Parallel Computing Research Articles

An efficient 3D tube-level flux-thermal model of external tubular receivers in solar power tower systems

Accurate and efficient radiative flux simulation and thermal analysis of liquid tubular receivers are essential for safe and stable operations of solar power tower systems. However, the complicated structure and photothermal processes on these receivers pose significant challenges for simulation. Although existing models have made great simplifications, the simulation results are still inefficient and inaccurate. To address these issues, a novel and universal 3D flux-thermal model is proposed. Firstly, a flux model is proposed via a tube-level Monte Carlo ray tracing method based on GPU parallel computing, enabling real-time and accurate radiative flux density distribution simulation for each absorber tube. Secondly, a high-resolution multi-tube thermal model is proposed, providing accurate 3D thermal analysis for each absorber tube. Through pre-factorization and highly parallelized designs, the proposed thermal model achieves a 600 times acceleration in efficiency, even with tens of millions of discrete elements. Finally, the novel flux-thermal model is validated against publicly published measured data from Solar Two, indicating a mean deviation of only 0.1% and a root mean square error of 0.006, demonstrating its high accuracy. Furthermore, the proposed flux-thermal model reveals complex and asymmetrical flux and temperature distributions on the absorber tube, in contrast to the symmetrical distributions obtained by the simplified models. Such inaccuracies in the simplified simulations may cause operational misjudgements and affect system safety potentially. Therefore, the proposed 3D tube-level flux-thermal model provides an accurate and efficient solution to radiative flux simulation and thermal analysis, enhancing the safe and stable operation of solar power tower systems.

The Fogees system for forecasting particulate matter concentrations in urban areas

Air quality forecasting requires appropriate models and data sources. The Fogees system presented in this paper enables mapping, evaluation and forecasting of the level of PMx pollution in the air, for urban and suburban areas. Input data were downloaded from the Meteoblue service, the GIS database and – in the case of integration with existing measurement systems – also from local air quality monitoring stations. The system uses a diagnostic model to determine the velocity field and a Lagrange model to describe pollution dispersion. The system has an autocalibration model and uses proprietary algorithms to estimate emissions from domestic heating, transport and background pollution levels. Modular design and parallel computation facilitate simultaneous calculation of forecasts for existing emission conditions and calculations for alternative emission conditions. The system permits forecasting for the next hour and for several consecutive hours. The validation results confirm that the system reliable forecasting of PM concentrations.

Accelerating Fortran codes: A method for integrating Coarray Fortran with CUDA Fortran and OpenMP

Fortran's prominence in scientific computing requires strategies to ensure both that legacy codes are efficient on high-performance computing systems, and that the language remains attractive for the development of new high-performance codes. Coarray Fortran (CAF), part of the Fortran 2008 standard introduced for parallel programming, facilitates distributed memory parallelism with a syntax familiar to Fortran programmers, simplifying the transition from single-processor to multi-processor coding. This research focuses on innovating and refining a parallel programming methodology that fuses the strengths of Intel Coarray Fortran, Nvidia CUDA Fortran, and OpenMP for distributed memory parallelism, high-speed GPU acceleration and shared memory parallelism respectively. We consider the management of pageable and pinned memory, CPU-GPU affinity in NUMA multiprocessors, and robust compiler interfacing with speed optimisation. We demonstrate our method through its application to a parallelised Poisson solver and compare the methodology, implementation, and scaling performance to that of the Message Passing Interface (MPI), finding CAF offers similar speeds with easier implementation. For new codes, this approach offers a faster route to optimised parallel computing. For legacy codes, it eases the transition to parallel computing, allowing their transformation into scalable, high-performance computing applications without the need for extensive re-design or additional syntax.

