High Performance Computing Technologies Research Articles

RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints

Molecular dynamics simulation is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementation, with trajectory data play a pivotal role in drug discovery. The advancement of high-performance computing and deep learning technology becomes popular and critical to predict protein properties from vast trajectory data, posing challenges regarding data features extraction from the complicated simulation data and dimensionality reduction. Simultaneously, it is essential to provide a meaningful explanation of the biological mechanism behind dimensionality. To tackle this challenge, we propose a new unsupervised model named RevGraphVAMP to intelligently analyze the simulation trajectory. This model is based on the variational approach for Markov processes (VAMP) and integrates graph convolutional neural networks and physical constraint optimization to enhance the learning performance. Additionally, we introduce attention mechanism to assess the importance of key interaction region, facilitating the interpretation of molecular mechanism. In comparison to other VAMPNets models, our model showcases competitive performance, improved accuracy in state transition prediction, as demonstrated through its application to two public datasets and the Shank3-Rap1 complex, which is associated with autism spectrum disorder. Moreover, it enhanced dimensionality reduction discrimination across different substates and provides interpretable results for protein structural characterization.

Construction of neural network model for exercise load monitoring based on yoga training data and rehabilitation therapy

The Internet of Things is based on the traditional Internet and its purpose is to achieve information exchange between users and devices, as well as between devices. The rapid development of sensor technology, communication network technology, and computer technology has enriched the coverage of the Internet of Things, including a wide range of intelligent applications such as healthcare, smart cities, and smart homes. The development of high-performance computing and machine learning technologies has promoted the wide application of intelligent auxiliary systems in sports medicine. With the rapid development of yoga in the field of sports, athletes can play the various functions of yoga, improve their physical strength and quality, and improve their strength, flexibility, etc., cultivate positive, optimistic, and healthy emotions, and these are conducive to rehabilitation treatment after sports injuries. Therefore, it is feasible and feasible to introduce yoga training into the monitoring of the exercise load of athletes. In this paper, neural network technology was used to break the traditional training method based on experience. Based on yoga training data, through experimental exercise research, it could explore a new effective way to monitor exercise load and rehabilitation treatment, and build an exercise load monitoring model of the Ant Colony Optimization (ACO) neural network. By sorting out the data, statistics and analysis of the data, this article confirmed the effect of yoga training on reducing fatigue after exercise. The experimental results showed that the prediction value obtained by the ACO neural network model was 9.106, and the error was only −0.003 compared to the actual detection value of 9.109. This result showed that the ACO neural network model can perfectly fit the functional relationship between yoga training level and exercise load and has high prediction accuracy. This also marked that the development of high-performance computing systems has entered a new journey in the field of sports and health.

Development of software for neuromarketing based on artificial intelligence and data science using high-performance computing and parallel programming technologies

This paper deals with the scientific research in a new area of human activity – neuromarketing. The development of new software for neuromarketing research based on modern methods and tools of artificial intelligence and data science (neural network technologies) using technologies of high-performance computing and parallel programming are considered in this paper. The results of experimental measurements of electrical activity of the consumer's brain based on electroencephalographic signals depending on various types of marketing stimuli are presented in this paper. The quality of the developed software for neuromarketing is evaluated on the basis of various quality metrics.

GPGPU Programming for Dipolar Field Calculation

Abstract Accelerating computational processes is paramount in numerical infrastructure development, particularly in applications such as the finite element method (FEM) and extensive calculations for simulating 3D processes in materials. In this work, we introduce a novel technique for computing the magnetostatic field of an ellipsoid particle, leveraging CUDA on a graphical card for parallel processing. The implementation on a GPU resulted in a remarkable 20-fold improvement in calculation speed. This achievement not only expedites research tasks, but also enables the exploration of larger and more intricate simulations, facilitating quicker model refinements and deeper insights into material behaviours under various conditions. The utilization of GPU computing aligns with the broader trend in scientific research and engineering, offering a versatile solution for diverse computational challenges beyond this specific task of magnetism. Overall, our work contributes to the ongoing effort to harness high-performance computing (HPC) technologies for accelerated and more efficient simulations in materials science and related fields.

