Distributed Parallel Computers Research Articles

Enhancing Agriculture Prediction through AI and Parallel Distributed Computing: A Comprehensive Study on the Impact of Weather

This revolutionary study focuses on incorporating Artificial Intelligence (AI) and Parallel Distributed Computing (PDC) in agriculture prediction, with a specific interest in weather patterns. Modern technologies are instrumental in providing solutions to enhance agricultural efficiency and sustainability. Results from this research show that PDCs and AI have the potential to make crop yield predictions more accurate, detect and prevent diseases, and allocate resources efficiently. This paper emphasizes accurate weather forecasts for sustainable farming practices while considering multiple factors involved in decision-making processes according to expert opinions. These hybrid computing approaches are highly effective in generating detailed spatiotemporal information about agricultural systems, improving agricultural predictions' accuracy. Therefore, the study has far-reaching implications for industry players seeking to improve productivity using data-based techniques for sustainability purposes.

Low consumption automatic discovery protocol for DDS-based large-scale distributed parallel computing

DDS (Data Distribution Service) is an efficient communication specification for distributed parallel computing. However, as the scale of computation expands, high network load and memory consumption consistently limit its performance. This paper proposes a low consumption automatic discovery protocol to improve DDS in large-scale distributed parallel computing. Firstly, an improved Bloom Filter called TBF (Threshold Bloom Filter) is presented to compress the data topic. Then it is combined with the SDP(Simple Discovery Protocol) to reduce the consumption of the automatic discovery process in DDS. On this basis, data publication and subscription between the distributed computing nodes are matched using binarization threshold θ and decision threshold T , which can be obtained through iterative optimization algorithms. Experiment results show that the SDPTBF can guarantee higher transmission accuracy while reducing network load and memory consumption, and therefore improve the performance of DDS-based large-scale distributed parallel computing.

Optimized Feature Learning for Anti-Inflammatory Peptide Prediction Using Parallel Distributed Computing

With recent advancements in computational biology, high throughput Next-Generation Sequencing (NGS) has become a de facto standard technology for gene expression studies, including DNAs, RNAs, and proteins; however, it generates several millions of sequences in a single run. Moreover, the raw sequencing datasets are increasing exponentially, doubling in size every 18 months, leading to a big data issue in computational biology. Moreover, inflammatory illnesses and boosting immune function have recently attracted a lot of attention, yet accurate recognition of Anti-Inflammatory Peptides (AIPs) through a biological process is time-consuming as therapeutic agents for inflammatory-related diseases. Similarly, precise classification of these AIPs is challenging for traditional technology and conventional machine learning algorithms. Parallel and distributed computing models and deep neural networks have become major computing platforms for big data analytics now required in computational biology. This study proposes an efficient high-throughput anti-inflammatory peptide predictor based on a parallel deep neural network model. The model performance is extensively evaluated regarding performance measurement parameters such as accuracy, efficiency, scalability, and speedup in sequential and distributed environments. The encoding sequence data were balanced using the SMOTETomek approach, resulting in a high-accuracy performance. The parallel deep neural network demonstrated high speed up and scalability compared to other traditional classification algorithms study’s outcome could promote a parallel-based model for predicting anti-Inflammatory Peptides.

A Fast Large-Scale Path Planning Method on Lunar DEM Using Distributed Tile Pyramid Strategy

In lunar exploration missions, path planning for lunar rovers using digital elevation models (DEMs) is currently a hot topic in academic research. However, research on path planning using large-scale DEMs has rarely been discussed, owing to the low time efficiency of existing algorithms. Therefore, in this article, we propose a fast path-planning method using a distributed tile pyramid strategy and an improved A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm. The proposed method consists of three main steps. First, the tile pyramid is generated for the large lunar DEM and stored in Hadoop distributed file system. Second, a distributed path-planning strategy based on tile pyramid (DPPS-TP) is used to accelerate path-planning tasks on large-scale lunar DEMs using Spark and Hadoop. Finally, an improved A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm was proposed to improve the speed of the path-planning task in each tile. The method was tested using lunar DEM images. Experimental results demonstrate that: in a single-machine serial strategy using source DEM generated by the Chang'e-2 CCD stereo camera, the proposed A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm for open list and closed list with random access feature (OC-RA-A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm) is 3.59 times faster than the traditional A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm in long-distance path planning tasks and compared to the distributed parallel computation strategy using source DEM generated by the Chang'e-2 CCD stereo camera, the proposed DPPS-TP based on tile pyramid DEM is 113.66 times faster in the long-range path planning task.

