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Journal articles on the topic 'Computing clusters'

Author: Grafiati

Published: 30 May 2022

Last updated: 25 July 2025

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1

ROSENBERG, ARNOLD L., and RON C. CHIANG. "HETEROGENEITY IN COMPUTING: INSIGHTS FROM A WORKSHARING SCHEDULING PROBLEM." International Journal of Foundations of Computer Science 22, no. 06 (2011): 1471–93. http://dx.doi.org/10.1142/s0129054111008829.

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Heterogeneity complicates the use of multicomputer platforms. Can it also enhance their performance? How can one measure the power of a heterogeneous assemblage of computers ("cluster"), in absolute terms (how powerful is this cluster) and relative terms (which cluster is more powerful)? Is a cluster that has one super-fast computer and the rest of "average" speed more/less powerful than one all of whose computers are "moderately" fast? If you can replace just one computer in a cluster with a faster one, should you replace the fastest? the slowest? A result concerning "worksharing" in heteroge

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WEBER, MICHAEL. "WORKSTATION CLUSTERS: ONE WAY TO PARALLEL COMPUTING." International Journal of Modern Physics C 04, no. 06 (1993): 1307–14. http://dx.doi.org/10.1142/s0129183193001026.

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The feasibility and constraints of workstation clusters for parallel processing are investigated. Measurements of latency and bandwidth are presented to fix the position of clusters in comparison to massively parallel systems. So it becomes possible to identify the kind of applications that seem to be suited for running on a cluster.

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Tripathy, Minakshi, and C. R. Tripathy. "A Comparative Analysis of Performance of Shared Memory Cluster Computing Interconnection Systems." Journal of Computer Networks and Communications 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/128438.

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In recent past, many types of shared memory cluster computing interconnection systems have been proposed. Each of these systems has its own advantages and limitations. With the increase in system size of the cluster interconnection systems, the comparative analysis of their various performance measures becomes quite inevitable. The cluster architecture, load balancing, and fault tolerance are some of the important aspects, which need to be addressed. The comparison needs to be made in order to choose the best one for a particular application. In this paper, a detailed comparative study on four

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М.Ю., Воробьев, Рыбаков А.А. та Сальников А.Н. "Инструмент для моделирования балансировки потока задач пользователей между несколькими независимыми вычислительными кластерами". Труды НИИСИ РАН 9, № 6 (2020): 142–47. http://dx.doi.org/10.25682/niisi.2019.6.0018.

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Работа посвящена организации программного окружения для анализа алгоритмов балансировки нагрузки на ресурсы группы вычислительных кластеров, используемых для высокопроизводительных вычислений. Моделирование производилось путем одновременного запуска нескольких систем ведения очередей SLURM, работающих на нескольких виртуальных машинах в режиме мультиузла. Поток задач создавался посредством инструмента моделирования pseudo-cluster, модифицированного для поддержки одновременной работы с несколькими кластерами. Система апробирована на модельном журнале вычислительных задач пользователей, состояще

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Stone, J., and F. Ercal. "Workstation clusters for parallel computing." IEEE Potentials 20, no. 2 (2001): 31–33. http://dx.doi.org/10.1109/45.954655.

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6

Singhania, Shrinkhala, and Monika Tak. "Workstation Clusters for Parallel Computing." International Journal of Engineering Trends and Technology 28, no. 1 (2015): 13–14. http://dx.doi.org/10.14445/22315381/ijett-v28p203.

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Regassa, Dereje, Heonyoung Yeom, and Yongseok Son. "Harvesting the Aggregate Computing Power of Commodity Computers for Supercomputing Applications." Applied Sciences 12, no. 10 (2022): 5113. http://dx.doi.org/10.3390/app12105113.

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Distributed supercomputing is becoming common in different companies and academia. Most of the parallel computing researchers focused on harnessing the power of commodity processors and even internet computers to aggregate their computation powers to solve computationally complex problems. Using flexible commodity cluster computers for supercomputing workloads over a dedicated supercomputer and expensive high-performance computing (HPC) infrastructure is cost-effective. Its scalable nature can make it better employed to the available organizational resources, which can benefit researchers who

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Radchenko, I., O. Shekhovtsov, A. Kovalenko, and O. Sytnyk. "FORMATION OF CLUSTERS ON SINGLE-BOARD COMPUTERS IN IOT NETWORKS." Системи управління, навігації та зв’язку. Збірник наукових праць 2, no. 76 (2024): 141–43. http://dx.doi.org/10.26906/sunz.2024.2.141.

