scholarly journals Federal Synergy Computing Model Based on Network Interconnection

2020 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Large Scale ◽  
Heterogeneous Computing ◽  
Computation Time ◽  
Research Problem ◽  
Computing System ◽  
The Internet ◽  
Collaborative Computing ◽  
Computing Power ◽  
Network Interconnection ◽  
Computing Model

To solve the shortage problem of the computing power provided by the single machine or the small cluster system in scientific research, we offer a collaborative computing system for users. This system has massive operation ability. It introduced a scalable mixed collaborative computing model. Through the internet and the heterogeneous computing equipment, the system uses the task decomposition model. This system can solve the research and development problem because of the shortage of capacity. To test the model, a subtask decomposition example is used. The results of the example analysis show that the computing work can obtain the shortest computation time when the number of calculation nodes is more than the number of subtasks; Maximum calculation efficiency can be achieved when the number of the calculating nodes closes to the number of subtasks. Through joint collaborative computing, the extensible mixed collaborative computing mode can effectively solve the mass computing problem for the system with heterogeneous hardware and software. This paper provides the reference for the system, which provides large scale computing power through the Internet and the research problem of due to the lack of computing ability.

scholarly journals Federal Synergy Computing Model Based on Network Interconnection

2020 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Large Scale ◽  
Heterogeneous Computing ◽  
Computation Time ◽  
Research Problem ◽  
Computing System ◽  
The Internet ◽  
Collaborative Computing ◽  
Computing Power ◽  
Network Interconnection ◽  
Computing Model

Abstract: To solve the shortage problem of the computing power provided by the single machine or the small cluster system in scientific research, we offer a collaborative computing system for users. This system has massive operation ability. It introduced a scalable mixed collaborative computing model. Through the internet and the heterogeneous computing equipment, the system uses the task decomposition model. This system can solve the research and development problem because of the shortage of capacity. To test the model, a subtask decomposition example is used. The results of the example analysis show that the computing work can obtain the shortest computation time when the number of calculation nodes is more than the number of subtasks; Maximum calculation efficiency can be achieved when the number of the calculating nodes closes to the number of subtasks. Through joint collaborative computing, the extensible mixed collaborative computing mode can effectively solve the mass computing problem for the system with heterogeneous hardware and software. This paper provides the reference for the system, which provides large scale computing power through the Internet and the research problem of due to the lack of computing ability.

scholarly journals Federal Synergy Computing Model Based on Network Interconnection

2020 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Large Scale ◽  
Heterogeneous Computing ◽  
Computation Time ◽  
Research Problem ◽  
Computing System ◽  
The Internet ◽  
Collaborative Computing ◽  
Computing Power ◽  
Network Interconnection ◽  
Computing Model

To solve the shortage problem of the computing power provided by the single machine or the small cluster system in scientific research, we offer a collaborative computing system for users. This system has massive operation ability. It introduced a scalable mixed collaborative computing model. Through the internet and the heterogeneous computing equipment, the system uses the task decomposition model. This system can solve the research and development problem because of the shortage of capacity. To test the model, a subtask decomposition example is used. The results of the example analysis show that the computing work can obtain the shortest computation time when the number of calculation nodes is more than the number of subtasks; Maximum calculation efficiency can be achieved when the number of the calculating nodes closes to the number of subtasks. Through joint collaborative computing, the extensible mixed collaborative computing mode can effectively solve the mass computing problem for the system with heterogeneous hardware and software. This paper provides the reference for the system, which provides large scale computing power through the Internet and the research problem of due to the lack of computing ability.

scholarly journals Federal Synergy Computing Model Based on Network Interconnection

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Large Scale ◽  
Heterogeneous Computing ◽  
Computation Time ◽  
Research Problem ◽  
Computing System ◽  
The Internet ◽  
Collaborative Computing ◽  
Computing Power ◽  
Network Interconnection ◽  
Computing Model

To solve the shortage problem of the computing power provided by the single machine or the small cluster system in scientific research, we offer a collaborative computing system for users. This system has massive operation ability. It introduced a scalable mixed collaborative computing model. Through the internet and the heterogeneous computing equipment, the system uses the task decomposition model. This system can solve the research and development problem because of the shortage of capacity. To test the model, a subtask decomposition example is used. The results of the example analysis show that the computing work can obtain the shortest computation time when the number of calculation nodes is more than the number of subtasks; Maximum calculation efficiency can be achieved when the number of the calculating nodes closes to the number of subtasks. Through joint collaborative computing, the extensible mixed collaborative computing mode can effectively solve the mass computing problem for the system with heterogeneous hardware and software. This paper provides the reference for the system, which provides large scale computing power through the Internet and the research problem of due to the lack of computing ability.

Social Network

2012 ◽  
pp. 8-24
Author(s):  
Roby Muhamad
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
The Internet ◽  
Computing Power ◽  
Network Research ◽  
Social Entities

Social network concerns the study of the structure of the patterns of relations among social entities. The study of social networks has a long history starting around 1930s when psychologist Moreno conducted the first known sociometric survey. Since then, the field of social network, first developed in sociology, has grown both empirically and theoretically, especially toward the end of the last century. The advent of powerful computing power and the Internet spurred growth on social network research. This combination of the proliferation of digital traces and increases in computing power provides opportunities to study large scale social networks and relevant dynamics.