Parallelization and optimization for a pin-by-pin whole core thermal–hydraulic analysis code

With increasingly developing high-performance computing (HPC) technology, pin-by-pin thermal–hydraulic simulations are now feasible. The in-house subchannel analysis code KMC-FBc has been extended to perform pin-by-pin calculations. To expedite the whole-core thermal–hydraulic analysis process, various parallelization and optimization strategies are proposed and implemented at the single-assembly and multi-assembly levels, including acceleration methods for solving large sparse matrix equation systems, memory and file input/output (I/O) optimization, hybrid MPI/OpenMP-based whole-core acceleration method and so on. Parameters for evaluating the parallel performance, such as the running time, speedup, and parallel efficiency, are tested on an HPC cluster. Results show that the maximum acceleration ratio for the whole-core problem with a mesh size in the tens of millions can reach approximately 120, with a parallel efficiency of around 32%, which demonstrates the rationality and effectiveness of the parallel algorithm and optimization methods and lays the foundation for future high-resolution and high-fidelity thermal–hydraulic analysis.

Value co-creation in a high-performance computing (HPC) service ecosystem: opportunities for European SMEs

PurposeThis paper aims to develop a conceptual framework as a valuable investigative tool for future empirical studies examining stakeholders’ interactions within a high-performance computing (HPC) service ecosystem and identifying the enablers for better value co-creation under various service provisioning contexts.Design/methodology/approachThis conceptual paper follows a theory adaptation research design that systematically examines the literature on a substantive topic encompassing HPC technology adoption and usage by European small and medium-sized enterprises (SMEs). Furthermore, it introduces theoretical perspectives from service innovation research as its methodological approach (method theory) to develop a theoretical framework for value co-creation within an HPC service ecosystem.FindingsThe conceptual framework presented in this paper identifies the critical resource elements of SMEs, which can potentially be integrated with other stakeholders’ resources and complement the limited internal resources of SMEs for enhanced value co-creation within an HPC service ecosystem.Research limitations/implicationsThe conceptualisation of an HPC service ecosystem and the potential of value co-creation through integrating stakeholders’ resources, presented in this paper, underscore further empirical research in this direction.Practical implicationsFindings from this paper can inform European policymakers for regional development to frame regional strategies and policies that enable SMEs to better uptake HPC services. Service providers can revisit their business models to reconfigure their organisational resources for enhanced value proposition and co-creation.Social implicationsThis study advances the agenda of transformative service research that aims uplifting changes and societal well-being through a more inclusive and sustainable value co-creation among a wider set of stakeholders within the service ecosystem.Originality/valueThe paper addresses an under-investigated research gap by examining SMEs’ adoption of HPC services and opportunities for higher value co-creation using a service ecosystem approach based on multi-actor interaction, collaboration and resource integration.

Design study of FIT dataflow machine for high performance 3-D electrostatic field simulation

To investigate portable, low power consumption, low cost and green high-performance computing (HPC) technologies which are suitable for industry applications, authors have been working on the development of dedicated computer based on dataflow architecture for electromagnetic field simulations. In this paper, we propose a Finite Integration Technique (FIT) dataflow machine based on BiCG-Stab scheme for 3-D electrostatic field simulations.

Environmental Sustainability and Digital Transformation of Socio-Economic: Quality of Life Perspective

Technological singularity has seriously affected all the social, environmental, and economic genesis factors in mankind’s history. The problem of assessing the quality of life in the digital economy is acquiring new nuances including social services. The objective of this study is to investigate unique impacts of digital transformation on economic, environment, and social progresses on the quality of life improvement in China. Environmental statistical data on the impact of investments in social, state, and other spheres of economic activity are analysed at the machine learning level. Application of high-performance computing (HPC) and big data technologies for obtaining data on socio-economic statistics in real-time, the presence of feedback in the Web 4.0 concept, transfer of a significant part of economic processes to internet platforms provide the information necessary for analysis. As a result, a basis is proposed for implementing software products in the form of institutional decision-making support systems for a long horizon of planning investments in the quality of life.