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The article looks at the problem of using single -board computers for Internet technology . An analysis of current single-board computers in various countries was carried out. Single-board computers and clusters of single-board computers have found their place in the concept of edge computing, allowing optimization of hard computing by placing computing resources closer to the core. The idea of a “virtual cluster” lies in the unification and organization of disparate heterogeneous devices for the development of various complex computing tasks from the available resources of the existing infras

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S., Shreyanth, and Niveditha S. "CLUSTER-BASED GRID COMPUTING ON WIRELESS NETWORK DATA TRANSMISSION WITH ROUTING ANALYSIS PROTOCOL AND DEEP LEARNING." International Journal of Advanced Research 11, no. 06 (2023): 517–34. http://dx.doi.org/10.21474/ijar01/17096.

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Grid computing based on clusters has emerged as a promising strategy for improving the efficacy of wireless network data transmission. This study examines the incorporation of cluster-based grid computing, routing analysis protocols, and deep learning techniques to optimize data transmission in wireless networks. The proposed method utilizes clusters to distribute computing duties and enhance resource utilization, resulting in efficient data transmission. To further improve the routing process, a novel routing analysis protocol is introduced, which dynamically adapts to network conditions and

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Sheng, Chong Chong, Wei Song Hu, Xin Ming Hu, and Bai Feng Wu. "StreamMAP: Automatic Task Assignment System on GPU Cluster." Advanced Materials Research 926-930 (May 2014): 2414–17. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2414.

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GPU Clusters which use General-Purpose GPUs (GPGPUs) as accelerators are becoming more and more popular in high performance computing area. Currently the mainly used programming model for GPU cluster is hybrid MPI/CUDA. However, when using this model, programmers tend to need detailed knowledge of the hardware resources, which makes the program more complicated and less portable. In this paper, we present StreamMAP, an automatic task assignment system on GPU Clusters. The main contributions of StreamMAP are (1) It provides powerful yet concise language extension suitable to describe the comput

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SAI, Oleksandr, and Kyrylo KRASNIKOV. "FUZZY MODEL OF ELECTRICITY CONTROL WITH WIRELESS INFORMATION PROCESSED ON GPU." Computer systems and information technologies, no. 3 (September 26, 2024): 44–50. http://dx.doi.org/10.31891/csit-2024-3-6.

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Methods of processing information transmitted through wireless networks with software development are investigated in the work. Innovative methods of data transmission such as optical technologies, quantum data transmission and wireless data transmission technologies are disclosed. It is noted that in the modern understanding, the concept of distributed computing defines the process of convergence (convergence) of distributed processing methods, such as GRID, cloud and fog computing, with the combination of virtual cluster systems (grid clusters, cloud clusters and fog clusters) into a single

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Pushkar, V. I., H. D. Kyselov, YE V. Olenovych, and O. H. Kyivskyi. "Computing cluster performance evaluation in department of the university." Electronics and Communications 15, no. 5 (2010): 211–16. http://dx.doi.org/10.20535/2312-1807.2010.58.5.285236.

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An educational cluster based on the low speed servers was created. Their testing and total performance evaluations were carried out. The testing results describe the possibility of cluster implementation for educational process in the meaning of parallel programming studying and clusters system administrators training.

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Llorens-Carrodeguas, Alejandro, Stefanos G. Sagkriotis, Cristina Cervelló-Pastor, and Dimitrios P. Pezaros. "An Energy-Friendly Scheduler for Edge Computing Systems." Sensors 21, no. 21 (2021): 7151. http://dx.doi.org/10.3390/s21217151.

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The deployment of modern applications, like massive Internet of Things (IoT), poses a combination of challenges that service providers need to overcome: high availability of the offered services, low latency, and low energy consumption. To overcome these challenges, service providers have been placing computing infrastructure close to the end users, at the edge of the network. In this vein, single board computer (SBC) clusters have gained attention due to their low cost, low energy consumption, and easy programmability. A subset of IoT applications requires the deployment of battery-powered SB

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Valentino, Rico, Woo-Sung Jung, and Young-Bae Ko. "A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks." Sensors 18, no. 11 (2018): 3751. http://dx.doi.org/10.3390/s18113751.