FLARE: field line analysis and reconstruction for 3D boundary plasma modeling

Abstract The FLARE code is a magnetic mesh generator that is integrated within a suite of tools for the analysis of the magnetic geometry in toroidal fusion devices. A magnetic mesh is constructed from field line segments and permits fast reconstruction of field lines in 3D boundary plasma codes such as EMC3-EIRENE. Both intrinsically non-axisymmetric configurations (stellarators) and those with symmetry breaking perturbations of an axisymmetric equilibrium (tokamaks) are supported. The code itself is written in Modern Fortran with MPI support for parallel computing, and it incorporates object-oriented programming for the definition of the magnetic field and the material surface geometry. Extended derived types for a number of different magnetohydrodynamic equilibrium and plasma response models are implemented. The core element of FLARE is a field line tracer with adaptive step-size control, and this is integrated into tools for the construction of Poincaré maps and invariant manifolds of X-points. A collection of high-level procedures that generate output files for visualization is build on top of that. The analysis modules are build with Python frontends that facilitate customization of tasks and/or scripting of parameter scans.

Impacts of Big Data Analytics for Agriculture tools and techniques

The present paper deals with “Impacts of Big Data Analytics for Agriculture tools and techniques.” A random sample 30 Various software platforms are developed to give information to farmers about new tools and techniques related to agriculture: (MySmartFarm, Awhere weather, Phenone, Farmlog, Datafloq, Farmeron) has been used by secondary source from Agriculture depart in the state of Bihar. The traditional tools and techniques (Big data) analysis massive amount of data. To store and analyze this type of data parallel computing and analyze paradigm is required. Big data analytic is used to weather changes and its impacts of agriculture in Bihar. From the big data analytic Agriculture framework is developed that identify disease based on symptoms similarity and recommend a solution based on high similarity and achieve their tools has been used. Then cleansing of data is done that is important information is extracted from unstructured redundant data and were normalization is done that is features are extracted from cleaned data. The data was used to analyze the agricultural tools and technique. It finds out disease name based on weather changes and its impacts of agriculture has been taken on past data. Result has been shows that data analytic in weather changes past 2018-2023 years that was.

A particle swarm optimization inspired global and local stability driven predictive load balancing strategy

In distributed systems and parallel computing, optimal load balancing is difficult. These abstract addresses load balancing in distributed situations, highlighting current solutions' flaws and emphasizing the need for new ones. Load balancing research includes centralized and distributed algorithms, heuristics, and predictive models. Despite various successful methods, workload adaptability, overhead reduction, and scaling to large systems remain unresolved. This study proposes a particle swarm optimization (PSO) load balancing method that considers global and local stability considerations. The proposed method uses PSO principles to balance exploration and exploitation and allocate resources among distributed nodes. Predictive components improve preventative load management by predicting workload changes. Global and local load balancing stability criteria distinguish this study. The recommended method considers global system-wide performance indicators, local node-level characteristics, and micro-level stability to maximize system efficiency. A dual-focus technique distinguishes the proposed load balancing strategy from others, solving dynamic distributed system challenges. The study examines load balancing system advances and suggests improvements and further research. More accurate prediction modeling, stability measures, and application-specific enhancements may be studied in the future. Experimental validation and real-world implementation of the recommended approach are necessary to determine its practicality and ability to handle modern distributed computing systems.

Aerodynamic configuration of a wide-range reversible vehicle

The design of wide-range high-efficiency aerodynamic configurations is one of the most important key technologies in the research of near-space hypersonic vehicles. A double-sided intake configuration with different inlets on the upper and lower surfaces is proposed to adapt to wide-range flight. Firstly, the double-sided intake configuration’s design method and flight profile are delineated. Secondly, Computational Fluid Dynamics (CFD) numerical simulation based on multi-Graphics Processing Unit (GPU) parallel computing is adopted to evaluate the vehicle’s performance comprehensively, aiming to verify the feasibility of the proposed scheme. This evaluation encompasses a wide-range basic aerodynamic characteristics, inlet performance, and heat flux at critical locations. The results show that the inlets of the designed integration configuration can start up across Mach number 3.5 to 8. The vehicle possesses multi-point cruising capability by flipping the fuselage. Simultaneously, a 180°rotation of the fuselage can significantly decrease the heat accumulation on the lower surface of the vehicle, particularly at the inlet lip, further decreasing the temperature gradient across the vehicle structure. This study has some engineering value for the aerodynamic configuration design of wide-range vehicles. However, further study reveals that the flow phenomena at the intersection of two inlets are complex, posing potential adverse impacts on propulsion efficiency. Therefore, it is imperative to conduct additional research to delve into this matter comprehensively.