Disclosure and Forecast of Stock Issuance Information Based on High Performance Computing and Blockchain Technology

Disclosure and Forecast of Stock Issuance Information Based on High Performance Computing and Blockchain Technology

The Use of Artificial Neural Networks in Agricultural Plants

Artificial Neural Networks use high-performance computing and big data technology, opportunities for science to create new opportunities in agriculture. The purpose of writing this article is to analyze the use of artificial neural networks on (a) plant diseases based on plant leaf diseases, (b) plant pests, (c) growth or quality, and (d) agricultural products. The writing method used is a literature study of the research that has been done. The keywords used in the search for references include ANN, plant, diseases, pests, growth or quality, and agricultural products. Publishers for the reference in this article are ScienceDirect and IEEE. The years of publication of the references are restricted from 2015 to 2022. Based on the literature study results, it was concluded that Artificial Neural Networks' deep learning models are accurate for detecting and classifying leaf diseases and pests, detecting growth, and application to agricultural plant products.

Competence development as critical issue for successful performance in HPC technology environment: A case study of Montenegro.

Absorptive capacity is the ability to absorb new knowledge and adopt new technologies. This capacity of an economy is measured through a series of indicators, however, the most important among them are precisely those elements related to the competences required for the adoption of new technologies, as well as their structure. This paper provides an overview of the concepts and systems needed to develop the competencies needed to implement modern technology such as High-Performance Computing (HPC) in Montenegro. In this research paper competencies are viewed holistically. This paper will elaborate the defined competencies related to the HPC technology environment, identified during the implementation of the EuroCC project in Montenegro, but also based on market analyses, combined with the identified indicators of absorption capacity, generally at the national level. By identifying the innovative and business potential of representatives of the public, academic and economic sectors, with special reference to small and medium-sized companies and the IT cluster that make up the dominant segments in the structure of the market sample, as generators and accelerators of innovation, smart growth and the digital economy, we got a clear picture regarding the development of necessary competencies within the NCC team but also at the national level. There must be a systemic approach and sustainable, dynamic projects and tools for the development of human resources in the development of competences.

The role of spatial cyberinfrastructure in geoinformation systems

The potential future of a spatial cyberinfrastructure is considered, combining advanced computer, information and communication technologies to expand the possibilities of computational and information scientific practice and improve the synthesis and analysis of scientific data through collaboration and sharing. Currently, spatial cyberinfrastructure represents a paradigm shift in scientific research that has facilitated easy access to computational utilities and facilitated collaboration across distances and disciplines, thereby enabling scientific breakthroughs to be achieved faster and more efficiently. Spatial cyberinfrastructure seeks to address the long-standing complex problems of processing and analyzing massive and heterogeneous spatial datasets, as well as the need and benefits of flexible and secure spatial data exchange. The focus is on spatial analysis using distributed and high performance computing, sensor networks and other advanced information technology capabilities to transform massive spatial datasets into information and knowledge. To solve large-scale and multi-scale problems, the spatial cyberinfrastructure, despite the complexity of the geographic space, allows transforming spatial analysis into powerful and affordable computing utilities to provide broad scientific breakthroughs when creating the well-known Digital Earth project. Critical analysis and recommendations are provided on issues of conflicting data that researchers do not want to exclude from various data models and geographic scales. This case study shows the value of spatial cyberinfrastructure in addressing the challenges that arise from spatial as well as multilingual, biographical and temporal ambiguities in various databases, solutions that, again, would not be possible without spatial cyberinfrastructure.

OPTIMIZING PARALLELISM IN UNITY WITH JOB SYSTEM TOOL

Every day the gaming industry is developing faster and faster. Especially the popularity of games increased during the pandemic, as many people were forced to stay at home for a long time and play games in their free time. With the increasing popularity of games, the requirements for the games themselves also increase. As a result, modern games require large computer resources. However, not all people can afford to buy computers with the most expensive video cards, many expensive CPUs, and many RAMs and other components. In order to make games accessible to users with weaker devices, developers have few options. At the moment, there are two main ways to solve this problem. The first is to host games on the cloud, and the second is to optimize games for weaker devices. To optimize games, you can use various methods, including a package Job System for Unity. The Job System is one of the main components for working with threads in an Entity Component System (ECS). Also, in the future, in games with the Unity engine, parallelization between weak devices and High-Performance Computing (HPC) technologies will be combined and implemented using cloud technologies. In the future, when this happens, it will help developers, gamers, and even companies providing cloud services.