A Lightweight Distributed Computing System Based on JavaScript Technology

2010 ◽  
Vol 34-35 ◽  
pp. 1911-1915
Author(s):  
Jun Tang
Keyword(s):  
Distributed Computing ◽  
Information Exchange ◽  
Large Scale ◽  
Computing System ◽  
The Internet ◽  
Computational Problem ◽  
Intermediary Role ◽  
Computational Platform ◽  
It Expert ◽  
The Web

Because the web is not only the platform for information exchange but also the computational platform based on JavaScript engine, every computer having installed modern browser on the Internet can easily access the web and execute some JavaScript programs. Under above conditions, we develop a lightweight distributed computing system based on the web and JavaScript technologies. Our system plays an intermediary role between the IT expert who has to solve large-scale computational problem and end users on the Internet. In the other words, people could easily cooperate with each other to finish complicated computational problem through the support of our system.

scholarly journals Synthesis of computer simulation and machine learning for achieving the best material properties of filled rubber

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Takashi Kojima ◽  
Takashi Washio ◽  
Satoshi Hara ◽  
Masataka Koishi
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Surrogate Model ◽  
Large Scale ◽  
Computation Time ◽  
Training Data ◽  
3D Images ◽  
Computing Power ◽  
Filled Rubber ◽  
Regional Stresses

Abstract Molecular dynamics (MD) simulation is used to analyze the mechanical properties of polymerized and nanoscale filled rubber. Unfortunately, the computation time for a simulation can require several months’ computing power, because the interactions of thousands of filler particles must be calculated. To alleviate this problem, we introduce a surrogate convolutional neural network model to achieve faster and more accurate predictions. The major difficulty when employing machine-learning-based surrogate models is the shortage of training data, contributing to the huge simulation costs. To derive a highly accurate surrogate model using only a small amount of training data, we increase the number of training instances by dividing the large-scale simulation results into 3D images of middle-scale filler morphologies and corresponding regional stresses. The images include fringe regions to reflect the influence of the filler constituents outside the core regions. The resultant surrogate model provides higher prediction accuracy than that trained only by images of the entire region. Afterwards, we extract the fillers that dominate the mechanical properties using the surrogate model and we confirm their validity using MD.

Double Precision Is Not Needed for Many-Body Calculations: New Conventional Wisdom

2018 ◽  
Author(s):  
Pavel Pokhilko ◽  
Evgeny Epifanovsky ◽  
Anna I. Krylov
Keyword(s):  
Large Scale ◽  
Computation Time ◽  
Coupled Cluster ◽  
Double Precision ◽  
Many Body ◽  
Single Precision ◽  
Parallel Performance ◽  
Point Representation ◽  
Electron Repulsion Integrals ◽  
Cluster Methods

Using single precision floating point representation reduces the size of data and computation time by a factor of two relative to double precision conventionally used in electronic structure programs. For large-scale calculations, such as those encountered in many-body theories, reduced memory footprint alleviates memory and input/output bottlenecks. Reduced size of data can lead to additional gains due to improved parallel performance on CPUs and various accelerators. However, using single precision can potentially reduce the accuracy of computed observables. Here we report an implementation of coupled-cluster and equation-of-motion coupled-cluster methods with single and double excitations in single precision. We consider both standard implementation and one using Cholesky decomposition or resolution-of-the-identity of electron-repulsion integrals. Numerical tests illustrate that when single precision is used in correlated calculations, the loss of accuracy is insignificant and pure single-precision implementation can be used for computing energies, analytic gradients, excited states, and molecular properties. In addition to pure single-precision calculations, our implementation allows one to follow a single-precision calculation by clean-up iterations, fully recovering double-precision results while retaining significant savings.

scholarly journals Sensor System: A Survey of Sensor Type, Ad Hoc Network Topology and Energy Harvesting Techniques

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 219
Author(s):  
Phuoc Duc Nguyen ◽  
Lok-won Kim
Keyword(s):  
Energy Harvesting ◽  
Large Scale ◽  
Ad Hoc ◽  
Sensor Nodes ◽  
Sensor System ◽  
Wireless Sensor ◽  
The Internet ◽  
Original Research ◽  
Energy Scavenging ◽  
Term Operation

People nowadays are entering an era of rapid evolution due to the generation of massive amounts of data. Such information is produced with an enormous contribution from the use of billions of sensing devices equipped with in situ signal processing and communication capabilities which form wireless sensor networks (WSNs). As the number of small devices connected to the Internet is higher than 50 billion, the Internet of Things (IoT) devices focus on sensing accuracy, communication efficiency, and low power consumption because IoT device deployment is mainly for correct information acquisition, remote node accessing, and longer-term operation with lower battery changing requirements. Thus, recently, there have been rich activities for original research in these domains. Various sensors used by processing devices can be heterogeneous or homogeneous. Since the devices are primarily expected to operate independently in an autonomous manner, the abilities of connection, communication, and ambient energy scavenging play significant roles, especially in a large-scale deployment. This paper classifies wireless sensor nodes into two major categories based the types of the sensor array (heterogeneous/homogeneous). It also emphasizes on the utilization of ad hoc networking and energy harvesting mechanisms as a fundamental cornerstone to building a self-governing, sustainable, and perpetually-operated sensor system. We review systems representative of each category and depict trends in system development.

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