Development Of High-performance Computing Research Articles

From complex data to clear insights: visualizing molecular dynamics trajectories.

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

Massively parallel implementation of iterative eigensolvers in large-scale plane-wave density functional theory

The Kohn-sham density functional theory (DFT) is a powerful method to describe the electronic structures of molecules and solids in condensed matter physics, computational chemistry and materials science. However, large and accurate DFT calculations within plane waves process a cubic-scaling computational complexity, which is usually limited by expensive computation and communication costs. The rapid development of high performance computing (HPC) on leadership supercomputers brings new opportunities for developing plane-wave DFT calculations for large-scale systems. Here, we implement parallel iterative eigensolvers in large-scale plane-wave DFT calculations, including Davidson, locally optimal block preconditioned conjugate gradient (LOBPCG), projected preconditioned conjugate gradient (PPCG) and the Chebyshev subspace iteration (CheFSI) algorithms, and analyze the performance of these algorithms in massively parallel plane-wave computing tasks. We adopt a two-level parallelization strategy that combines the message passing interface (MPI) with open multi-processing (OpenMP) parallel programming to handle data exchange and matrix operations in the construction and diagonalization of large-scale Hamiltonian matrix within plane waves. Numerical results illustrate that these iterative eigensolvers can scale up to 42,592 processing cores with high peak performance of 30% on leadship supercomputers to study the electronic structures of bulk silicon systems containing 10,648 atoms. Program summaryProgram Title: Plane wave density functional theory (PWDFT)CPC Library link to program files:https://doi.org/10.17632/c8v2mx5vn4.1Developer's repository link:https://bitbucket.org/berkeleylab/scalesLicensing provisions: BSD 3-clauseProgramming language: C++Nature of problem: PWDFT is used for electronic structure calculations based on Kohn-Sham density functional theory. The key challenge to address is a constrained energy minimization problem, which can also be formulated as a nonlinear eigenvalue problem. MPI/OpenMP-based approaches are employed to provide multi-core acceleration for the study of the chemical and material properties of larger-scale molecules and solids.Solution method: PWDFT implements self-consistent field (SCF) iterations and direct constrained minimization algorithms with various acceleration strategies. It is written in C++ and offers parallel acceleration based on MPI/OpenMP.

Transforming Structural Engineering: Examining the Opportunities and Risks of ChatGPT and Other Large Language Models

The advancements in technology, particularly the development of high-performance computing (HPC) and large language models (LLMs) like ChatGPT, can potentially transform the field of Structural Engineering. Use of LLMs, such as ChatGPT, offers several opportunities in Structural Engineering, including the development of innovative design solutions, use in code-based structural analysis programs by automating repetitive coding tasks, conforming to building code requirements by automating compliance checks, and storing information. The critical concerns arise in LLM’s regarding biases, misinformation, safety, reliability, and lack of domain expertise. This paper explores the opportunities and risks associated with using ChatGPT and LLMs in Structural Engineering, focusing on efficiency, accuracy, and reliability. The main aim of the study is to examine the limitations and potential risks of relying solely on machine-generated information and to provide mitigation strategies to overcome them. Careful management to prevent harmful content, collaboration with human experts for accurate results, establishing guidelines and standards are obligatory measures to address ethical concerns such as bias, privacy, and abuse. Continuous monitoring and updating of LLMs are essential to maintain accuracy and relevance. While ChatGPT and LLMs offer significant benefits in Structural Engineering, responsible usage in combination with human expertise and machine-generated insights are vital to maximizing their potential while mitigating risks and ensuring safe as well as reliable engineering practices.

BIM-Based Building Performance Simulation Analysis: A Multi-Parameter-Driven Approach to Building Energy Efficiency and Carbon Reduction

