Parallel Simulation Framework Research Articles

Many-objective optimization for overall performance of an electric sport utility vehicle under multiple temperature conditions based on natural gradient boosting model

In this study, significant efforts have been devoted to optimizing energy consumption and driving range of an electric sport utility vehicle (ESUV). Vehicle energy flow tests were conducted at different temperatures, and integrated simulation models were built and validated. Subsequently, Natural Gradient Boosting (NGBoost) model was constructed based on extensive datasets obtained by parallel simulation framework, and many-objective optimizations of ESUV design parameters were performed. The results show that energy consumption per 100 km under high and low temperatures increases by 19.3 % and 34.9 % compared to room temperature, while effective work of half-axis decreases by 12.9 % and 28 %, suggesting extreme environmental temperature significantly affects ESUV performance. Mean square errors (MSEs) of NGBoost model predictions are less than 0.12, indicating its reliable predictive capability for unknown data. Many-Objective Random Walk Grey Wolf Optimizer (MORW-GWO) algorithm achieves optimal or suboptimal results on 10 test problems, fully demonstrating its efficient convergence and excellent robustness. Battery recovered energy, effective work of half-axis and electricity consumption per 100 km of final optimization scheme are increased by 20.2 %, 9.0 % and 13.0 %, with the largest overall percentage improvement of 17.3 %. These findings can provide data support and directional guidance for optimization designs and performance improvements of ESUVs.

A universal parallel simulation framework for energy pipeline networks on high-performance computers

Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework’s performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high-performance computers.

A Novel Decoupled EMT Approach and Parallel Simulation Framework for Modularized Solid-State Transformers

A Novel Decoupled EMT Approach and Parallel Simulation Framework for Modularized Solid-State Transformers

Adaptive Multidimensional Parallel Fault Simulation Framework on Heterogeneous System

Adaptive Multidimensional Parallel Fault Simulation Framework on Heterogeneous System

Fast CO2 saturation simulations on large-scale geomodels with artificial intelligence-based Wavelet Neural Operators

Carbon capture and storage (CCS) projects rely on numerical reservoir simulations for determining where and how much CO2 can safely be stored underground. Conventional simulations are computationally expensive and time consuming, thereby increasing the turn-around time of decision-making processes and projects. Recent research on artificial intelligence (AI) based approaches for solving the underlying two-phase flow equations has shown enormous promise, as the computational burden can be shifted to the training time, while simulations can be performed in fractions of a second at test time. Applications of AI methods for simulating CO2 flow and other differential equations have so far been limited to 2D or small 3D problems with relatively simple geometries. We introduce a cloud-native framework for parallel simulations of large-scale training data sets using the Open Porous Media (OPM) simulator and we propose a new AI architecture based on Fourier Neural Operators (FNOs) with 3D wavelet transforms. This architecture performs better than FNOs on data with shock fronts, such as simulating CO2 saturation. We also provide a new training dataset for CO2 flow based on the Sleipner CO2 storage geomodel from the Norwegian continental shelf and show that both FNOs and Wavelet Neural Operators (WNOs) can be trained on models with over 200,000 cells and lead to significant speed-ups of up to 50,000× (FNOs) and 100× (Wavelet NOs) over numerical simulations of CO2 saturation with OPM.

Recommending Strategic Air Traffic Management Initiatives in Convective Weather

The presence of uncertainty in weather forecasts poses significant challenges for air traffic managers. These challenges can have major repercussions on stakeholders in terms of their impact on the delay within the system. In this paper, we discuss an approach for recommending traffic management initiative (TMI) parameters during uncertain weather conditions. We propose four methods for TMI selection. The first two favor random exploration of TMI decisions. An epsilon-greedy approach and a softmax algorithm are also evaluated against the two random exploration approaches. A parallel fast-time simulation framework is presented for evaluating the proposed methods over a range of weather forecast scenarios. A set of regional TMIs is applied and tested against a set of case days in which the airspace capacity in the Northeast United States was compromised by convective weather. Both the softmax and epsilon-greedy approaches demonstrate strong performance relative to the other methods. The results suggest that the approach could potentially aid air traffic stakeholders in understanding how to best deal with weather forecast uncertainty.