Deep Learning Algorithms and Parallel Distributed Computing Techniques for High-Resolution Load Forecasting Applying Hyperparameter Optimization

Electrical load forecasting is one of the critical tasks that helps power utility companies in planning and operation as well as the energy managementsystem (EMS) in controlling and optimizing the power grid’s performance. It guarantees system adequacy and reliability by reducing the gap between supply and demand, and it bolsters cybersecurity by enabling false data detection and system security by predicting system peak load. This article aims to build an accurate long-term load forecasting model based on deep learning (DL) algorithms. Initially, big-data analytics and feature engineering are applied to the power load and weather historical data. Next, three different load forecasting models are developed based on three corresponding deep neural network models (DNN): the simple RNN, the long short term memory (LSTM), and the gated recurrent unit (GRU), respectively. To find an accurate DNN model, a two-step hyperparameter optimization is performed consisting of cross-validation. In the first step, the random search (RS) algorithm is employed to narrow the parameter search space to a smaller local one. In the second step, the grid search (GS) algorithm is used to find the best hyperparameters in the local area located by RS. However, the computational burden of the DNN training process is too high and may lead to out-of-memory (OOM) issues, especially when we are dealing with a big volume of data. To overcome this problem, we developed our Python program based on Tensorflow framework and implemented the framework on a high-performance computer cluster (HPCC). Then, different jobs are defined to employ parallel distributed computing (PDC) capability on the SIU BigDawg HPCC. To this end, all DNN hyperparameters (e.g., number of layers, neurons, dropout, batch size, epoch, learning rate, etc.) can be optimized and result in a comprehensive and accurate load forecasting model. Our proposed method, along with the programming framework, is evaluated on a real-world dataset. Among the three models developed and tested, the GRU, with load and weather as features, has demonstrated excellent performance and outperformed the other two models for long-term forecasting with the best metrics (i.e., MSE, MAPE, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^2$</tex-math></inline-formula> ) and daily error almost less than 1%. Also, our best model (optimized GRU) is compared with the other reported state-of-the-art models such as DBN, SVR, RM-LSTM, DM-GCNN, TCMS-CNN. It is demonstrated that the proposed framework yields a superior performance than the others.

Application of multi-factor dynamic interaction graph in vulnerability assessment and online monitoring of transmission lines

The use of interaction graph to evaluate and obtain vulnerable transmission lines is an effective means of preventing cascading failure. However, the continuous changes of the key parameters that affect the cascading failure during the operation of the power system will affect the actual vulnerability of the lines. Especially with the increase in the penetration rate of renewable energy, the impact of frequent uncertainty and randomness brought by it can no longer be ignored. Therefore, the assessment results obtained from the static interaction graph cannot meet the accuracy requirements in the engineering, and also limit the scope of its application. How to realize the dynamization of the static interaction graph is the key to solving the above problems, which is also the difficulty that the current research has been facing. This paper proposes a construction method of multi-factor dynamic interaction graph based on generalized fault chains. With data-driven as the core, it realizes true dynamic and more accurate vulnerability assessment with the help of parallel distributed computing, uninterrupted data stream and sliding window. On this basis, according to the characteristics of generalized fault chains and the dynamic interaction graph, we propose and realize online monitoring for the classification of vulnerability lines for the first time. This function provides strong support for the implementation of measures with real-time characteristics, and can also more effectively reduce the risk and harm of cascading failure. Finally, this paper combines the IEEE 118 node system and deliberate attacks to prove the significance and effectiveness of the research.