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Drones have recently become extremely popular, especially in military and civilian applications. Examples of drone utilization include reconnaissance, surveillance, and packet delivery. As time has passed, drones’ tasks have become larger and more complex. As a result, swarms or clusters of drones are preferred, because they offer more coverage, flexibility, and reliability. However, drone systems have limited computing power and energy resources, which means that sometimes it is difficult for drones to finish their tasks on schedule. A solution to this is required so that drone clusters can c

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Du, Ran, Jingyan Shi, Xiaowei Jiang, and Jiaheng Zou. "Cosmos : A Unified Accounting System both for the HTCondor and Slurm Clusters at IHEP." EPJ Web of Conferences 245 (2020): 07060. http://dx.doi.org/10.1051/epjconf/202024507060.

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HTCondor was adopted to manage the High Throughput Computing (HTC) cluster at IHEP in 2016. In 2017 a Slurm cluster was set up to run High Performance Computing (HPC) jobs. To provide accounting services for these two clusters, we implemented a unified accounting system named Cosmos. Multiple workloads bring different accounting requirements. Briefly speaking, there are four types of jobs to account. First of all, 30 million single-core jobs run in the HTCondor cluster every year. Secondly, Virtual Machine (VM) jobs run in the legacy HTCondor VM cluster. Thirdly, parallel jobs run in the Slurm

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Accion, E., A. Bria, G. Bernabeu, et al. "Dimensioning storage and computing clusters for efficient high throughput computing." Journal of Physics: Conference Series 396, no. 4 (2012): 042040. http://dx.doi.org/10.1088/1742-6596/396/4/042040.

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Khettabi, Karima, Zineddine Kouahla, Brahim Farou, Hamid Seridi, and Mohamed Amine Ferrag. "Efficient Method for Continuous IoT Data Stream Indexing in the Fog-Cloud Computing Level." Big Data and Cognitive Computing 7, no. 2 (2023): 119. http://dx.doi.org/10.3390/bdcc7020119.

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Internet of Things (IoT) systems include many smart devices that continuously generate massive spatio-temporal data, which can be difficult to process. These continuous data streams need to be stored smartly so that query searches are efficient. In this work, we propose an efficient method, in the fog-cloud computing architecture, to index continuous and heterogeneous data streams in metric space. This method divides the fog layer into three levels: clustering, clusters processing and indexing. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to group

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18

Xu, Zhao. "Coordination Method on Cloud Computing Clusters." Advanced Materials Research 605-607 (December 2012): 2160–63. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2160.

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This paper makes the case for explicit coordination of network transmission activities among virtual machines (VMs) in the data center Ethernet to proactively prevent network congestion. We think that virtualization has opened up new opportunities for explicit coordination that are simple, effective, currently feasible, and independent of switch-level hardware support. We show that explicit coordination can be implemented transparently without modifying any applications, standard protocols, network switches, or VMs.

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Grushin, D. A., and N. N. Kuzyurin. "On Effective Scheduling in Computing Clusters." Programming and Computer Software 45, no. 7 (2019): 398–404. http://dx.doi.org/10.1134/s0361768819070077.

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20

Harrawood, Brian P., Greeshma A. Agasthya, Manu N. Lakshmanan, Gretchen Raterman, and Anuj J. Kapadia. "Geant4 distributed computing for compact clusters." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 764 (November 2014): 11–17. http://dx.doi.org/10.1016/j.nima.2014.07.014.

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21

Atiquzzaman, Mohammed, and Pradip K. Srimani. "Parallel computing on clusters of workstations." Parallel Computing 26, no. 2-3 (2000): 175–77. http://dx.doi.org/10.1016/s0167-8191(99)00101-5.

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22

Hamad, Faten. "An Overview of Hadoop Scheduler Algorithms." Modern Applied Science 12, no. 8 (2018): 69. http://dx.doi.org/10.5539/mas.v12n8p69.

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Hadoop is a cloud computing open source system, used in large-scale data processing. It became the basic computing platforms for many internet companies. With Hadoop platform users can develop the cloud computing application and then submit the task to the platform. Hadoop has a strong fault tolerance, and can easily increase the number of cluster nodes, using linear expansion of the cluster size, so that clusters can process larger datasets. However Hadoop has some shortcomings, especially in the actual use of the process of exposure to the MapReduce scheduler, which calls for more researches

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Singh, Niharika, Upasana Lakhina, Ajay Jangra, and Priyanka Jangra. "Verification and Identification Approach to Maintain MVCC in Cloud Computing." International Journal of Cloud Applications and Computing 7, no. 4 (2017): 41–59. http://dx.doi.org/10.4018/ijcac.2017100103.