Global warming and other environmental problems are increasing the demand for high-performance buildings. The development of high-performance computers and building information models has a significant impact on buildings’ energy simulation. High-performance building design needs to comprehensively consider geography, climate, material, cost and other factors which is a highly complex multidisciplinary research problem. Therefore, it is urgent to use advanced modeling and simulation technology to realize it, involving Building Information Modelling (BIM), parametric design and cloud platform. Comprehensive simulation of building performance refers to a multidisciplinary collaborative design, and the correlation between research objects and parameters should be achieved by complex programming design. This study integrates BIM, computer, cloud computing and other technologies to simulate BIM-based building energy consumption performance. Based on project information, geometry information and physical properties exhibited by materials stored in BIM model, the energy analysis model is created. Revit–Dynamo API functions are employed to generate a novel BIM model in Revit after automatically changing and transferring user-defined parameters. BIM energy consumption model is converted into Green Building eXtended Markup Language (GBXML) file and uploaded to Green Building Studio (GBS) cloud server. The optimal project solution is yielded by retrieving the energy consumption simulation results of BIM models with a range of parameters. The case study shows that building volume, glass material, window-wall ratio and window height have significant influence on energy consumption targets of buildings. In hot-summer and cold-winter areas, the total energy consumption of glass materials with high insulation and reflection coefficient is small. The window size slightly impacts the annual lighting energy consumption, but it has significant influence on the annual air conditioning energy consumption, with a maximum increase of about 22%. Finally, the application advantages and limitations of the framework in high-performance building design and its application prospects in energy-saving building design are discussed.

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.

Harmonisation in Atmospheric Dispersion Modelling Approaches to Assess Toxic Consequences in the Neighbourhood of Industrial Facilities

In the land use planning framework in the neighbourhood of industrial facilities, the current approach to predicting the consequences of massive toxic gas releases is generally based on Gaussian or integral models. For many years, CFD models have been more and more used in this context, in accordance with the development of high-performance computing (HPC). The present paper focuses on harmonising input data for atmospheric transport and dispersion (AT&D) modelling between the widely used approaches. First, a synthesis of the practice’s harmonisation for atmospheric dispersion modelling within the framework of risk assessment is presented. Then, these practices are applied to a large-scale INERIS ammonia experimental release. For illustration purposes, the impact of the proposed harmonisation will be evaluated using different approaches: the SLAB model, the FDS model, and the Code_Saturne model. The two main focuses of this paper are the adaptation of the source term dealing with a massive release and the wind flow modelling performance using an experimental signal for CFD model inflow. Finally, comparisons between the modelling and experimental results enable checking the consistency of these approaches and reinforce the importance of the input data harmonisation for each AT&D modelling approach.

Learned multiphysics inversion with differentiable programming and machine learning

We present the Seismic Laboratory for Imaging and Modeling/Monitoring open-source software framework for computational geophysics and, more generally, inverse problems involving the wave equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, the software is designed to be both readable and scalable, allowing researchers to easily formulate problems in an abstract fashion while exploiting the latest developments in high-performance computing. The design principles and their benefits are illustrated and demonstrated by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which, aside from coupling of wave physics and multiphase flow, involves machine learning.

Computational methods and points for attention in absolute configuration determination

With the rapid development of high performance computers and computational methods, including software, an increasing number of experimental chemists have tried to use computational methods such as optical rotation (OR, including the matrix model), optical rotatory dispersion (ORD), electronic circular dichroism (ECD or CD), vibrational circular dichroism (VCD), and magnetic shielding constants—nuclear magnetic resonance (NMR)—to explain and/or assign absolute configuration (AC) for various compounds. Such reports in the field of natural products have increased dramatically. However, every method has its range of application. This leads, in some cases, to incorrect conclusions by researchers who are not familiar with these methods. In this review, we provide experimental chemists and researchers with more computational details and suitable suggestions, and especially hope that this experience may help readers avoid computational pitfalls. Finally, we discuss the use of simplified models to replace original complex structures with a long side chain. The fundamental basis for using models to represent complex chiral compounds, such as in OR calculations, is the existence of conformation pairs with near canceling conformer contributions that justify the use of models rather than the original compounds. Using examples, we here introduce the transition state (TS) calculation, which may benefit readers in this area for use and mastery for their AC study. This review will summarize the general concepts involved in the study of AC determinations.