State of the Art: High-Performance and High-Throughput Computing for Remote Sensing Big Data

In recent years, with the increasing number of Earth observation satellites and the popularization and application of various sensors, remote sensing data have shown a rapid growth trend and present typical big data characteristics. The continuous enrichment of remote sensing data has provided large information resources for Earth science research and promoted the wide application of remote sensing technology in resources, ecology, environment, energy, health, urban management, and so on. However, mining information from multisource heterogeneous remote sensing big data, which requires a large amount of computing power, has many challenges in terms of generality, security, and timeliness. In this article, we summarize the existing research on high-performance computing (HPC) and high-throughput computing (HTC) technologies toward improving the processing efficiency of remote sensing big data. We also analyze the problems and challenges of HPC/HTC technologies in the storage, computation, and analysis of remote sensing big data. Finally, we predict the trend of remote sensing big data processing in the direction of HPC/HTC.

High-Performance Computing in Meteorology under a Context of an Era of Graphical Processing Units

This short review shows how innovative processing units—including graphical processing units (GPUs)—are used in high-performance computing (HPC) in meteorology, introduces current scientific studies relevant to HPC, and discusses the latest topics in meteorology accelerated by HPC computers. The current status surrounding HPC is distinctly complicated in both hardware and software terms, and flows similar to fast cascades. It is difficult to understand and follow the status for beginners; they need to overcome the obstacle of catching up on the information on HPC and connecting it to their studies. HPC systems have accelerated weather forecasts with physical-based models since Richardson’s dream in 1922. Meteorological scientists and model developers have written the codes of the models by making the most of the latest HPC technologies available at the time. Several of the leading HPC systems used for weather forecast models are introduced. Each institute chose an HPC system from many possible alternatives to best match its purposes. Six of the selected latest topics in high-performance computing in meteorology are also reviewed: floating points; spectral transform in global weather models; heterogeneous computing; exascale computing; co-design; and data-driven weather forecasts.

Rapid Model Development for GSFLOW With Python and pyGSFLOW

Following the advancement of high-performance computing and sensor technology and the increased availability of larger climate and land-use data sets, hydrologic models have become more sophisticated. Instead of simple boundary conditions, these data sets are incorporated with the aim of providing more accurate insights into hydrologic processes. Integrated surface-water and groundwater models are developed to represent the most important processes that affect the distribution of water in hydrologic systems. GSFLOW is an integrated hydrologic modeling software that couples surface-water processes from PRMS and groundwater processes from MODFLOW and simulates feedbacks between both components of the hydrologic system. Development of GSFLOW models has previously required multiple tools to separately create surface-water and groundwater input files. The use of these multiple tools, custom workflows, and manual processing complicates reproducibility and confidence in model results. Based on a need for rapid, reproduceable, and robust methods, we present two example problems that showcase the latest updates to pyGSFLOW. The software package, pyGSFLOW, is an end-to-end data processing tool made from open-source Python libraries that enables the user to edit, write input files, run models, and postprocess model output. The first example showcases pyGSFLOW’s capabilities by developing a streamflow network in the Russian River watershed with an area of 3,850 km2 located on the coast of northern California. A second example examines the effects of model discretization on hydrologic prediction for the Sagehen Creek watershed with an area of 28 km2, near Lake Tahoe, California, in the northern Sierra Nevada.

Lightweight distributed computing framework for orchestrating high performance computing and big data

In recent years, the need for the ability to work remotely and subsequently the need for the availability of remote computer-based systems has increased substantially. This trend has seen a dramatic increase with the onset of the 2020 pandemic. Often local data is produced, stored, and processed in the cloud to remedy this flood of computation and storage needs. Historically, HPC (high performance computing) and the concept of big data have been utilized for the storage and processing of large data. However, both HPC and Hadoop can be utilized as solutions for analytical work, though the differences between these may not be obvious. Both use parallel processing techniques and offer options for data to be stored in either a centralized or distributed manner. Recent studies have focused on using a hybrid approach with both technologies. Therefore, the convergence between HPC and big data technologies can be filled with distributed computing machines at the layer described. This paper results from the motivation that there exists a necessity for a distributed computing framework that can scale from SOC (system on chip) boards to desktop computers and servers. For this purpose, in this article, we propose a distributed computing environment that can scale up to devices with heterogeneous architecture, where devices can set up clusters with resource-limited nodes and then run on top of. The solution can be thought of as a minimalist hybrid approach between HPC and big data. Within the scope of this study, not only the design of the proposed system is detailed, but also critical modules and subsystems are implemented as proof of concept.