Security Attitude Prediction Model of Secret-Related Computer Information System Based on Distributed Parallel Computing Programming

In recent years, there has been an upward trend in the number of leaked secrets. Among them, secret-related computers or networks are connected to the Internet in violation of regulations, cross-use of mobile storage media, and poor security management of secret-related intranets, which are the main reasons for leaks. Therefore, it is of great significance to study the physical isolation and protection technology of classified information systems. Physical isolation is an important part of the protection of classified information systems, which cuts off the possibility of unauthorized outflow of information from the network environment. To achieve the physical isolation of the network environment and build a safe and reliable network, it is necessary to continuously improve the level of network construction and strengthen network management capabilities. At present, the realization of physical isolation technology mainly relies on security products such as firewall, intrusion detection, illegal outreach, host monitoring, and auditing. This study analyzes network security systems such as intrusion detection, network scanning, and firewall. Establishing a model based on network security vulnerabilities and making up for network security hidden dangers caused by holes are generally a passive security system. In a network, the leader of network behavior—human behavior—needs to be constrained according to the requirements of the security management system and monitoring. Accordingly, this study proposes a security monitoring system for computer information network involving classified computer. The system can analyze, monitor, manage, and process the network behavior of the terminal computer host in the local area network, to achieve the purpose of reducing security risks in the network system. Based on the evaluation value sequence, the initial prediction value sequence is obtained by sliding adaptive triple exponential smoothing method. The time-varying weighted Markov chain is used for error prediction, the initial prediction value is corrected, and the accuracy of security situation prediction is improved. According to the security protection requirements of secret-related information systems, a complete, safe, reliable, and controllable security protection system for secret-related information systems is constructed, and the existing security risks and loopholes in secret-related information systems are eliminated to the greatest extent possible. This enables the confidentiality, integrity, and availability of confidential data and information in the computer information system to be reliably protected.

A distributed algorithm for high-dimension convex quadratically constrained quadratic programs

We propose a Jacobi-style distributed algorithm to solve convex, quadratically constrained quadratic programs (QCQPs), which arise from a broad range of applications. While small to medium-sized convex QCQPs can be solved efficiently by interior-point algorithms, large-scale problems pose significant challenges to traditional algorithms that are mainly designed to be implemented on a single computing unit. The exploding volume of data (and hence, the problem size), however, may overwhelm any such units. In this paper, we propose a distributed algorithm for general, non-separable, large-scale convex QCQPs, using a novel idea of predictor-corrector primal-dual update with an adaptive step size. The algorithm enables distributed storage of data as well as parallel distributed computing. We establish the conditions for the proposed algorithm to converge to a global optimum, and implement our algorithm on a computer cluster with multiple nodes using Message Passing Interface (MPI). The numerical experiments are conducted on data sets of various scales from different applications, and the results show that our algorithm exhibits favorable scalability for solving large-scale problems.

A note on computing with Kolmogorov Superpositions without iterations

We extend Kolmogorov’s Superpositions to approximating arbitrary continuous functions with a noniterative approach that can be used by any neural network that uses these superpositions. Our approximation algorithm uses a modified dimension reducing function that allows for an increased number of summands to achieve an error bound commensurate with that of r iterations for any r. This new variant of Kolmogorov’s Superpositions improves upon the original parallelism inherent in them by performing highly distributed parallel computations without synchronization. We note that this approach makes implementation much easier and more efficient on networks of modern parallel hardware, and thus makes it a more practical tool.

Mpi4py: Status Update After 12 Years of Development

MPI for Python (mpi4py) has evolved to become the most used Python binding for the message passing interface (MPI). We report on various improvements and features that mpi4py gradually accumulated over the past decade, including support up to the MPI-3.1 specification, support for CUDA-aware MPI implementations, and other utilities at the intersection of MPI-based parallel distributed computing and Python application development.