An efficient communication strategy for massively parallel computation in CFD

With the development of high-performance computers, it is necessary to develop efficient parallel algorithms in the field of computational fluid dynamics (CFD). In this study, a novel parallel communication strategy based on asynchronous and packaged communication is proposed. The strategy implements an aggregated communication process, which requires only one communication in each iteration step, significantly reducing the number of communications. The correctness and convergence of the novel strategy are demonstrated from both theoretical and experimental perspectives. And based on the real vehicle CHN-T model with 140 million meshes, a detailed performance comparison and analysis is performed for the novel strategy and the traditional strategy, showing that the novel strategy has significant advantages in terms of scalability. Finally, the strong scalability and weak scalability tests are carried out separately for the CHN-T model. The strong scaling efficiency can reach 74% with 10.5 billion meshes and 256,000 cores. The weak scaling parallel efficiency can reach 90% with 10 billion meshes and 179,000 cores. This research work has laid an important foundation for the development of the fast design of aircraft and cutting-edge numerical methods.

Odd–even effect on the thermal conductivity of liquid crystalline epoxy resins

Rapid developments in high-performance computing and high-power electronics are driving needs for highly thermal conductive polymers and their composites for encapsulants and interface materials. However, polymers typically have low thermal conductivities of ∼0.2 W/(m K). We studied the thermal conductivity of a series of epoxy resins cured by one diamine hardener and seven diepoxide monomers with different precise ethylene linker lengths (x = 2-8). We found pronounced odd-even effects of the ethylene linker length on the liquid crystalline order, mass density, and thermal conductivity. Epoxy resins with even x have liquid crystalline structure with the highest density of 1.44 g/cm3 and highest thermal conductivity of 1.0 W/(m K). Epoxy resins with odd x are amorphous with the lowest density of 1.10 g/cm3 and lowest thermal conductivity of 0.17 W/(m K). These findings indicate that controlling precise linker length in dense networks is a powerful route to molecular design of thermally conductive polymers.

BIM-Based Multi-Objective Optimization of Low-Carbon and Energy-Saving Buildings

Global warming and other environmental problems are increasing the demand for green and low-carbon buildings. The development of high-performance computers and building information models has a significant impact on low-carbon buildings. Low-carbon building design needs to comprehensively consider geography, climate, material, cost and other factors, a highly complex multidisciplinary research problem. Therefore, it is urgent to use advanced modeling and simulation technology, involving BIM, parametric design, cloud platform and evolutionary algorithm. This paper proposes a BIM based low-carbon building design optimization framework, which realizes the comprehensive trade-off function of building low-carbon energy saving and daylighting performance through an improved genetic algorithm. The framework drives BIM through parameterization and integrates building environment information, geometric information and operation information, including six parts: BIM model establishment, parameter-driven development, building performance simulation, multi-objective optimization design, Pareto frontier analysis, and energy-saving decision-making and evaluation. The case study shows that the simulation results obtained through the framework can effectively achieve building energy conservation while maximizing the lighting performance of the building, providing a scientific basis and reference for construction professionals to design low-carbon buildings. Finally, the application advantages and limitations of the framework in low-carbon building design and its application prospects in low-carbon energy-saving building design are discussed. This research has made contributions to the multi-disciplinary low-carbon energy conservation research field, realized the multi-objective optimization strategy of building performance based on BIM, genetic algorithm and simulation, and is an important supplement to existing building energy conservation and emission reduction optimization design.

A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain

Traditional video conference systems depend largely on computational complexity to ensure system security, but with the development of high-performance computers, the existing encryption system will be seriously threatened. To solve this problem, a hybrid encryption scheme for quantum secure video conferencing combined with blockchain is proposed in this study. In the system solution architecture, first, the quantum key distribution network is embedded in the classic network; then, the “classical + quantum” hybrid encryption scheme is designed according to the secret level required for the video conference content. Besides, the real-time monitoring module of the quantum key distribution network is designed to ensure that users can check the running state of the network at any time. Meeting minutes can be shared by combining with blockchain. In order to quickly query meeting minutes, a cache-efficient query method based on B+ tree is proposed. The experimental results show that compared with the traditional video conference system, the quantum secure video conference system sufficiently integrates the technical advantages of the quantum key distribution to resist the security threats such as channel eavesdropping and high-performance computational attacks while ensuring the stable operation of the classic system, thus providing a video conference system with a higher security level. Meanwhile, the query time cost of blockchain with different lengths is tested, and the query efficiency of the proposed method is 3.15-times higher than the original query efficiency of blockchain.

