluation and optimization of energy use and greenhouse gases mitigation for farm production systems in Mashhad, Iran.

The current paper aimed at evaluating and optimizing the efficiency of energy use and mitigation of environmental pollution by minimizing the emissions and intensity of greenhouse gases (GHG) for farm production systems in Mashhad, Iran. The results indicated that the total values of consumed energy were equal to 28,648, 38,479, and 43,490MJha-1 for barley, canola, and forage corn, respectively. The highest rate of the consumed energy for all crops belonged to irrigation electricity, diesel fuel, and nitrogen consumption. The highest and lowest energy efficiencies were respectively achieved from forage corn and canola, while barley production led to the lowest GHG emissions. The estimation of GHG intensity demonstrated that canola with 53% took up the highest intensity, which was followed by barley (30%) and forage corn (17%). Since the intensity of GHG takes into account the amount of energy produced by each crop per hectare, this index is more suitable than GHG emissions for assessing environmental pollution. The results of optimal responses of the multiple goal programming (MGP) model revealed that the optimum evaluated cultivation areas (for meeting the goals of the study) for barley and forage corn were 10,088 and 3256ha, respectively, while the MGP model generally eliminates canola from the cultivation plan. On the other hand, the plan proposed by the MGP model increases the total energy efficiency from 103,625 for the current systems to 105,169 for the optimized systems per year. Furthermore, the emissions and intensity of GHG were reduced by the model to 311,786kgCO2eq and 10kgCO2eqMJoutput-1 per year, respectively.

Discharge coefficients for ogee weirs including the effects of a sloping upstream face

Abstract Discharge coefficients (C0) for ogee weirs are essential factors for predicting the discharge-head relationship. The present study investigates three influences on the C0: effect of approach depth, weir upstream face slope, and the actual head, which may differ from the design head. This study uses experimental data with multiple non-linear regression techniques and Gene Expression Programming (GEP) models that are applied to introduce practical equations that can be used for design. Results show that the GEP method is superior to the regression analysis for predicting the discharge coefficient. Performance criteria for GEP are R2 = 0.995, RMSE = 0.021 and MAE = 0.015. Design examples are presented that show that the proposed GEP equation correlates well with the data and eliminates linear interpolation using existing graphs.

A Behavioral Portfolio Decision Model with Triangular Fuzzy Number Return and Investor’s Sentiment

This paper deals with a behavioral portfolio decision problem with triangular fuzzy number return. A fuzzy sentimental mean model for behavioral portfolio decision is proposed by taking into account investor’s sentiment and multiple mental accounts. The presented behavioral portfolio decision model maximizes the fuzzy sentimental mean value of portfolio return and ensures the portfolio return of each mental account exceeding the given minimum triangular fuzzy number level with a given possibility degree. Then, multiple programming models are designed to solve the optimal behavioral portfolio strategy. Finally, a numerical example is given to illustrate the validity of the proposed approach.

Multi-criteria approach for platelet inventory management in hospitals

In this paper, a multiple criteria approach is proposed for platelet inventory management in hospitals. It has been reported that about 20% of the total platelet collected is outdated due to the short shelf life of platelets and demand uncertainty resulting in severe shortage. A multiple criteria mathematical programming (MCMP) model is developed to minimise three criteria: platelet wastage, shortage, and procurement and holding cost. The MCMP problem is solved using three MCMP techniques; preemptive goal programming (PGP), non-preemptive goal programming (NPGP) and weighted objective methods (WOM). A case study is discussed by applying the model to the daily demand data of platelets. Sensitivity analysis is also performed to analyze the impact of goal priorities in the PGP model and weights in the NPGP model and WOM. Based on the policies obtained under PGP, NPGP and WOM methods, the hospital management can decide the most suitable inventory policy for implementation. From the results, it is recommended that preemptive goal programming technique can be used if few goals are extremely important or given very high priority compared to others and is recommended to use weighted moving average or non-preemptive goal programming techniques if equal importance is given to all the goals.

Multi-criteria approach for platelet inventory management in hospitals

In this paper, a multiple criteria approach is proposed for platelet inventory management in hospitals. It has been reported that about 20% of the total platelet collected is outdated due to the short shelf life of platelets and demand uncertainty resulting in severe shortage. A multiple criteria mathematical programming (MCMP) model is developed to minimise three criteria: platelet wastage, shortage, and procurement and holding cost. The MCMP problem is solved using three MCMP techniques; preemptive goal programming (PGP), non-preemptive goal programming (NPGP) and weighted objective methods (WOM). A case study is discussed by applying the model to the daily demand data of platelets. Sensitivity analysis is also performed to analyze the impact of goal priorities in the PGP model and weights in the NPGP model and WOM. Based on the policies obtained under PGP, NPGP and WOM methods, the hospital management can decide the most suitable inventory policy for implementation. From the results, it is recommended that preemptive goal programming technique can be used if few goals are extremely important or given very high priority compared to others and is recommended to use weighted moving average or non-preemptive goal programming techniques if equal importance is given to all the goals.