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MultiVersion concurrency control is maintained over transactional database systems for secure, fast and efficient access to the shared data file implementation scenario. Most of the services and application offered in cloud world are real-time, which entails optimized compatibility service environment between master and slave clusters. In the paper, offered methodology supports replication and triggering methods intended for data consistency and dynamicity. Here, cluster based communication is set up for processing. Intercommunication among different clusters is administered through middleware

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Thesma, Vaishnavi, Glen C. Rains, and Javad Mohammadpour Velni. "Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping." Sensors 24, no. 3 (2024): 970. http://dx.doi.org/10.3390/s24030970.

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In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster’s distributed file system for robust file access and distributed, parallel processing. We then submit job requests to our cluste

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Meng, Lamei. "The Promotion Effect of the Improved ISCA Model on the Application of Accounting Informatization in Small- and Medium-Sized Enterprises in the Cloud Computing Environment." Mobile Information Systems 2022 (May 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/4228178.

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Cloud computing has played a strong role in promoting the accounting informatization of small and medium-sized enterprises, which is helpful to accelerate the construction of accounting informatization. In order to improve the accounting informatization construction effect of small and medium-sized enterprises, this paper proposes an improved ISCA accounting informatization model based on cloud computing and proposes a semisupervised clustering method of accounting information based on the minimum prototype cluster to classify accounting information. Moreover, this paper uses labeled samples t

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Mesheryakov, Roman, Alexander Moiseev, Anton Demin, Vadim Dorofeev, and Vasily Sorokin. "Using Parallel Computing in Queueing Network Simulation." Key Engineering Materials 685 (February 2016): 943–47. http://dx.doi.org/10.4028/www.scientific.net/kem.685.943.

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The paper is devoted to the simulation of queueing networks on high performance computer clusters. The objective is to develop a mathematical model of queueing network and simulation approach to the modelling of the general network functionality, as well as to provide a software implementation on a high-performance computer cluster. The simulation is based on a discrete-event approach, object oriented programming, and MPI technology. The model of the queueing networks simulation system was developed as an application that allows a user to simulate networks of rather free configuration. The exp

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Kovalenko, Vadim, Anna Rodakova, Hamza Mohammed Ridha Al-Khafaji, Artem Volkov, Ammar Muthanna, and Andrey Koucheryavy. "Resource Allocation Computing Algorithm for UAV Dynamical Statements based on AI Technology." Webology 19, no. 1 (2022): 2307–19. http://dx.doi.org/10.14704/web/v19i1/web19157.

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An unmanned aerial vehicle (UAV) is one of the complex and relevant communication networks of 5G and 2030 networks. Development of technologies for virtualization (NFV), containerization and orchestration of data systems. NFV technology can be implemented not only in the data center but also on the switch or router. Thus, by analogy with the above-described trend, flying network segments can also use computing power to solve any problems. For example, deployment on virtual distributed capacities of a flying station controller, an internal network controller. Within the framework of this direct

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Fujiwara, Yasuhiro, Atsutoshi Kumagai, Yasutoshi Ida, Masahiro Nakano, Makoto Nakatsuji, and Akisato Kimura. "Efficient Algorithm for K-Multiple-Means." Proceedings of the ACM on Management of Data 2, no. 1 (2024): 1–26. http://dx.doi.org/10.1145/3639273.

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K-Multiple-Means is an extension of K-means for the clustering of multiple means used in many applications, such as image segmentation, load balancing, and blind-source separation. Since K-means uses only one mean to represent each cluster, it fails to capture non-spherical cluster structures of data points. However, since K-Multiple-Means represents the cluster by computing multiple means and grouping them into specified c clusters, it can effectively capture the non-spherical clusters of the data points. To obtain the clusters, K-Multiple-Means updates a similarity matrix of a bipartite grap

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Lu, Wei Jia, and Zhuang Zhi Yan. "Improved FCM Algorithm Based on K-Means and Granular Computing." Journal of Intelligent Systems 24, no. 2 (2015): 215–22. http://dx.doi.org/10.1515/jisys-2014-0119.

