A Simulation Framework for Rapid Analysis of Reconfigurable Computing Systems

In this paper, the problem of parallel loop scheduling for heterogeneous networks of workstations is discussed. A practical network of workstations is heterogeneous where computing power varies in different composing workstations. A simple loop scheduling technique is insufficient in exploiting maximum computing power of such a system. We propose a fundamental idea for a performance prediction tool to gauge the relative computing power among composing workstations so that parallel performance of a program run on a given network can be predicted. In view of the performance prediction results, a loop scheduling approach is then incorporated into the system to achieve close-to-optimal parallel performance. Examples running benchmark programs demonstrate a significant gain from the proposed approach over traditional scheduling approaches.

For the centrifugal compressor aerodynamic design of a turbocharger, first of all, the works for system matching to the engine specification must be preceded. Then, mean line design together with performance prediction should be carried out for preliminary design. In the mean line prediction, a slip factor is adopted as a function of flow coefficient and geometry instead of Wiesner’s equation, and it is found that the predicted result of slip magnitude is more accurate than that of conventional slip factor. Also, three-dimensional blade profile shape is generated on the basis of the preliminary design. The Navier-Stokes Equation solver with a turbulent model is used to find whether three-dimensionally designed geometry is reasonable by analyzing loading distribution of the blade. By investigating diffuser flow field of the simulated result, the diffuser inlet and exit angles were modified for the flow to move smoothly along the diffuser geometry. Modified performance prediction results shows better than those of original specification. Consequently, off design performance prediction results and numerical simulation result show good agreement with the experimental data. The modified design results show more increased compression ratio and efficiency than those of previous design results. The increased choke margin has made a stable operating range larger.

PurposeThis research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities.Design/methodology/approachThe authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance.FindingsThe major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs.Research limitations/implicationsThis paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities.Practical implicationsThe findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings.Originality/valueThis research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

This paper examines the viability of using the combination of the vortex lattice method for aerodynamic performance prediction with a genetic algorithm for the optimization of the aerodynamic performance of horizontal axis wind turbine blades. The work described in this paper includes the adaptation of a vortex lattice code designed to predict propeller performance to wind turbine performance prediction and the optimization process including results for both single point and multipoint design optimization efforts. Background The economics of deploying large wind turbine farms as a substantial source of electrical power is driven in large part by the efficiency of power conversion from wind energy to rotational mechanical energy. In the 1920’s, Betz formulated the basic analysis for the limiting case for horizontal axis wind turbine efficiency and set up the guidelines for how windturbine efficiencies should be calculated. A modern explanation of the Betz analysis can be found in Reference 1. In the 1930’s Glauert applied classical aerodynamic methods to airplane propeller designs in an effort to optimize performance of the horizontal axis machine for propulsion. 2 In the 1970’s and 1980’s blade element and momentum theory models were developed and refined for modeling wind turbine performance 3-5 and the efficiency of these models made them ideally suited for the genetic algorithm optimization work performed in the 1990’s by Selig et al. 6 This method for optimizing horizontal axis wind turbines using genetic algorithms used an improved version of the momentum theory models and demonstrated a successful and efficient optimization strategy. Hampsey 1 improved upon the optimization efforts by using a B-spline approach for modeling the blades along with a panel method for performance prediction. In this important optimization using a relatively higher order method for aerodynamic prediction, the blade geometries were not modeled using traditional airfoil shape theory. In the development of performance prediction for sails, it has been shown that the prediction of aerodynamic loads using the vortex lattice method is often much more accurate than load predictions based on simpler momentum theory methods. As with the panel methods, computational efficiency necessary for optimization can be maintained with the vortex lattice method. 7 A vortex lattice method has been applied to ship propellers 8 and to airplane propellers and has been shown to accurately predict performance in appropriate applications. 9,10 The airplane propeller analysis reported in

The long-run queueing performance of a multi-scale TCP traffic model, the HOO model, is analysed. Since the model links the multi-scale behaviour with practical traffic elements and approximates TCP traffic very well, the analysis is expected to provide insights into the physical interpretation of multi-scale traffic and to give useful results for performance prediction. To derive a meaningful solution and avoid the extreme difficulty of an exact analysis, the authors adopt several techniques to track the problem, among which are techniques to establish equivalent processes to the traffic process or the queue content process in two cases, the case of fast flows and the case of slow flows. Quantitative results for the queue tails are obtained in both cases, and a unified form is derived. It indicates that three levels of traffic elements in different time scales, i.e. the connection, the burst and the packet, all affect the asymptotic queueing performance. It shows quantitatively how the connection determines the index of the queue tail, and the burst and the packet contribute to the tail with their averages. Used with simple statistical inferences, the analytical result is shown to predict the queueing performance of real traffic well.