A Behavioral Portfolio Model with Interval Return and Investor’s Sentiment

This paper proposes a behavioral portfolio decision model with interval returns and investor’s sentiment. A sentiment-adjusted mean model for behavioral portfolio selection is presented by taking into account investor’s sentiment return and multiple mental accounts. The proposed behavioral model maximizes the sentimental mean value of portfolio interval return and ensures the portfolio interval return of each mental account exceeding the given minimum return level with a given possibility degree. Then, multiple programming models are designed to solve the optimal behavioral portfolio strategy. Finally, a numerical example is given to illustrate the validity of the proposed approach.

Furniture board segmentation based on multiple sensors and integer programming model

In the current furniture production line, the level of automation in the stage of loading and unloading is not high enough. In order to improve its automation, a novel integer programming based method for automatically segmenting board is proposed and a multi-sensor configuration is given. In such a configuration, we include multiple cameras and Lidar sensors. The cameras attached on each board are used to collect quick response (QR) code information, while Lidar sensors can obtain each board’s contours information. We then formulate each board’s segmentation as the integer programming problem. The experimental results show that our method can achieve a very high segmentation accuracy of 95% on average.

Stochastic goal programming approach to determine the side effects to be labeled on pharmaceutical drugs

Pharmaceutical companies constantly face the challenge of providing high-quality products that maximize benefits while minimizing risks to patients. Recent years have witnessed an increase in drug-reaction-related lawsuits driving pharmaceutical companies to declare more side effects on the product label, which may negatively influence the product sales because patients or prescribing physicians may perceive the drug to be unsafe. Clearly, the objectives of minimizing lawsuit cost and potential customer loss are conflicting in nature, and therefore, it is necessary to determine the side effects to report on the label such that the best compromise is achieved between the two. A stochastic multiple criteria mathematical programming (SMCMP) model is developed to serve as a decision support system in assisting pharmaceutical companies with labeling decisions. A non-preemptive stochastic goal programming technique is used to solve the SMCMP model to attain a tradeoff solution between the two objectives. We verify the model with sample datasets and conduct sensitivity analysis by varying the lawsuit costs and number of customers lost. The results indicate that when the target value for lawsuit cost increases, fewer drug-related reactions are declared, while more side effects are labeled with an increase in target customer loss.

Regional Input—Output Multiple Choice Goal Programming Model and Method for Industry Structure Optimization on Energy Conservation and GHG Emission Reduction in China

To assess the potential of China’s industrial restructuring on energy conservation and greenhouse gas (GHG) emission reduction in 2020, this study proposes an input—output multi-choice goal programming model and method. In this model, the goals include the maximization of gross domestic product (GDP), minimization of energy consumption and GHG emission. They are subjected to the input—output balance, economy development, energy supply, and industry diversity. And four scenarios with different decision preferences are taken into accounted in the solutions of the industrial structure optimization model. The results demonstrate that industrial restructuring has potential in energy saving and emission reducing. First, after optimization, energy consumption intensity and GHG emission intensity can drop by 13.88246% and 5.33767% over 2012, and GDP can grow up at annual growth rate 6.6% from 2013–2020. Second, promoting the development of the low energy-intensive and low GHG emission intensive sectors is an effective method for energy conservation and emission reduction. Three, compared to energy intensity reduction, GHG emission intensity reduction is less effective for four scenarios. Four, there are several difficulties to achieve the amounts and intensity control targets of energy conservation and GHG emission reduction simultaneously. It is suggested that China had better strive to promote progress of technologies of energy conservation and GHG emission reduction while adjusting the industrial structure.

A multi-criterion approach to optimal vaccination planning: Method and solution

Seasonal influenza is a serious public health concern, against which vaccination is one of the most effective ways to protect people. However, the effect of vaccination on containing influenza spread critically depends on the immunization programme adopted. Therefore, the problem of finding the optimal combination of vaccination strategies, with a view to decreasing the programme cost, enhancing vaccination efficiency, and improving societal benefits, is of great theoretical and practical importance. We develop a multiple criteria mathematical programming model to address the problem, analyze the model, and derive the structural properties of the optimal solution. Conducting extensive numerical studies to assess the merit of the model, we find that an integrated strategy embracing early-stage indiscriminate mass vaccination with late-stage targeted vaccination outperforms other strategies in cost and efficacy.

Runtime Support for Multiple Offload-Based Programming Models on Clustered Manycore Accelerators

Heterogeneous systems coupling a main host processor with one or more manycore accelerators are being adopted virtually at every scale to achieve ever-increasing GOps/Watt targets. The increased hardware complexity of such systems is paired at the application level by a growing number of applications concurrently running on the system. Techniques that enable efficient accelerator resources sharing, supporting multiple programming models will thus be increasingly important for future heterogeneous SoCs. In this paper we present a runtime system for a cluster-based manycore accelerator, optimized for the concurrent execution of offloaded computation kernels from different programming models. The runtime supports spatial partitioning, where clusters can be grouped into several virtual accelerator instances. Our runtime design is modular and relies on a generic component for resource (cluster) scheduling, plus specialized components which deploy generic offload requests into the target programming model semantics. We evaluate the proposed runtime system on two real heterogeneous systems, focusing on two concrete use cases: i) single-user, multi-application high-end embedded systems and ii) multi-user, multi-workload low-power microservers. In the first case, our approach achieves 93 percent efficiency in terms of available accelerator resource exploitation. In the second case, our support allows 47 percent performance improvement compared to single-programming model systems.