Graph optimization algorithm using symmetry and host bias for low-latency indirect network

It is known that an indirect network with a small host-to-host Average Shortest Path Length (h-ASPL) improves overall system performance in a parallel computer system. As a means to discuss such indirect networks in graph theory, the Order/Radix Problem (ORP) has been proposed. ORP involves finding a graph with a minimum h-ASPL that satisfies a given number of hosts and radix. A graph in ORP represents an indirect network and has two types of vertices: host and switch. We propose an optimization algorithm to generate graphs with a sufficiently small h-ASPL. The primary features of the proposed algorithm are the symmetry of the graph and the bias of the hosts adjacent to each switch. These features reduce the computational time to calculate the h-ASPL and improve the search performance of the algorithm. The performance of the proposed algorithm is evaluated using problems presented by Graph Golf, an international ORP competition. Our results show that the proposed algorithm can generate graphs with a smaller h-ASPL than the existing algorithm. To evaluate the performance of the graphs generated by the proposed algorithm, we use the parallel simulation framework SimGrid and the parallel benchmark collection NAS Parallel Benchmarks. Our results also show that the graphs generated by the proposed algorithm have higher performance than those generated by the existing algorithm.

QuOp_MPI: A framework for parallel simulation of quantum variational algorithms

QuOp_MPI is a Python package designed for parallel simulation of quantum variational algorithms. It presents an object-orientated approach to quantum variational algorithm design and utilises MPI-parallelised sparse-matrix exponentiation, the fast Fourier transform and parallel gradient evaluation to achieve the highly efficient simulation of the fundamental unitary dynamics on massively parallel systems. In this article, we introduce QuOp_MPI and explore its application to the simulation of quantum algorithms designed to solve combinatorial optimisation algorithms, including the Quantum Approximation Optimisation Algorithm, the Quantum Alternating Operator Ansatz, and the Quantum Walk-assisted Optimisation Algorithm.

A Scalable Quantum Key Distribution Network Testbed Using Parallel Discrete-Event Simulation

Quantum key distribution (QKD) has been promoted as a means for secure communications. Although QKD has been widely implemented in many urban fiber networks, the large-scale deployment of QKD remains challenging. Today, researchers extensively conduct simulation-based evaluations for their designs and applications of large-scale QKD networks for cost efficiency. However, the existing discrete-event simulators offer models for QKD hardware and protocols based on sequential event execution, which limits the scale of the experiments. In this work, we explore parallel simulation of QKD networks to address this issue. Our contributions lay in the exploration of QKD network characteristics to be leveraged for parallel simulation as well as the development of a parallel simulation framework for QKD networks. We also investigate three techniques to improve the simulation performance including (1) a ladder queue based event list, (2) memoization for computationally intensive quantum state transformation information, and (3) optimization of the network partition scheme for workload balance. The experimental results show that our parallel simulator is 10 times faster than a sequential simulator when simulating a 128-node QKD network. Our linear-regression-based network partition scheme can further accelerate the simulation experiments up to two times over using a randomized network partition scheme.

Parallel simulation and optimization framework of supplies production processes for unconventional emergencies.

The outbreak of unconventional emergencies leads to a surge in demand for emergency supplies. How to effectively arrange emergency production processes and improve production efficiency is significant. The emergency manufacturing systems are typically complex systems, which are difficult to be analyzed by using physical experiments. Based on the theory of Random Service System (RSS) and Parallel Emergency Management System (PeMS), a parallel simulation and optimization framework of production processes for surging demand of emergency supplies is constructed. Under this novel framework, an artificial system model paralleling with the real scenarios is established and optimized by the parallel implementation processes. Furthermore, a concrete example of mask shortage, which occurred at Huoshenshan Hospital in the COVID-19 pandemic, verifies the feasibility of this method.

Distributed and Parallel simulation methods for pest control and crop monitoring with IoT assistance

ABSTRACT In today's world, Agriculture moves toward technological advancements termed as Modern Agriculture. The usage of multiple pest control and crop management frameworks plays a significant role in crop monitoring. The existing framework faces challenges. It uses a single core Graphical Processing Unit (GPU) to handle the various sensors attached for pest control and crop monitoring system. Therefore, Distributed and parallel simulation Framework (DPSF) with the Internet of Things (IoT) Assistance is proposed for pest control and crop monitoring system. It reduces the stress on a single GPU and shares the process equally and simultaneously to the available GPUs and views data on the dashboard without crashing. The Technique will reduce the execution time. DPSF uses multi-threading concept, in which each core in GPU shares tasks to other cores. This process is done in four layers; each layer is shared to handle the specific task– Crop management, Pest detection and control, Output functions and input functions. The data is processed simultaneously and managed in an optimised and controlled fashion. Simulation analysis shows that the DPSF with IoT Assistance can classify and control pest effectively with IoT assistance.