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AbstractThe fuzzy clustering algorithm has been widely used in the research area and production and life. However, the conventional fuzzy algorithms have a disadvantage of high computational complexity. This article proposes an improved fuzzy C-means (FCM) algorithm based on K-means and principle of granularity. This algorithm is aiming at solving the problems of optimal number of clusters and sensitivity to the data initialization in the conventional FCM methods. The initialization stage of the K-medoid cluster, which is different from others, has a strong representation and is capable of det

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Piyoungkorn, Kajornsak, Siriboon Chaisawat, and Chalee Vorakulpipat. "Trusted Electronic Contract for Enabling Peer-to-Peer HPC Resource Sharing." Applied Sciences 12, no. 10 (2022): 5153. http://dx.doi.org/10.3390/app12105153.

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With the growing need for HPC resource usage in Thailand, this study aims to foster the creation of an HPC resource sharing ecosystem based on available in-house computing infrastructure. The model of computing resource sharing based on blockchain technology is presented for bridging communication between multiple clusters of HPC systems. The use of blockchain technology allows states among HPC systems to be synchronized and extends capabilities in enforcing governing rules. A smart contract was deployed on the blockchain network to enable users to request computing resources. Upon a request b

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Syvolovskyi, Illia, and Volodymyr Lysechko. "A METHOD OF HIERARCHICAL CLUSTERING OF NODES IN DISTRIBUTED TELECOMMUNICATION SYSTEMS USING GRAPH ALGORITHMS." Системи управління, навігації та зв’язку. Збірник наукових праць 2, no. 80 (2025): 255–62. https://doi.org/10.26906/sunz.2025.2.255.

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This article describes a modified method for hierarchical clustering of computing nodes in distributed telecommunication systems, considering node performance, network topology, delays, and communication channel bandwidth. The proposed method is based on a modified Louvain algorithm that performs multi-step graph clustering with dynamic parameter adjustment. This makes it possible to control the size of clusters and their internal density in accordance with the specified targets, minimizing the fragmentation of the network structure. Based on a comparative analysis of modern clustering methods

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Colucci, Simona, Francesco Maria Donini, and Eugenio Di Sciascio. "Computing the Commonalities of Clusters in Resource Description Framework: Computational Aspects." Data 9, no. 10 (2024): 121. http://dx.doi.org/10.3390/data9100121.

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Clustering is a very common means of analysis of the data present in large datasets, with the aims of understanding and summarizing the data and discovering similarities, among other goals. However, despite the present success of the use of subsymbolic methods for data clustering, a description of the obtained clusters cannot rely on the intricacies of the subsymbolic processing. For clusters of data expressed in a Resource Description Framework (RDF), we extend and implement an optimized, previously proposed, logic-based methodology that computes an RDF structure—called a Common Subsumer—desc

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Bushur, Jacob, and Chao Chen. "Exploiting Raspberry PI Clusters and Campus Lab Computers for Distributed Computing." International Journal of Computer Science and Information Technology 14, no. 03 (2022): 41–54. http://dx.doi.org/10.5121/ijcsit.2022.14304.

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Distributed computing networks harness the power of existing computing resources and grant access to significant computing power while averting the costs of a supercomputer. This work aims to configure distributed computing networks using different computer devices and explore the benefits of the computing power of such networks. First, an HTCondor pool consisting of sixteen Raspberry Pi single-board computers and one laptop is created. The second distributed computing network is set up with Windows computers in university campus labs. With the HTCondor setup, researchers inside the university

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Uma Maheswari Kaliyaperumal, Mary Saira Bhanu Somasundaram, and Nickolas Savarimuthu. "Partitioning-Based Data Sharing Approach for Data Integrity Verification in Distributed Fog Computing." International Journal of Engineering and Technology Innovation 13, no. 2 (2023): 160–74. http://dx.doi.org/10.46604/ijeti.2023.10685.

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With the increasing popularity of the internet of things (IoT), fog computing has emerged as a unique cutting-edge approach along with cloud computing. This study proposes an approach for data integrity verification in fog computing that does not require metadata stored on the user side and can handle big data efficiently. In the proposed work, fuzzy clustering is used to cluster IoT data; dynamic keys are used to encrypt the clusters; and dynamic permutation is used to distribute encrypted clusters among fog nodes. During the process of data retrieval, fuzzy clustering and message authenticat

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Dongarra, Jack J., and Bernard Tourancheau. "Clusters and Computational Grids for Scientific Computing." International Journal of High Performance Computing Applications 13, no. 3 (1999): 179. http://dx.doi.org/10.1177/109434209901300301.