RPINNs: Rectified-physics informed neural networks for solving stationary partial differential equations

Due to the development of high performance computing, deep learning algorithm has made a significant progress in many fields such as computational mathematics. The physics-informed neural networks have put forward a innovative idea for tackling a broad range of forward and inverse problems of partial differential equations. Motivated by the philosophy of physics-informed neural network and the multigrid method, we introduce the gradient information of numerical solution of physics-informed neural network into the new neural networks and propose the rectified-physics informed neural network for solving stationary partial differential equations. And for solving multi-objective optimization of neural networks, the dynamic weight strategy is adopted to balance numerical difference among terms in the loss function, and effectively alleviate the gradient ill-conditioned phenomenon. Finally, we perform a series of numerical experiments to demonstrate effectiveness of the RPINNs method which is combined with the dynamic weight strategy to improve calculation accuracy.

SW-LZMA: Parallel Implementation of LZMA Based on SW26010 Many-Core Processor

With the development of high-performance computing and big data applications, the scale of data transmitted, stored, and processed by high-performance computing cluster systems is increasing explosively. Efficient compression of large-scale data and reducing the space required for data storage and transmission is one of the keys to improving the performance of high-performance computing cluster systems. In this paper, we present SW-LZMA, a parallel design and optimization of LZMA based on the Sunway 26010 heterogeneous many-core processor. Combined with the characteristics of SW26010 processors, we analyse the storage space requirements, memory access characteristics, and hotspot functions of the LZMA algorithm and implement the thread-level parallelism of the LZMA algorithm based on Athread interface. Furthermore, we make a fine-grained layout of LDM address space to achieve DMA double buffer cyclic sliding window algorithm, which optimizes the performance of SW-LZMA. The experimental results show that compared with the serial baseline implementation of LZMA, the parallel LZMA algorithm obtains a maximum speedup ratio of 4.1 times using the Silesia corpus benchmark, while on the large-scale data set, speedup is 5.3 times.

Intelligent fitting global real‐time task scheduling strategy for high‐performance multi‐core systems

With the development of high-performance computing, it is possible to solve large-scale computing problems. However, the irregularity and access characteristics of computing problems bring challenges to the realisation and performance optimisation. Improving the performance of a single core makes it challenging to maintain Moore's law, and multi-core processors emerge. A chip brings together multiple universal processor cores of equal status and has the same structure supported by an isomorphic multi-core processor. In high-performance computing, the granularity of computing tasks leads to the complexity of scheduling strategies. Satisfying high system performance, load balancing and processor fault tolerance at a minimum cost is the key to task scheduling in the high-performance field, especially in specific multi-core hardware architecture. In this study, global real-time task scheduling is implemented in a high-performance multi-core system. The system adopts the hybrid scheduling among clusters and the intelligent fitting within clusters to implement the global real-time task scheduling strategy. In the cluster scheduling policy, tasks are allowed to preempt the core with low priority, and the priority of tasks that access memory is dynamically improved, higher than that of all the tasks without memory access. An intelligent fitting method is also proposed. When the data read by the task is in the cache and the cache access ability value of the task is within a reasonable threshold, the priority of the task is promoted to the highest priority, preempting the core without the access memory task. The results show that the intelligently fitting global scheduling strategy for multi-core systems has better performance in the nuclear utilisation rate and task schedulability.

Research on Grid Computing of an Ocean Information Data Processing System Based on the Computer Environment

Zhang, Y., 2020. Research on grid computing of an ocean information data processing system based on the computer environment. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 234–237. Coconut Creek (Florida), ISSN 0749-0208.With the development of high-performance computing, grids are shared and used in a unified framework of geographically distributed resources to access ocean information data from different resource nodes geographically distributed at different scales. Based on the computer environment, a marine environment information sharing system is proposed in this paper that can generate irregular grids to represent two-dimensional sea and land shapes, and OpenGL and other computer graphics technologies are used to realize the visualization of ocean numerical prediction results. At the same time, a regular grid is used for calculation in the ocean numerical model. The ocean numerical model improves the simulation results. Finally, the rational results of grid computing in the marine information system are proved through the subdivision and numerical prediction simulation experiments of China's maritime and continental boundaries.