A general framework for metaverse based on parallel computing and HPC

As virtual and actual universes merge inside the creating metaverse, requests have pointedly ascended for continuous, intuitive, and intense encounters. The ability of the metaverse to effectively analyze and render complicated links and information supplied by clients is critical for realizing that goal. These demanding computational demands are starting to be supported by parallel processing, and high-performance computing (HPC) is beyond uncertainty key to this domain. The integrative framework presented in this paper addresses the core challenges of inertness, flexibility, and ease of use while integrating equal registration into the metaverse. The system enables prompt handling of client actions and quick response times by distributing calculations over multiple processors, which is essential for the seamless client experience. It also manages the vast amount of metaverse material and interactions as well as the various data processing needs. The paper looks at intrinsic equal processing difficulties in this unique climate, including creating versatile and energy-effective equal calculations that consider load adjusting and asset designation. It features the need to democratize equal figuring assets to produce metaverse extension while accentuating the significance of information protection and security conventions in multi-client settings. The cooperative energy between metaverse development and equal registering progressions vows to push limits, empowering remarkable degrees of virtual submersion and collaboration.

Parallel Optimization of Message Passing Interface for 3D Sound Propagation Model BELLHOP3D

Abstract BELLHOP3D is a widely used three-dimensional computational model for acoustic wave propagation in the ocean based on the ray method. Although its computational efficiency is relatively high, there is still significant scope for improvement. This paper presents an optimized version of BELLHOP3D that utilizes the message passing interface (MPI) to parallelize the calculation of azimuth and pitch angles. The parallel algorithm developed in this study exhibits high efficiency and produces stable and reliable results. The current work introduces both the serial and parallel algorithms of BELLHOP3D, and subsequently validates the accuracy of the parallel program via numerical simulations and also evaluates the parallel computing performance. The computation results demonstrate that parallel calculation of both azimuth and pitch angles through the use of the message transfer interface considerably enhances the parallel efficiency of BELLHOP3D.

Grapher: A Reconfigurable Graph Computing Accelerator with Optimized Processing Elements

In recent years, various graph computing architectures have been proposed to process graph data that represent complex dependencies between different objects in the world. The designs of the processing element (PE) in traditional graph computing accelerators are often optimized for specific graph algorithms or tasks, which limits their flexibility in processing different types of graph algorithms, or the parallel configuration that can be supported by their PE arrays is inefficient. To achieve both flexibility and efficiency, this paper proposes Grapher, a reconfigurable graph computing accelerator based on an optimized PE array, efficiently supporting multiple graph algorithms, enhancing parallel computation, and improving adaptability and system performance through dynamic hardware resource configuration. To verify the performance of Grapher, this paper selected six datasets from the Stanford Network Analysis Project (SNAP) database for testing. Compared with the existing typical graph frameworks Ligra, Gemini, and GraphBIG, the processing time for the six datasets using the BFS, CC, and PR algorithms was reduced by up to 39.31%, 35.43%, and 27.67%, respectively. The energy efficiency has also been improved by 1.8× compared to Hitgraph and 4.7× compared to ThunderGP.