QSW_MPI: A framework for parallel simulation of quantum stochastic walks

QSW_MPI is a Python package developed for time-series simulation of continuous-time quantum stochastic walks. This model allows for the study of Markovian open quantum systems in the Lindblad formalism, including a generalisation of the continuous-time random walk and continuous-time quantum walk. Consisting of a Python interface accessing parallelised Fortran libraries utilising sparse data structures, QSW_MPI is scalable to massively parallel computers, which makes possible the simulation of a wide range of walk dynamics on directed and undirected graphs of arbitrary complexity. Program summaryProgram Title: QSW_MPICPC Library link to program files:http://dx.doi.org/10.17632/r8jsx4xxgv.1Licensing provisions: GPLv3Programming language: Python 3 + Fortran 2003External routines/libraries: NumPy [1], SciPy [2], MPI for Python [3], h5py[4]Nature of problem: QSW_MPI provides a framework for the simulation of quantum stochastic walks on arbitrary graphs (directed/undirected, weighted/unweighted).Solution method: A parallel distributed-memory implementation of the matrix exponential via a truncated Taylor series expansion with scaling and squaring [5].Additional comments including restrictions and unusual features: QSW_MPI will provide support for the simulation of multiple quantum walkers in a future version.

A generic modeling and development approach for WECC composite load model

The composite load model (CLM) proposed by the Western Electricity Coordinating Council (WECC) is gaining increasing traction in industry, particularly in North America. At the same time, it has been recognized that further improvements in structure, initialization and aggregation methods are needed to enhance model accuracy and flexibility. However, the lack of a generic modeling and open-source implementation of the WECC CLM has become a roadblock for many researchers for further improvement. To bridge this gap, this paper presents a generic CLM modeling approach and the first open reference implementation of the WECC CLM. Individual load components and the CLM are first developed and tested in MATLAB, then translated to the high-performance computing (HPC) based, parallel simulation framework – GridPACK™. The main contributions of the paper include: (1) presenting important yet undocumented details of modeling and initializing the CLM, particularly for a parallel simulation framework like GridPACK™; (2) implementation details of the load components such as the single-phase air conditioner motor; (3) an improved initialization method; (4) implementing the CLM in a modular and extensible manner. The implementation has been tested at both the component and system levels, and benchmarked against commercial simulation programs with satisfactory accuracy.

Simulating large vCDN networks: A parallel approach

Virtualization and cloud computing are being used by Communication Service Providers to deploy and utilize virtual Content Distribution Networks (vCDNs) to reduce costs and increase elasticity thereby avoiding performance, quality, reliability and availability limitations that characterize traditional CDNs. As cache placement is based on both the content type and geographic location of a user request, it has a significant impact on service delivery and network congestion. To study the effectiveness of cache placements and hierarchical network architectures composed of sites, a novel parallel simulation framework is proposed utilizing a discrete-time approach. Unlike other simulation approaches, the proposed simulation framework can update, in parallel, the state of sites and their resource utilization with respect to incoming requests in a significantly faster manner at hyperscale. It allows for simulations with multiple types of content, different virtual machine distributions, probabilistic caching, and forwarding of requests. In addition, power consumption models allow the estimation of energy consumption of the physical resources that host virtual machines. The results of simulations conducted to assess the performance and applicability of the proposed simulation framework are presented. Results are promising for the potential of this simulation framework in the study of vCDNs and optimization of network infrastructure.

Epsim: A Scalable and Parallel Marssx86 Simulator With Exploiting Epoch-Based Execution

In general, a detailed modeling and evaluation of computer architectures make a cycle-accurate simulator necessary. As the architectures become increasingly complex for parallel, cloud, and neural computing, nowadays, the complexity of the simulator grows rapidly, and thus its execution is too slow or infeasible for practical use. In order to alleviate the problem, many previous studies have focused on reducing the simulation time in a variety of ways such as using sampling methods, adding hardware accelerators, and so on. In this paper, we propose a new parallel simulation framework, called Epoch-based Parallel SIMulator, to obtain scalable speedup with large number of cores. The framework is based on a well-known cycle-accurate full-system simulator, MARSSx86. From the simulator, we build an epoch, that is an execution interval, where the architectural simulation by PTLSim does not involve any interaction with QEMU. Therefore, we can simulate epochs independently, i.e., execute multiple epochs completely in parallel by PTLSim with their live-in data. Our performance evaluation shows that we achieve $12.8\times $ speed on average with 16-core parallel simulation from the SPEC CPU2006 benchmarks and the PARSEC benchmarks, providing the performance scalability.

