Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for ga...Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.展开更多
Many latest high performance distributed computational environments come with high bandwidth in commu- nication. Such high bandwidth distributed systems provide unprecedented opportunities for analyzing huge datasets,...Many latest high performance distributed computational environments come with high bandwidth in commu- nication. Such high bandwidth distributed systems provide unprecedented opportunities for analyzing huge datasets, but simultaneously posts new technical challenges. For users, progressive query answering is important. For utility of systems, load balancing is critical. How we can achieve progressive and load balancing distributed computation is an interesting and promising research direction. As skyline analysis has been shown very useful in many multi-criteria decision making applications, in this paper, we study the problem of progressive and load balancing distributed skyline analysis. We propose a simple yet scalable approach which comes with several nice properties for progressive and load balancing query answering. We conduct extensive experiments which demonstrate the feasibility and effectiveness of the proposed method.展开更多
Recently,research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase.Physical expansion limits exist for tradition...Recently,research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase.Physical expansion limits exist for traditional standalone storage systems,such as I/O and file system capacity.However,the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data location.And this leads to system performance degradation due to low locality occurring in a Wide Area Network(WAN)environment with high network latency.This problem hinders deploying distributed storage systems to multiple data centers over WAN.It lowers the scalability of distributed storage systems to accommodate data storage needs.This paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system performance.The proposed distributed storage method monitors data utilization and locality to classify data temperature as hot,warm,and cold.With assigned data temperature,the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment,leading to overall system performance degradation.This paper also conducts simulations to evaluate the proposed and existing distributed storage methods.The result shows that our proposed method reduced latency by 38%compared to the existing method.Therefore,the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods,such as consistent hashing.展开更多
In LEO(Low Earth Orbit)satellite communication systems,the satellite network is made up of a large number of satellites,the dynamically changing network environment affects the results of distributed computing.In orde...In LEO(Low Earth Orbit)satellite communication systems,the satellite network is made up of a large number of satellites,the dynamically changing network environment affects the results of distributed computing.In order to improve the fault tolerance rate,a novel public blockchain consensus mechanism that applies a distributed computing architecture in a public network is proposed.Redundant calculation of blockchain ensures the credibility of the results;and the transactions with calculation results of a task are stored distributed in sequence in Directed Acyclic Graphs(DAG).The transactions issued by nodes are connected to form a net.The net can quickly provide node reputation evaluation that does not rely on third parties.Simulations show that our proposed blockchain has the following advantages:1.The task processing speed of the blockchain can be close to that of the fastest node in the entire blockchain;2.When the tasks’arrival time intervals and demanded working nodes(WNs)meet certain conditions,the network can tolerate more than 50%of malicious devices;3.No matter the number of nodes in the blockchain is increased or reduced,the network can keep robustness by adjusting the task’s arrival time interval and demanded WNs.展开更多
To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution scheme.However,the distributed machine learning is difficult to train due to that the ...To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution scheme.However,the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly,which highly demand on computing and memory resources.To overcome the challenges,we propose a distributed computing framework for L-BFGS optimization algorithm based on variance reduction method,which is a lightweight,few additional cost and parallelized scheme for the model training process.To validate the claims,we have conducted several experiments on multiple classical datasets.Results show that our proposed computing framework can steadily accelerate the training process of solver in either local mode or distributed mode.展开更多
Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interacti...Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.展开更多
Mobile agents provide a new method for the distributed computation. This paper presents the advantages of using mobile agents in a distributed virtual environment (DVE) system, and describes the architecture of hetero...Mobile agents provide a new method for the distributed computation. This paper presents the advantages of using mobile agents in a distributed virtual environment (DVE) system, and describes the architecture of heterogeneous computer's distributed virtual environment system (HCWES) designed to populate some mobile agents as well as stationary agents. Finally, the paper introduces how heterogeneous computer network communication is to be realized.展开更多
The title complex is widely used as an efficient key component of Ziegler-Natta catalyst for stereospecific polymerization of dienes to produce synthetic rubbers. However, the quantitative structure-activity relations...The title complex is widely used as an efficient key component of Ziegler-Natta catalyst for stereospecific polymerization of dienes to produce synthetic rubbers. However, the quantitative structure-activity relationship(QSAR) of this kind of complexes is still not clear mainly due to the difficulties to obtain their geometric molecular structures through laboratory experiments. An alternative solution is the quantum chemistry calculation in which the comformational population shall be determined. In this study, ten conformers of the title complex were obtained with the function of molecular dynamics conformational search in Gabedit 2.4.8, and their geometry optimization and thermodynamics calculation were made with a Sparkle/PM7 approach in MOPAC 2012. Their Gibbs free energies at 1 atm. and 298.15 K were calculated. Population of the conformers was further calculated out according to the theory of Boltzmann distribution, indicating that one of the ten conformers has a dominant population of 77.13%.展开更多