Distributed dictionary learning for industrial process monitoring with big data

With the development of sensor and communication technology, industrial systems have accumulated a large amount of data. This data has provided new perspectives and methods for industrial system analysis, monitoring and control, which is proven to be of great significance. However, with the collection and storage of industrial data in a 7 × 24 manner, the computing and information processing capabilities of edge controllers and computers at industrial sites face new challenges. Therefore, this paper proposes a distributed dictionary learning algorithm based on the MapReduce framework. The dictionary learning method can efficiently extract useful information from high-dimensional data for process monitoring. In addition, deploying the algorithm under the MapReduce framework can achieve the purpose of parallel distributed computing, which would solve the issue that the ability of calculation and information processing is limited at industrial sites. Based on extensive numerical experiments, the proposed method can improve the effectiveness and robustness of process monitoring for industrial processes.

Computational Strategies for Scalable Genomics Analysis.

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.

Distributed Parallel Performance of Compression Molding Model for Some Characteristics Identification of Tire Tread Block

A control process based on the mathematical modeling of phase change simulations was used to address process limitations of tire tread manufacturing process. The objective of this model was to predict and monitor expected characteristic identifications respect to space and time. This paper proposes a high-performance control process for visualizing the temperature behavior of a compression molding process using pressure, density, elasticity, crack propagation, volume, space, and time during tire tread block manufacturing operations. The control process modeling of tire tread blocks at the liquid-solid interface enabled the use of large sparse computations for solving the real time molding process of a Rubber Compression Molding Machine (RCMM) LWB VRE 1000 type at the Malaysia Rubber Board Research Center (LGM). The modeling simulation emphasized parallel algorithms, domain decomposition, and parallel processing techniques on Distributed Parallel Computer Architecture (DPCS). This study identified alternative numerical methods and their parallelization by comparing numerical analysis and parallel performance indicators using tables, graphs, and multidimensional visualizations. The multidimensional visualization of characteristics using the high-performance control process model accurately illustrated the heating and cooling profile of the compression molding process.

Distributed Parallel Performance of Compression Molding Model for Some Characteristics Identification of Tire Tread Block.

A control process based on the mathematical modeling of phase change simulations was used to address process limitations of tire tread manufacturing process. The objective of this model was to predict and monitor expected characteristic identifications respect to space and time. This paper proposes a high-performance control process for visualizing the temperature behavior of a compression molding process using pressure, density, elasticity, crack propagation, volume, space, and time during tire tread block manufacturing operations. The control process modeling of tire tread blocks at the liquid-solid interface enabled the use of large sparse computations for solving the real time molding process of a Rubber Compression Molding Machine (RCMM) LWB VRE 1000 type at the Malaysia Rubber Board Research Center (LGM). The modeling simulation emphasized parallel algorithms, domain decomposition, and parallel processing techniques on Distributed Parallel Computer Architecture (DPCS). This study identified alternative numerical methods and their parallelization by comparing numerical analysis and parallel performance indicators using tables, graphs, and multidimensional visualizations. The multidimensional visualization of characteristics using the high-performance control process model accurately illustrated the heating and cooling profile of the compression molding process.

Implementation of a 3D model heat equation using fragmented programming technology

Development of efficient numerical programs for large distributed parallel computers is a challenging problem. Many programming languages, systems and libraries exist and evolve to help with it, yet the problem is far from being solved. Of interest are particular application implementations’ studies, which reveal actual capabilities of a system in the real computation. In this paper, the implementation of an indicative 3D model heat equation parallel solver using fragmented programming technology and LuNA system is investigated. A comparative testing with conventional MPI implementation is presented. The pros and cons of the approach are analyzed for corresponding applications class.

Three‐dimensional image registration using distributed parallel computing

Three-dimensional (3D) images have become increasingly popular in practice. They are commonly used in medical imaging applications. In such applications, it is often critical to compare two 3D images, or monitor a sequence of 3D images. To make the image comparison or image monitoring valid, the related 3D images should be geometrically aligned first, which is called image registration (IR). However, IR for 3D images would take much computing time, especially when a flexible method is considered, which does not impose any parametric form on the underlying geometric transformation. Here, the authors explore a fast-computing environment for 3D IR based on the distributed parallel computing. The selected 3D IR method is based on the Taylor's expansion and 3D local kernel smoothing. It is flexible, but involves much computation. The authors demonstrate that this fast-computing environment can effectively handle the computing problem while keeping the good properties of the 3D IR method. The method discussed here is therefore useful for applications involving big data.