Consideration of Thermal Comfort, Daylighting Comfort, and Life-Cycle Decarbonization in the Retrofit of Kindergarten Buildings in China: A Case Study

Kindergartens play a crucial role in nurturing the physical, cognitive, and social development of children. Hence, designing kindergarten buildings requires the consideration of the unique requirements and behavior of children. Considering the rapid urbanization of China and its commitment to achieving the 3060 carbon goal, in this study, we examine the retrofitting of kindergarten buildings in China and propose a retrofit optimization method for kindergarten buildings that considers thermal comfort, daylighting, and life-cycle carbon emissions. Through this method, information on the thermal and daylighting comfort of occupants, weather data, occupant scheduling, and envelope and energy system of the kindergarten building to be retrofitted can be obtained through various approaches, such as video playback, field investigation, literature research, and consult drawings. On this basis, optimization variables are selected, and a physical model is established to guide the retrofit process. Afterward, a rapid comprehensive optimization framework based on parallel computing is adopted to obtain the comprehensive optimal design scheme for the building to be retrofitted. The proposed method is applied to a kindergarten building retrofit case in Nanjing, China, and the results show that the optimal comprehensive scheme results in a reduction in carbon emissions of 34,158.3 kg, an increase in the thermal comfort period of 2.7%, and an improvement in daylighting comfort of 79.7% over the benchmark scheme. The significance of this study extends beyond its potential for widespread application in kindergarten building retrofits. It contributes to advancing sustainable building design and environmental stewardship, creating healthier and more comfortable learning environments for children while mitigating the environmental impact of buildings. Moreover, it emphasizes the importance of considering children’s unique needs and behaviors in building design, ultimately leading to better outcomes for their overall development.

Fast power flow calculation for distribution networks based on graph models and hierarchical forward-backward sweep parallel algorithm

IntroductionIn response to the issues of complexity and low efficiency in line loss calculations for actual distribution networks, this paper proposes a fast power flow calculation method for distribution networks based on Neo4j graph models and a hierarchical forward-backward sweep parallel algorithm.MethodsFirstly, Neo4j is used to describe the distribution network structure as a simple graph model composed of nodes and edges. Secondly, a hierarchical forward-backward sweep method is adopted to perform power flow calculations on the graph model network. Finally, during the computation of distribution network subgraphs, the method is combined with the Bulk Synchronous Parallel (BSP) computing model to quickly complete the line loss analysis.Results and DiscussionResults from the IEEE 33-node test system demonstrate that the proposed method can calculate network losses quickly and accurately, with a computation time of only 0.175s, which is lower than the MySQL and Neo4j graph methods that do not consider hierarchical parallel computing.

Efficient Commutative PQC Algorithms on Isogenies of Edwards Curves

The article presents the author’s works in the field of modifications and modeling of the Post-Quantum Cryptography (PQC) Commutative Supersingular Isogeny Diffie-Hellman (CSIDH) algorithm on non-cyclic supersingular Edwards curves and its predecessor Couveignes-Rostovtsev-Stolbunov (CRS) scheme on ordinary non-cyclic Edwards curves are reviewed. Lower estimates of the computational speed gains of the modified algorithms over the original ones are obtained. The most significant results were obtained by choosing classes of non-cyclic Edwards curves connected as quadratic twist pairs instead of cyclic complete Edwards curves, as well as the method of algorithm randomization as an alternative to “constant time CSIDH”. It is shown that in the CSIDH and Commutative Supersingular Isogeny Key Encapsulation (CSIKE) algorithms, there are two independent cryptosystems with the possibility of parallel computation, eliminating the threat of side-channel attacks. There are four such cryptosystems for the CRS scheme. Integral lower bound estimates of the performance gain of the modified CSIDH algorithm are obtained at 1.5 × 29, and for the CRS scheme are 3 × 29.