In the large-scale Distributed Virtual Environment(DVE)multimedia systems,one of key challenges is to distributedly preserve causal order delivery of messages in real time.Most of the existing causal order control app...In the large-scale Distributed Virtual Environment(DVE)multimedia systems,one of key challenges is to distributedly preserve causal order delivery of messages in real time.Most of the existing causal order control approaches with real-time constraints use vector time as causal control information which is closely coupled with system scales.As the scale expands,each message is attached a large amount of control information that introduces too much network transmission overhead to maintain the real-time causal order delivery.In this article,a novel Lightweight Real-Time Causal Order(LRTCO)algorithm is proposed for large-scale DVE multimedia systems.LRTCO predicts and compares the network transmission times of messages so as to select the proper causal control information of which the amount is dynamically adapted to the network latency variations and unconcerned with system scales.The control information in LRTCO is effective to preserve causal order delivery of messages and lightweight to maintain the real-time property of DVE systems.Experimental results demonstrate that LRTCO costs low transmission overhead and communication bandwidth,reduces causal order violations efficiently,and improves the scalability of DVE systems.展开更多
To meet the challenge of implementing rapidly advanced, time-consuming medical image processing algorithms, it is necessary to develop a medical image processing technology to process a 2D or 3D medical image dynamica...To meet the challenge of implementing rapidly advanced, time-consuming medical image processing algorithms, it is necessary to develop a medical image processing technology to process a 2D or 3D medical image dynamically on the web. But in a premier system, only static image processing can be provided with the limitation of web technology. The development of Java and CORBA (common object request broker architecture) overcomes the shortcoming of the web static application and makes the dynamic processing of medical images on the web available. To develop an open solution of distributed computing, we integrate the Java, and web with the CORBA and present a web-based medical image dynamic processing methed, which adopts Java technology as the language to program application and components of the web and utilies the CORBA architecture to cope with heterogeneous property of a complex distributed system. The method also provides a platform-independent, transparent processing architecture to implement the advanced image routines and enable users to access large dataset and resources according to the requirements of medical applications. The experiment in this paper shows that the medical image dynamic processing method implemented on the web by using Java and the CORBA is feasible.展开更多
This paper discusses the model of how the Agent is applied to implement distributed computing of Ada95 and presents a dynamic allocation strategy for distributed computing that based on pre-allocationand Agent. The ...This paper discusses the model of how the Agent is applied to implement distributed computing of Ada95 and presents a dynamic allocation strategy for distributed computing that based on pre-allocationand Agent. The aim of this strategy is realizing dynamic equilibrium allocation.展开更多
Distributed cryptographic computing system plays an important role since cryptographic computing is extremely computation sensitive. However, no general cryptographic computing system is available. Grid technology can...Distributed cryptographic computing system plays an important role since cryptographic computing is extremely computation sensitive. However, no general cryptographic computing system is available. Grid technology can give an efficient computational support for cryptographic applications. Therefore, a general-purpose grid-based distributed computing system called DCCS is put forward in this paper. The architecture of DCCS is simply described at first. The policy of task division adapted in DCCS is then presented. The method to manage subtask is further discussed in detail. Furthermore, the building and execution process of a computing job is revealed. Finally, the details of DCCS implementation under Globus Toolkit 4 are illustrated.展开更多
Using Java, Java enabled Web and object-oriented programming technologies, a framework is designed to or ganize multicornputer system on lntranet quickly to complete Monte Carlo simulation parallelizing, The high-perf...Using Java, Java enabled Web and object-oriented programming technologies, a framework is designed to or ganize multicornputer system on lntranet quickly to complete Monte Carlo simulation parallelizing, The high-performance computing enviromnent is embedded in Web server so it can be accessed more easily. Adaptive parallelism and eager scheduling algorithm are used to realize load balancing, parallel processing and system fault-tolerance. Independent sequence pseudo-randorn number generator schemes to keep the parallel simulation availability. Three kinds of stock option pricing models as instances, ideal speedup and pricing results obtained on test bed. Now, as a Web service, a high-performance financial derivative security-pricing platform is set up for training and studying. The framework can also be used to develop other SPMD (single procedure multiple data) application. Robustness is still a major problem for further research.展开更多