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Sun, Chengzhung, Wanlei Zhou, Andrzej Goscinski, and Xue-bin Chi. "Advanced services for Clusters and Internet computing." Future Generation Computer Systems 20, no. 4 (2004): 501–3. http://dx.doi.org/10.1016/s0167-739x(03)00169-9.

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Javangula, Pradeep, Kourosh Modarre, Paresh Shenoy, Yi Liu, and Aran Nayebi. "Efficient Hybrid Algorithms for Computing Clusters Overlap." Procedia Computer Science 108 (2017): 1050–59. http://dx.doi.org/10.1016/j.procs.2017.05.212.

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Mahanti, Anirban, and Derek L. Eager. "Adaptive data parallel computing on workstation clusters." Journal of Parallel and Distributed Computing 64, no. 11 (2004): 1241–55. http://dx.doi.org/10.1016/j.jpdc.2004.07.005.

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Dongarra, Jack, Masaaki Shimasaki, and Bernard Tourancheau. "Clusters and computational grids for scientific computing." Parallel Computing 27, no. 11 (2001): 1401–2. http://dx.doi.org/10.1016/s0167-8191(01)00095-3.

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Niemi, Tapio, and Ari-Pekka Hameri. "Memory-based scheduling of scientific computing clusters." Journal of Supercomputing 61, no. 3 (2011): 520–44. http://dx.doi.org/10.1007/s11227-011-0612-6.

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Xavier, Percival, Wentong Cai, and Bu-Sung Lee. "Workload management of cooperatively federated computing clusters." Journal of Supercomputing 36, no. 3 (2006): 309–22. http://dx.doi.org/10.1007/s11227-006-8300-7.

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Liu, Zhouding, and Jia Li. "Scalable and Distributed Mathematical Modeling Algorithm Design and Performance Evaluation in Heterogeneous Computing Clusters." Scalable Computing: Practice and Experience 25, no. 5 (2024): 3812–21. http://dx.doi.org/10.12694/scpe.v25i5.3001.

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A growing number of scalable and distributed methods are required to effectively simulate complicated events as computing needs in the research and industrial sectors keep growing. A novel approach for developing and accessing mathematically modeled methods in heterogeneous computing clusters is proposed in this study to meet this difficulty. The suggested methodology uses DRL based Parallel Computational model for the evaluation of Heterogenous computing clusters. The algorithms makes use of parallelization methods to split up the processing burden among several nodes, supporting the variety

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Mellerup, Erling, Martin Balslev Jørgensen, Henrik Dam, and Gert Lykke Møller. "Combinations of SNP genotypes from the Wellcome Trust Case Control Study of bipolar patients." Acta Neuropsychiatrica 30, no. 2 (2017): 106–10. http://dx.doi.org/10.1017/neu.2017.36.

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ObjectivesCombinations of genetic variants are the basis for polygenic disorders. We examined combinations of SNP genotypes taken from the 446 729 SNPs in The Wellcome Trust Case Control Study of bipolar patients.MethodsParallel computing by graphics processing units, cloud computing, and data mining tools were used to scan The Wellcome Trust data set for combinations.ResultsTwo clusters of combinations were significantly associated with bipolar disorder. One cluster contained 68 combinations, each of which included five SNP genotypes. Of the 1998 patients, 305 had combinations from this clust

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Wu, Wenkai, Theodoros Pavloudis, Alexey V. Verkhovtsev, Andrey V. Solov’yov, and Richard E. Palmer. "Molecular dynamics simulation of nanofilament breakage in neuromorphic nanoparticle networks." Nanotechnology 33, no. 27 (2022): 275602. http://dx.doi.org/10.1088/1361-6528/ac5e6d.

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Abstract Neuromorphic computing systems may be the future of computing and cluster-based networks are a promising architecture for the realization of these systems. The creation and dissolution of synapses between the clusters are of great importance for their function. In this work, we model the thermal breakage of a gold nanofilament located between two gold nanoparticles via molecular dynamics simulations to study on the mechanisms of neuromorphic nanoparticle-based devices. We employ simulations of Au nanowires of different lengths (20–80 Å), widths (4–8 Å) and shapes connecting two Au1415

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Li, Tsung-Lung, and Wen-Cai Lu. "Structural and electronic characteristics of intercalated monopotassium–rubrene: Simulation on a commodity computing cluster." Journal of Theoretical and Computational Chemistry 15, no. 04 (2016): 1650035. http://dx.doi.org/10.1142/s0219633616500358.