Key issues in exascale computing

Over the past several decades, high performance computing (HPC) in China has undergone tremendous growth under the continuous support of national research programs. The development of exascale computers is the current goal set by the National Key R&D Project on HPC. Starting with a brief historical review of Chinas HPC development, this article analyzes the major challenges encountered in developing exascale computers. Thereafter, some important issues in realizing exascale computing are discussed, including architecture, processor, interconnect, parallel system software, parallel programming, algorithm, and resilience.

Improving the image quality of elastic reverse-time migration in the dip-angle domain using deep learning

Elastic reverse-time migration (ERTM) is becoming increasingly feasible with the development of high-performance computing. It can provide more physical information on subsurface structures. However, the crosstalk artifacts degrade the imaging resolution of ERTM. To obtain high-resolution ERTM imaging, we have developed additional constraints through a convolutional neural network (CNN) in the dip-angle domain. This procedure can significantly improve the image quality of ERTM by recognizing the dominant reflection events and rejecting the crosstalk artifacts in the dip-angle domain. This method can be divided into the following three steps. First, we generate the dip-angle gathers of ERTM using Poynting vectors shot by shot. Then, we stack all the dip-angle gathers over all the shots. Finally, we adopt the CNN to predict the dip-angle constraint, which can suppress the crosstalk artifacts and enhance the ERTM image quality. The picking method using CNN is an end-to-end procedure that can perform automatic picking without additional human intervention once the network is well-trained. The numerical examples have verified the potential of our method.

An Overview of Thermal and Mechanical Design, Control, and Testing of the World's Most Powerful and Fastest Supercomputer

Abstract A new era of computing has begun with the development of high-performance computing (HPC), artificial intelligence (AI), machine learning (ML), and cognitive systems. Dramatic increases in the power density of the electronic components have led to the design and architecture of efficient thermal management technologies on these systems. IBM designed and delivered in 2018 the most powerful and fastest supercomputers of the world known as Summit and Sierra having 200 petaflops peak computing performance through LINPACK benchmarks. These systems which are called as IBM POWER AC922 are both air and liquid cooled, where water is employed in liquid-cooled systems to cool the high-power electronic components including IBM POWER9 processors and NVIDIA graphics processing units (GPUs). In this paper, we highlight the overview of the thermal and mechanical design strategies applied to these systems. Testing and experimental analysis with comparison to computational modeling is provided. Thermal control strategies are investigated for the optimization of overall system efficiency. In air cooled systems, we discuss the fan and heat sink designs, as well as the preheating effect on the PCIe section. In liquid-cooled systems, which have a unique cold plate design cooling the processors and the GPUs with water, we examine the water flow path design for the central processing units (CPUs), the GPUs, and the thermal performance of the cold plate. An overview of the cooling assemblies such as TIMs and air baffles in these systems is discussed. Unit and rack manifolds and rear door heat exchanger (RDHx) are investigated. Water flow and pressure distribution at the node and rack-level are provided.

Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning

Mandatory accurate and specific diagnosis demands have brought about increased challenges for radiologists in pediatric posterior fossa tumor prediction and prognosis. With the development of high-performance computing and machine learning technologies, radiomics provides increasing opportunities for clinical decision-making. Several studies have applied radiomics as a decision support tool in intracranial tumors differentiation. Here we seek to achieve preoperative differentiation between ependymoma (EP) and pilocytic astrocytoma (PA) using radiomics analysis method based on machine learning. A total of 135 Magnetic Resonance Imaging (MRI) slices are divided into training sets and validation sets. Three kinds of radiomics features, including Gabor transform, texture and wavelet transform based ones are used to obtain 300 multimodal features. Kruskal–Wallis test score (KWT) and support vector machines (SVM) are applied for feature selection and tumor differentiation. The performance is investigated via accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. Results show that the accuracy, sensitivity, specificity, and AUC of the selected feature set are 0.8775, 0.9292, 0.8000, and 0.8646 respectively, having no significantdifferencescomparedwiththe overall feature set. For different types of features, texture features yield the best differentiation performance and the significance analysis results are consistent with this. Our study demonstrates texture features perform better than the other features. The radiomics approach based on machine learning is efficient for pediatric posterior fossa tumors differentiation and could enhance the application of radiomics methods for assisted clinical diagnosis.