Fine-Grained Agent-Based Modeling to Predict Covid-19 Spreading and Effect of Policies in Large-Scale Scenarios.

Modeling and forecasting the spread of COVID-19 remains an open problem for several reasons. One of these concerns the difficulty to model a complex system at a high resolution (fine-grained) level at which the spread can be simulated by taking into account individual features. Agent-based modeling usually needs to find an optimal trade-off between the resolution of the simulation and the population size. Indeed, modeling single individuals usually leads to simulations of smaller populations or the use of meta-populations. In this article, we propose a solution to efficiently model the Covid-19 spread in Lombardy, themost populated Italian region with about ten million people. In particular, the model described in this paper is, to the best of our knowledge, the first attempt in literature to model a large population at the single-individual level. To achieve this goal, we propose a framework that implements: i. a scale-free model of the social contacts combining a sociability rate, demographic information, and geographical assumptions; ii. a multi-agent system relying on the actor model and the High-Performance Computing technology to efficiently implement ten million concurrent agents. We simulated the epidemic scenario from January to April 2020 and from August to December 2020, modeling the government's lockdown policies and people's mask-wearing habits. The social modeling approach we propose could be rapidly adapted for modeling future epidemics at their early stage in scenarios where little prior knowledge is available.

Scalability of Nek5000 on High-Performance Computing Clusters Toward Direct Numerical Simulation of Molten Pool Convection

In a postulated severe accident, a molten pool with decay heat can form in the lower head of a reactor pressure vessel, threatening the vessel’s structural integrity. Natural convection in molten pools with extremely high Rayleigh (Ra) number is not yet fully understood as accurate simulation of the intense turbulence remains an outstanding challenge. Various models have been implemented in many studies, such as RANS (Reynolds-averaged Navier–Stokes), LES (large-eddy simulation), and DNS (direct numerical simulation). DNS can provide the most accurate results but at the expense of large computational resources. As the significant development of the HPC (high-performance computing) technology emerges, DNS becomes a more feasible method in molten pool simulations. Nek5000 is an open-source code for the simulation of incompressible flows, which is based on a high-order SEM (spectral element method) discretization strategy. Nek5000 has been performed on many supercomputing clusters, and the parallel performance of benchmarks can be useful for the estimation of computation budgets. In this work, we conducted scalability tests of Nek5000 on four different HPC clusters, namely, JUWELS (Atos Bullsquana X1000), Hawk (HPE Apollo 9000), ARCHER2 (HPE Cray EX), and Beskow (Cray XC40). The reference case is a DNS of molten pool convection in a hemispherical configuration with Ra = 1011, where the computational domain consisted of 391 million grid points. The objectives are (i) to determine if there is strong scalability of Nek5000 for the specific problem on the currently available systems and (ii) to explore the feasibility of obtaining DNS data for much higher Ra. We found super-linear speed-up up to 65536 MPI-rank on Hawk and ARCHER2 systems and around 8000 MPI-rank on JUWELS and Beskow systems. We achieved the best performance with the Hawk system with reasonably good results up to 131072 MPI-rank, which is attributed to the hypercube technique on its interconnection. Given the current HPC technology, it is feasible to obtain DNS data for Ra = 1012, but for cases higher than this, significant improvement in hardware and software HPC technology is necessary.

Scalable in-memory processing of omics workflows

We present a proof of concept implementation of the in-memory computing paradigm that we use to facilitate the analysis of metagenomic sequencing reads. In doing so we compare the performance of POSIX™file systems and key-value storage for omics data, and we show the potential for integrating high-performance computing (HPC) and cloud native technologies. We show that in-memory key-value storage offers possibilities for improved handling of omics data through more flexible and faster data processing. We envision fully containerized workflows and their deployment in portable micro-pipelines with multiple instances working concurrently with the same distributed in-memory storage. To highlight the potential usage of this technology for event driven and real-time data processing, we use a biological case study focused on the growing threat of antimicrobial resistance (AMR). We develop a workflow encompassing bioinformatics and explainable machine learning (ML) to predict life expectancy of a population based on the microbiome of its sewage while providing a description of AMR contribution to the prediction. We propose that in future, performing such analyses in ’real-time’ would allow us to assess the potential risk to the population based on changes in the AMR profile of the community.