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The structural and electronic characteristics of the intercalated monopotassium–rubrene (K1Rub) are studied. In the intercalated K1Rub, one of the two pairs of phenyl groups of rubrene is intercalated by potassium, whereas the other pair remains pristine. This structural feature facilitates the comparison of the electronic structures of the intercalated and pristine pairs of phenyl groups. It is found that, in contrast to potassium adsorption to rubrene, the potassium intercalation promotes the carbon [Formula: see text] orbitals of the intercalated pair of phenyls to participate in the electr

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Ward, Doyle V., Andrew G. Hoss, Raivo Kolde, et al. "Integration of genomic and clinical data augments surveillance of healthcare-acquired infections." Infection Control & Hospital Epidemiology 40, no. 6 (2019): 649–55. http://dx.doi.org/10.1017/ice.2019.75.

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AbstractBackground:Determining infectious cross-transmission events in healthcare settings involves manual surveillance of case clusters by infection control personnel, followed by strain typing of clinical/environmental isolates suspected in said clusters. Recent advances in genomic sequencing and cloud computing now allow for the rapid molecular typing of infecting isolates.Objective:To facilitate rapid recognition of transmission clusters, we aimed to assess infection control surveillance using whole-genome sequencing (WGS) of microbial pathogens to identify cross-transmission events for ep

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Alandes Pradillo, Maria, Nils Høimyr, Pablo Llopis Sanmillan, and Markus Tapani Jylhänkangas. "Migrating Engineering Windows HPC applications to Linux HTCondor and Slurm Clusters." EPJ Web of Conferences 245 (2020): 09016. http://dx.doi.org/10.1051/epjconf/202024509016.

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The CERN IT department has been maintaining different High Performance Computing (HPC) services over the past five years. While the bulk of computing facilities at CERN are running under Linux, a Windows cluster was dedicated for engineering simulations and analysis related to accelerator technology development. The Windows cluster consisted of machines with powerful CPUs, big memory, and a low-latency interconnect. The Linux cluster resources are accessible through HTCondor, and are used for general purpose parallel but single-node type jobs, providing computing power to the CERN experiments

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Zhuang, Yi Jie, Min Wang, and Xiao Chong Pan. "Research on Cloud Computing Industrial Cluster Innovation Evaluation Model." Advanced Materials Research 711 (June 2013): 647–51. http://dx.doi.org/10.4028/www.scientific.net/amr.711.647.

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In this paper, a Bayesian network-based assessment model used for evaluating the innovation of cloud computing industry is presented. Firstly, the innovation measurement model of cloud computing industrial clusters is designed. Then Bayesian network assessment and the self-learning method to the model are proposed. Finally, accompanying with empirical data, the most likely innovation status value of cloud computing industrial clusters and key variables influencing the innovation status value can be predicted. This model can provide the theory basis for researching the innovative development of

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Al-Neama, Mohammed W., Naglaa M. Reda, and Fayed F. M. Ghaleb. "An Improved Distance Matrix Computation Algorithm for Multicore Clusters." BioMed Research International 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/406178.

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Distance matrix has diverse usage in different research areas. Its computation is typically an essential task in most bioinformatics applications, especially in multiple sequence alignment. The gigantic explosion of biological sequence databases leads to an urgent need for accelerating these computations.DistVectalgorithm was introduced in the paper of Al-Neama et al. (in press) to present a recent approach for vectorizing distance matrix computing. It showed an efficient performance in both sequential and parallel computing. However, the multicore cluster systems, which are available now, wit

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Barreiro Megino, Fernando Harald, Jeffrey Ryan Albert, Frank Berghaus, et al. "Using Kubernetes as an ATLAS computing site." EPJ Web of Conferences 245 (2020): 07025. http://dx.doi.org/10.1051/epjconf/202024507025.

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In recent years containerization has revolutionized cloud environments, providing a secure, lightweight, standardized way to package and execute software. Solutions such as Kubernetes enable orchestration of containers in a cluster, including for the purpose of job scheduling. Kubernetes is becoming a de facto standard, available at all major cloud computing providers, and is gaining increased attention from some WLCG sites. In particular, CERN IT has integrated Kubernetes into their cloud infrastructure by providing an interface to instantly create Kubernetes clusters, and the University of V

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