A deep-level decomposed model to accelerate hydraulic simulations in large water distribution networks

As the size of water distribution network (WDN) models continues to grow, developing and applying real-time models or digital twins to simulate hydraulic behaviors in large-scale WDNs is becoming increasingly challenging. The long response time incurred when performing multiple hydraulic simulations in large-scale WDNs can no longer meet the current requirements for the efficient and real-time application of WDN models. To address this issue, there is a rising interest in accelerating hydraulic calculations in WDN models by integrating new model structures with abundant computational resources and mature parallel computing frameworks. This paper presents a novel and efficient framework for steady-state hydraulic calculations, comprising a joint topology-calculation decomposition method that decomposes the hydraulic calculation process and a high-performance decomposed gradient algorithm that integrates with parallel computation. Tests in four WDNs of different sizes with 8 to 85,118 nodes demonstrate that the framework maintains high calculation accuracy consistent with EPANET and can reduce calculation time by up to 51.93 % compared to EPANET in the largest WDN model. Further investigation found that factors affecting the acceleration include the decomposition level, consistency of sub-model sizes and sub-model structures. The framework aims to help develop rapid-responding models for large-scale WDNs and improve their efficiency in integrating multiple application algorithms, thereby supporting the water supply industry in achieving more adaptive and intelligent management of large-scale WDNs.

DC-Net: Divide-and-conquer for salient object detection

In this paper, to guide the model’s training process to explicitly present a progressive trend, we first introduce the concept of Divide-and-Conquer into Salient Object Detection (SOD) tasks, called DC-Net. Our DC-Net guides multiple encoders to solve different subtasks and then aggregates the feature maps with different semantic information obtained by multiple encoders into the decoder to predict the final saliency map. The decoder of DC-Net consists of newly designed two-level Residual nested-ASPP (ResASPP2) modules, which improve the sparse receptive field existing in ASPP and the disadvantage that the U-shape structure needs downsampling to obtain a large receptive field. Based on the advantage of Divide-and-Conquer’s parallel computing, we parallelize DC-Net through reparameterization, achieving competitive performance on five LR-SOD and five HR-SOD datasets under high efficiency (60 FPS and 55 FPS). Codes and results are available: https://github.com/PiggyJerry/DC-Net.

GPU-accelerated estimation of heat transfer coefficients in continuous casting under large interference by a novel multiagent-based dimensional learning Jaya algorithm

This paper addresses the challenge of estimating heat transfer coefficients (HTCs) through the measured surface temperatures under large interference in the secondary cooling zone (SCZ) of continuous casting processes. Conventional methods for calculating HTCs, typically reliant on surface temperature data, face significant challenges in achieving accuracy. These challenges primarily stem from the limitations inherent in traditional methodologies and the distortions caused by temperature outliers, which are a consequence of large interference. To address these issues more effectively, we introduced a novel algorithm–multi-agent and dimensional learning based Jaya algorithm (MADL-Jaya)–specifically designed to estimate HTCs. Furthermore, a hybrid method that integrates weighted least squares (WLS) with the MADL-Jaya was proposed to mitigate the impact of outliers. Additionally, estimating HTCs entails substantial computational demands. Consequently, a double-deck parallel algorithm employing graphics processing unit (GPU) acceleration was presented. This architecture facilitates both parallel heat transfer models and parallel MADL-Jaya calculations, thus significantly reducing the computational time required for HTC estimation. The experimental results unequivocally affirm the efficacy of the proposed algorithm in the accurate estimation of HTCs within the SCZ of continuous casting, effectively neutralizing the detrimental influence of outlier values while markedly improving computational speed.

A Hybrid Parallel Computing Architecture Based on CNN and Transformer for Music Genre Classification

Music genre classification (MGC) is the basis for the efficient organization, retrieval, and recommendation of music resources, so it has important research value. Convolutional neural networks (CNNs) have been widely used in MGC and achieved excellent results. However, CNNs cannot model global features well due to the influence of the local receptive field; these global features are crucial for classifying music signals with temporal properties. Transformers can capture long-range dependencies within an image thanks to adopting the self-attention mechanism. Nevertheless, there are still performance and computational cost gaps between Transformers and existing CNNs. In this paper, we propose a hybrid architecture (CNN-TE) based on CNN and Transformer encoder for MGC. Specifically, we convert the audio signals into mel spectrograms and feed them into a hybrid model for training. Our model employs a CNN to initially capture low-level and localized features from the spectrogram. Subsequently, these features are processed by a Transformer encoder, which models them globally to extract high-level and abstract semantic information. This refined information is then classified using a multi-layer perceptron. Our experiments demonstrate that this approach surpasses many existing CNN architectures when tested on the GTZAN and FMA datasets. Notably, it achieves these results with fewer parameters and a faster inference speed.