The goal of this paper is to present a versatile framework for solution verification of PDE's. We first generalize the Richardson Extrapolation technique to an optimized extrapolation solution procedure that construc...The goal of this paper is to present a versatile framework for solution verification of PDE's. We first generalize the Richardson Extrapolation technique to an optimized extrapolation solution procedure that constructs the best consistent solution from a set of two or three coarse grid solution in the discrete norm of choice. This technique generalizes the Least Square Extrapolation method introduced by one of the author and W. Shyy. We second establish the conditioning number of the problem in a reduced space that approximates the main feature of the numerical solution thanks to a sensitivity analysis. Overall our method produces an a posteriori error estimation in this reduced space of approximation. The key feature of our method is that our construction does not require an internal knowledge of the software neither the source code that produces the solution to be verified. It can be applied in principle as a postprocessing procedure to off the shelf commercial code. We demonstrate the robustness of our method with two steady problems that are separately an incompressible back step flow test case and a heat transfer problem for a battery. Our error estimate might be ultimately verified with a near by manufactured solution. While our pro- cedure is systematic and requires numerous computation of residuals, one can take advantage of distributed computing to get quickly the error estimate.展开更多
In many applications such as computational fluid dynamics and weather prediction, as well as image processing and state of Markov chain etc., the grade of matrix n is often very large, and any serial algorithm cannot ...In many applications such as computational fluid dynamics and weather prediction, as well as image processing and state of Markov chain etc., the grade of matrix n is often very large, and any serial algorithm cannot solve the problems. A distributed cluster-based solution for very large linear equations is discussed, it includes the definitions of notations, partition of matrix, communication mechanism, and a master-slaver algorithm etc., the computing cost is O(n^3/N), the memory cost is O(n^2/N), the I/O cost is O(n^2/N), and the com- munication cost is O(Nn ), here, N is the number of computing nodes or processes. Some tests show that the solution could solve the double type of matrix under 10^6 × 10^6 effectively.展开更多
In this paper, we adopt Java platform to achieve a multi-tier distributed object enterprise computing model which provides an open, flexible, robust and cross-platform standard for enterprise applications of new gener...In this paper, we adopt Java platform to achieve a multi-tier distributed object enterprise computing model which provides an open, flexible, robust and cross-platform standard for enterprise applications of new generation. In addition to this model, we define remote server objects as session or entity objects according to their roles in a distributed application server, which separate information details from business operations for software reuse. A web store system is implement by using this multi-tier distributed object enterprise computing model.展开更多
Many science and engineering applications involve solvinga linear least-squares system formed from some field measurements. In the distributed cyber-physical systems(CPS),each sensor node used for measurement often on...Many science and engineering applications involve solvinga linear least-squares system formed from some field measurements. In the distributed cyber-physical systems(CPS),each sensor node used for measurement often only knowspartial independent rows of the least-squares system. To solve the least-squares all the measurements must be gathered at a centralized location and then perform the computa-tion. Such data collection and computation are inefficient because of bandwidth and time constraints and sometimes areinfeasible because of data privacy concerns. Iterative methods are natural candidates for solving the aforementionedproblem and there are many studies regarding this. However,most of the proposed solutions are related to centralized/parallel computations while only a few have the potential to beapplied in distributed networks. Thus distributed computations are strongly preferred or demanded in many of the realworld applications, e.g. smart-grid, target tracking, etc. Thispaper surveys the representative iterative methods for distributed least-squares in networks.展开更多
The paper describes the use of invented,developed,and tested in different countries of the high-level spatial grasp model and technology capable of solving important problems in large social systems,which may be repre...The paper describes the use of invented,developed,and tested in different countries of the high-level spatial grasp model and technology capable of solving important problems in large social systems,which may be represented as dynamic,self-evolving and distributed social networks.The approach allows us to find important solutions on a holistic level by spatial navigation and parallel pattern matching of social networks with active self-propagating scenarios represented in a special recursive language.This approach effectively hides inside the distributed and networked language implementation traditional system management routines,often providing hundreds of times shorter and simpler high-level solution code.The paper highlights the demands to efficient simulation of social systems,briefs the technology used,and provides some programming examples for solutions of practical problems.展开更多
Today we witness the exponential growth of scientific research. This fast growth is possible thanks to the rapid development of computing systems since its first days in 1947 and the invention of transistor till the p...Today we witness the exponential growth of scientific research. This fast growth is possible thanks to the rapid development of computing systems since its first days in 1947 and the invention of transistor till the present days with high performance and scalable distributed computing systems. This fast growth of computing systems was first observed by Gordon E. Moore in 1965 and postulated as Moore’s Law. For the development of the scalable distributed computing systems, the year 2000 was a very special year. The first GHz speed processor, GB size memory and GB/s data transmission through network were achieved. Interestingly, in the same year the usable Grid computing systems emerged, which gave a strong impulse to a rapid development of distributed computing systems. This paper recognizes these facts that occurred in the year 2000, as the G-phenomena, a millennium cornerstone for the rapid development of scalable distributed systems evolved around the Grid and Cloud computing paradigms.展开更多
基金the Natural Science Foundation of Ningxia Province(No.2021AAC03230).