Different techniques for estimating the execution time of parallel applications have been studied for the last 25 years. These approaches have proposed different methods for predicting the performance behaviour of applications. Most of these methods rely on analysing one or more of the following aspects: system workload, application structure, platform system, and the computing resources that the application needs to perform its operations. These elements are used and applied by different methods such as analytic and non-analytic methods. However, no wide-ranging survey of these approaches exists at the time of writing. This paper presents a systematic review of performance prediction methods for parallel applications, which were published in the open literature during the period 2005–2020. We define a classification framework to categorise the reviewed approaches. In addition, we identify some directions and trends in performance prediction as well as some unsolved issues.

This paper presents a new and simple search technique to determine the optimum control parameters, such as turn-on and turn-off angles, current set level, etc., of a switched reluctance drive. Thanks to this new technique, several drive performance quantities such as efficiencies, torque ripple, energy consumption, torque per ampere, etc., can be optimized simultaneously over the entire torque- or power-speed operation range of the drive. In prior work, these performance quantities were sequentially optimized, one at a time, this despite reported evidence that improving one performance quantity, such as efficiency, can have a negative impact on others, such as torque ripple. The technique was successfully used offline to determine the optimum firing angles for achieving highest drive efficiency with lower torque ripple in a 102-kW switched reluctance motor drive intended for electric vehicle propulsion. The performance prediction and test results are found in good correlation.

The simulation of thermal–fluid–solid coupling in turbines is critically important for design optimization. Historically, most research on thermal–fluid–solid coupling has been conducted in three-dimensional, often with computational speeds that do not meet practical expectations. This study proposes a one-dimensional performance prediction and multi-objective optimization design methodology for turbines, integrating aerothermodynamics and structural strength, to facilitate rapid multidisciplinary coupling design optimization at a low-dimensional level. Initially, a multidisciplinary coupled turbine performance prediction model is established, incorporating the combined effects of turbine aerothermodynamics and structural mechanics. This model links the thermodynamics of the blade passage with the stress and strain of the blade. The predictive accuracy of this model is validated against experimental data from a four-stage axial flow turbine, showing a maximum error of 1.56% for the total temperature ratio and 1.69% for the total expansion ratio. Subsequently, using blade parameters, degree of reaction, load coefficient, and flow coefficient as optimization variables and targeting the turbine's overall isentropic efficiency and power as optimization objectives, a rapid Non-dominated Sorting Genetic Algorithm II and the Technique for Order Preference by Similarity to an Ideal Solution are employed to optimize the last stage of the four-stage axial flow turbine. The optimized turbine demonstrates an increase in overall isentropic efficiency by 1.333% and an increase in overall power by 3.329%, while satisfying structural strength requirements. The novelty of this study lies in its rapid optimization design and performance prediction method for the coupled aerothermodynamics and structural mechanics at a one-dimensional level.

Ship hydrodynamic performance evaluation technique is one of the prerequisites and key conditions for ship-type optimization based on SBD technology, which provides an effective means to establish the mathematical model of ship design optimization problem. The accuracy of the hydrodynamic performance evaluation directly affects the quality of the optimization results [1]. In the optimization process, the optimization algorithm will adjust the next search direction based on the performance prediction results. Therefore, the reliability of the performance prediction results is the key to ensure that the search direction of the optimization algorithm in the design space is correct or not, and is also directly related to the success or failure of the optimization design.

The Fast-Steering secondary mirror (FSM) of Giant Magellan Telescope (GMT) consists of seven 1.1m diameter segments with effective diameter of 3.2m. Each of the seven segments is held by three axial supports and a central lateral support with a vacuum system to mirror gravity compensations. The mirror assembly has a tip-tilt feature capable of guiding to attenuate telescope wind shake and mount control jitter. Both on-axis and off-axis mirror segments were optimized under various design considerations using finite element analyses and optical performance analyses. An integrated model of the mirror cell assembly was developed including sub-assembly parts consisting of axial supports, lateral support, breakaway mechanism, seismic restraints, and pressure seal. In this paper for the optimized FSM mirror and mirror cell assembly, the design considerations are addressed, and performance prediction results are discussed in detail with respect to the specifications.