A new online field feature selection algorithm based on streaming data

The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts. In terms of text representation, this paper proposes an online field feature selection algorithm (OFFS algorithm) based on streaming data, which solves the problems of low efficiency and memory consumption of traditional feature selection algorithms. With improvements in the vector space model, the new algorithm can select the real-time feature of the data and quickly generate text vector. In the aspect of classifier design, an OFFS-BP neural network text classifier based on BP neutral network and OFFS algorithm is designed. It adapts to the distributed parallel computing, reduces the training time and balances the computation efficiency and classification accuracy. Finally based on the Spark platform, the OFFS-BP neural network classifier is implemented. The experimental results show that the OFFS-BP neural network classifier is more suitable for big data environment with less computation time and higher classification efficiency.

Let’s HPC: A web-based platform to aid parallel, distributed and high performance computing education

Let’s HPC (www.letshpc.org) is an evolving open-access web-based platform to supplement conventional classroom oriented High Performance Computing (HPC) and Parallel & Distributed Computing (PDC) education. This platform has been developed to allow users to learn, evaluate, teach and see the performance of parallel algorithms from a system’s viewpoint. The Let’s HPC platform’s motivation comes from the experiences of teaching HPC/PDC courses and it is designed to help streamline the process of analyzing parallel programs.At the heart of this platform is a database archiving the performance and execution environment related data of standard parallel algorithm implementations run on different computing architectures using different programming environments. The online plotting and analysis tools of our platform can be combined seamlessly with the database to aid self-learning, teaching, evaluation and discussion of different HPC related topics, with a particular focus on a holistic system’s perspective. The user can quantitatively compare and understand the importance of numerous deterministic as well as non-deterministic factors of both the software and the hardware that impact the performance of parallel programs. Instructors of HPC/PDC related courses can use the platform’s tools to illustrate the importance of proper data collection and analysis in understanding factors impacting performance as well as to encourage peer learning among students. Scripts are provided for automatically collecting performance related data, which can then be analyzed using the platform’s tools. The platform also allows students to prepare a standard lab/project report aiding the instructor in uniform evaluation. The platform’s modular design enables easy inclusion of performance related data from contributors as well as addition of new features in the future.This paper summarizes the background and motivation behind the Let’s HPC project, the design philosophy of the platform, the present capabilities of the platform, as well as the plans for future developments.

PPAPI: A Program for Groundwater Modeling Tasks in Distributed Parallel Computing Environments.

Article impact statement: The software presented in the article helps to run highly parameterized groundwater model calibrations and uncertainty analysis.

Adaptive composite quantile regressions and their asymptotic relative efficiency

ABSTRACTThe composite quantile regression (CQR for short) provides an efficient and robust estimation for regression coefficients. In this paper we introduce two adaptive CQR methods. We make two contributions to the quantile regression literature. The first is that, both adaptive estimators treat the quantile levels as realizations of a random variable. This is quite different from the classic CQR in which the quantile levels are typically equally spaced, or generally, are treated as fixed values. Because the asymptotic variances of the adaptive estimators depend upon the generic distribution of the quantile levels, it has the potential to enhance estimation efficiency of the classic CQR. We compare the asymptotic variance of the estimator obtained by the CQR with that obtained by quantile regressions at each single quantile level. The second contribution is that, in terms of relative efficiency, the two adaptive estimators can be asymptotically equivalent to the CQR method as long as we choose the generic distribution of the quantile levels properly. This observation is useful in that it allows to perform parallel distributed computing when the computational complexity issue arises for the CQR method. We compare the relative efficiency of the adaptive methods with respect to some existing approaches through comprehensive simulations and an application to a real-world problem.