Parallel computation of aeroacoustics of industrially relevant complex-geometry aeroengine jets

Jet noise is still a distinct noise component when a commercial aircraft is taking off. A parallel high-fidelity simulation framework for industrial jet noise prediction is presented in this paper. This framework includes complex geometry meshing and Ffowcs Williams-Hawkings (FW-H) surface placement during preprocessing, a parallel hybrid RANS-LES flow solver coupled with an FW-H acoustic solver in the simulation and mean and unsteady data processing after the simulation. The use of this framework is demonstrated through two jet noise prediction cases: in-flight heated jets and installed ultra-high bypass-ratio (UHBPR) engines. These simulations can provide more insight than experimental tests into jet flow physics for engineering model improvement. Additional advantages are also shown in the cost and turn-around time. Thus there is great potential for high-fidelity jet noise simulations to partly replace rig tests for industrial use in the future.

Development of a framework for parallel reservoir simulation

The development of a parallel reservoir simulator is a more complex task than nonparallel simulators. Problems related to parallel implementation such as parallel communication, model division among processors, and the management of data distributed among processors, and other issues should be addressed and solved in addition to the already complex task of reservoir simulator development. Hence, the development of parallel reservoir simulators is more time intensive than the traditional development on single processor computers. In this work, a framework was developed to aid the development of parallel reservoir simulators. The framework developed in this work implements and handles the parallel processing complexity and provides easy-to-use programming interfaces to accelerate the development of new parallel reservoir simulators or the parallelization of existing ones. Additionally, The University of Texas Compositional Simulator was used in conjunction with the framework developed in this work to create a parallel reservoir simulator. Case studies are used to verify accuracy and to test parallel performance of our new parallel reservoir simulator.

Development of a framework for parallel reservoir simulation

The development of a parallel reservoir simulator is a more complex task than nonparallel simulators. Problems related to parallel implementation such as parallel communication, model division amon...

A realistic, accurate and fast source modeling approach for the EEG forward problem

The aim of this paper is to advance electroencephalography (EEG) source analysis using finite element method (FEM) head volume conductor models that go beyond the standard three compartment (skin, skull, brain) approach and take brain tissue inhomogeneity (gray and white matter and cerebrospinal fluid) into account. The new approach should enable accurate EEG forward modeling in the thin human cortical structures and, more specifically, in the especially thin cortices in children brain research or in pathological applications. The source model should thus be focal enough to be usable in the thin cortices, but should on the other side be more realistic than the current standard mathematical point dipole. Furthermore, it should be numerically accurate and computationally fast. We propose to achieve the best balance between these demands with a current preserving (divergence conforming) dipolar source model. We develop and investigate a varying number of current preserving source basis elements n (n=1,…,n=5). For validation, we conducted numerical experiments within a multi-layered spherical domain, where an analytical solution exists. We show that the accuracy increases along with the number of basis elements, while focality decreases. The results suggest that the best balance between accuracy and focality in thin cortices is achieved with n=4 (or in extreme cases even n=3) basis functions, while in thicker cortices n=5 is recommended to obtain the highest accuracy. We also compare the current preserving approach to two further FEM source modeling techniques, namely partial integration and St. Venant, and show that the best current preserving source model outperforms the competing methods with regard to overall balance. For all tested approaches, FEM transfer matrices enable high computational speed. We implemented the new EEG forward modeling approaches into the open source duneuro library for forward modeling in bioelectromagnetism to enable its broader use by the brain research community. This library is build upon the DUNE framework for parallel finite elements simulations and integrates with high-level toolboxes like FieldTrip. Additionally, an inversion test has been implemented using the realistic head model to demonstrate and compare the differences between the aforementioned source models.

A multi‐GPU finite element computation and hybrid collision handling process framework for brain deformation simulation

AbstractThis paper offers a fast multi‐graphics processing unit (GPU) parallel simulation framework to the problem of real‐time and nonlinear finite element computation of brain deformation. A load balancing strategy is proposed to ensure the efficient distribution of nonlinear finite element computation on multi‐GPU. A data storage structure is designed to minimize the amount of data transfer and make full use of the overlay technique of GPU to reduce the transferring latency between multi‐GPUs. We further present a fast central processing unit (CPU)–GPU parallel continuous collision detection and response method, which not only can deal with the collision between the brain and skull but also can handle the self‐collision of the brain. Our method can make full use of CPU and GPU to implement a parallel computation about deformation and collision detection. Our experimental results show that our method is able to handle a brain geometric model with high detail gyrus composed of more than 40,000 tetrahedron elements. This can facilitate the fidelity of the current virtual brain surgery simulator. We evaluate our approach qualitatively and quantitatively and compare it with related works.