A computation scheme was implemented in a general-purpose, finite-difference simulator to model multi-lateral wells. Frictional pressure drop along the wellbore and proper fluid mixing at lateral connection points were included in the calculation of the wellbore pressure profile for each lateral. The formulation is general and a lateral can be branched out from any point along the main well path. Since the production profile of each lateral is influenced by the wellbore pressure distribution, the prediction accuracy depends on a good wellbore flow model. The Beggs and Brill(1) correlation and the homogeneous flow model were implemented, along with a new model proposed by Ouyang et al.(2) which included an acceleration term and accounted for the lubrication effect due to radial influx. Well performance prediction results using the three models were compared. The impacts of different tubing sizes on the well performance and the production contribution from each lateral were studied. Introduction Recently multi-lateral wells have been employed in off-shore platforms or remote and hard to access terrain areas to save costs comparing with drilling multiple vertical or horizontal wells. This work is to study the pressure and production profiles along each lateral using different pipe flow models. The vertical standoffs of laterals to GOC or WOC, the optimal length of laterals, the angles between laterals, the completion PI values used in simulation, and the optimal number of laterals are other important factors in designing multi-lateral wells. However, these issues will not be addressed here. Numerical Method The completion production rate and wellbore pressure profiles along each lateral are closely related. There is also interaction between laterals. The fluid dynamics calculation inside the wellbore can be described as follows: Pipeflow Calculation There are many steady-state correlations available to calculate frictional pressure losses. In this work, we implemented three models, namely, the Beggs and Brill model, the homogeneous model, and a modified homogeneous model (including radial inflow effect) in a general-purpose reservoir simulator. The homogeneous model gave a better pressure match for the Stanford/Marathon two-phase flow tests(3,4). It was a very simplified model. For two-phase flow, it used a mixing rule to calculate the density and viscosity values of the gas-liquid mixture. Then it used the mixture properties in the single-phase fluid-wall frictional loss formula. We did not use a transient pipeflow model(5) because it would require much longer computing time. Radial Inflow Effect Rigorously speaking, the regular pipe flow calculation is not suitable for open hole or perforation intervals due to the presence of radial inflow from the reservoir. For turbulent flow, the radial inflow provides a so called "lubrication effect" and reduces the frictional loss(6,7). For laminar flow (rarely occurring in oil field practice), it increases the frictional loss(8). Here we used a formula proposed by Ouyang et al.(3) to model radial inflow effect, Equation (1) (Available In Full Paper) where ƒo is the corrected frictional coefficient due to radial inflow and ƒ is the wall friction coefficient. For the open hole, Rew can be expressed as Equation (2) (Available In Full Paper)

The exponential growth of distributed artificial intelligence training workloads has created unprecedented challenges in network performance management and optimization. Traditional monitoring approaches fail to adequately address the dynamic and complex communication patterns inherent in distributed AI systems. This paper presents a comprehensive machine learning-based framework for network performance monitoring and prediction specifically designed for distributed AI training environments. Our approach leverages advanced feature engineering techniques to capture multi-dimensional network performance metrics, including latency variations, bandwidth utilization patterns, and communication bottlenecks. We develop and evaluate multiple machine learning models, including gradient boosting machines, neural networks, and ensemble methods, to predict network performance degradation before it impacts training efficiency. Experimental evaluation on real-world distributed training scenarios demonstrates that our framework achieves 94.7% prediction accuracy for network latency spikes and reduces training time by 23.4% through proactive performance optimization. The proposed monitoring architecture provides real-time insights into network behavior patterns, enabling adaptive resource allocation and communication scheduling optimization. Our contributions include a novel feature extraction methodology, a comprehensive performance prediction model, and a scalable monitoring framework that can be deployed across heterogeneous distributed computing environments.

In this chapter, a cycle approximate simulator for multi-processor system-on chip is presented. The aim of this simulation tool is to enable an enhanced software/hardware analysis capability for bus based systems. The most important contributions are represented by its high flexibility (easy configuration of a SoC using dedicated libraries for generic and specific components and easy integration of other simulators and models), accurate modeling of features specific to multiprocessor systems (busses, inter processor communication mechanisms, etc), accurate implementation of a wide range of performance metrics and power consumption estimates (for processors that support this) and high simulation speed. This way, the proposed simulator can be used for both hardware architecture design exploration and software development.

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