Improving the precision of forces in real-space pseudopotential density functional theory.

The high-order finite difference real-space pseudopotential density functional theory (DFT) approach is a valuable method for large-scale, massively parallel DFT calculations. A significant challenge in the approach is the oscillating "egg-box" error introduced by aliasing associated with a coarse grid spacing. To address this issue while minimizing computational cost, we developed a finite difference interpolation (FDI) scheme [Roller et al., J. Chem. Theory Comput. 19, 3889 (2023)] as a means of exploiting the high resolution of the pseudopotential to reduce egg-box effects systematically. Here, we show an implementation of this method in the PARSEC code and examine the practical utility of the combination of FDI with additional methods for improving force precision and/or reducing its computational cost, including orbital-based forces, compensating charges (namely, adding and subtracting a judiciously chosen charge density such that the total density is unaltered), and a modified spatial domain in which the real-space grid is defined. Using selected small molecules, as well as metallic Li, as test cases, we show that a combination of all four aspects leads to a significant reduction in computational cost while retaining a high level of precision that supports accurate structures and vibrational spectra, as well as stable and accurate molecular dynamics runs.

SNN-BERT: Training-efficient Spiking Neural Networks for energy-efficient BERT

Spiking Neural Networks (SNNs) are naturally suited to process sequence tasks such as NLP with low power, due to its brain-inspired spatio-temporal dynamics and spike-driven nature. Current SNNs employ ”repeat coding” that re-enter all input tokens at each timestep, which fails to fully exploit temporal relationships between the tokens and introduces memory overhead. In this work, we align the number of input tokens with the timestep and refer to this input coding as ”individual coding”. To cope with the increase in training time for individual encoded SNNs due to the dramatic increase in timesteps, we design a Bidirectional Parallel Spiking Neuron (BPSN) with following features: First, BPSN supports spike parallel computing and effectively avoids the issue of uninterrupted firing; Second, BPSN excels in handling adaptive sequence length tasks, which is a capability that existing work does not have; Third, the fusion of bidirectional information enhances the temporal information modeling capabilities of SNNs; To validate the effectiveness of our BPSN, we present the SNN-BERT, a deep direct training SNN architecture based on the BERT model in NLP. Compared to prior repeat 4-timestep coding baseline, our method achieves a 6.46× reduction in energy consumption and a significant 16.1% improvement, raising the performance upper bound of the SNN domain on the GLUE dataset to 74.4%. Additionally, our method achieves 3.5× training acceleration and 3.8× training memory optimization. Compared with artificial neural networks of similar architecture, we obtain comparable performance but up to 22.5× energy efficiency. We would provide the codes.

IDAD: An improved tensor train based distributed DDoS attack detection framework and its application in complex networks

With the vigorous development of Internet technology, the scale of systems in the network has increased sharply, which provides a great opportunity for potential attacks, especially the Distributed Denial of Service (DDoS) attack. In this case, detecting DDoS attacks is critical to system security. However, current detection methods exhibit limitations, leading to compromises in accuracy and efficiency. To cope with it, three key strategies are implemented in this paper: (i) Using tensors to model large-scale and heterogeneous data in complex networks; (ii) Proposing a denoising algorithm based on the improved and distributed tensor train (IDTT) decomposition, which optimizes the tensor train(TT) decomposition in terms of parallel computation and low-rank estimation; (iii) Combining (i), (ii) and Light Gradient Boosting Machine (LightGBM) classification model, an efficient DDoS attack detection framework is proposed. Datasets CIC-DDoS2019 and NSL-KDD are used to evaluate the framework, and results demonstrate that accuracy can reach 99.19% while having the characteristics of low storage consumption and well speedup ratio.