文摘Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.
基金Supported by the Doctoral Research Foundation of the Natural Science Foundation of Guangdong Province under Grant No.8451064101000054the National Natural Science Foundation of China under Grant Nos. 60773198,60703111+3 种基金Natural Science Foundation of Guangdong Province under Grant Nos. 06104916,8151027501000021Research Foundation of Science and Technology PlanProject in Guangdong Province under Grant No. 2008B050100040Program for New Century Excellent Talents in University ofChina under Grant No. NCET-06-0727the Fundamental Research Funds for the Central Universities,SCUT,under Grant No.2009ZM0008
文摘Many latest high performance distributed computational environments come with high bandwidth in commu- nication. Such high bandwidth distributed systems provide unprecedented opportunities for analyzing huge datasets, but simultaneously posts new technical challenges. For users, progressive query answering is important. For utility of systems, load balancing is critical. How we can achieve progressive and load balancing distributed computation is an interesting and promising research direction. As skyline analysis has been shown very useful in many multi-criteria decision making applications, in this paper, we study the problem of progressive and load balancing distributed skyline analysis. We propose a simple yet scalable approach which comes with several nice properties for progressive and load balancing query answering. We conduct extensive experiments which demonstrate the feasibility and effectiveness of the proposed method.
基金This research was supported by the Chung-Ang University Graduate Research Scholarship in 2021.This study was carried out with the support of‘R&D Program for Forest Science Technology(Project No.2021338C10-2223-CD02)’provided by Korea Forest Service(Korea Forestry Promotion Institute).
文摘Recently,research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase.Physical expansion limits exist for traditional standalone storage systems,such as I/O and file system capacity.However,the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data location.And this leads to system performance degradation due to low locality occurring in a Wide Area Network(WAN)environment with high network latency.This problem hinders deploying distributed storage systems to multiple data centers over WAN.It lowers the scalability of distributed storage systems to accommodate data storage needs.This paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system performance.The proposed distributed storage method monitors data utilization and locality to classify data temperature as hot,warm,and cold.With assigned data temperature,the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment,leading to overall system performance degradation.This paper also conducts simulations to evaluate the proposed and existing distributed storage methods.The result shows that our proposed method reduced latency by 38%compared to the existing method.Therefore,the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods,such as consistent hashing.
基金funded in part by the National Natural Science Foundation of China (Grant no. 61772352, 62172061, 61871422)National Key Research and Development Project (Grants nos. 2020YFB1711800 and 2020YFB1707900)+2 种基金the Science and Technology Project of Sichuan Province (Grants no. 2021YFG0152, 2021YFG0025, 2020YFG0479, 2020YFG0322, 2020GFW035, 2020GFW033, 2020YFH0071)the R&D Project of Chengdu City (Grant no. 2019-YF05-01790-GX)the Central Universities of Southwest Minzu University (Grants no. ZYN2022032)
文摘In LEO(Low Earth Orbit)satellite communication systems,the satellite network is made up of a large number of satellites,the dynamically changing network environment affects the results of distributed computing.In order to improve the fault tolerance rate,a novel public blockchain consensus mechanism that applies a distributed computing architecture in a public network is proposed.Redundant calculation of blockchain ensures the credibility of the results;and the transactions with calculation results of a task are stored distributed in sequence in Directed Acyclic Graphs(DAG).The transactions issued by nodes are connected to form a net.The net can quickly provide node reputation evaluation that does not rely on third parties.Simulations show that our proposed blockchain has the following advantages:1.The task processing speed of the blockchain can be close to that of the fastest node in the entire blockchain;2.When the tasks’arrival time intervals and demanded working nodes(WNs)meet certain conditions,the network can tolerate more than 50%of malicious devices;3.No matter the number of nodes in the blockchain is increased or reduced,the network can keep robustness by adjusting the task’s arrival time interval and demanded WNs.
基金partly supported by National Key Basic Research Program of China(2016YFB1000100)partly supported by National Natural Science Foundation of China(NO.61402490)。
文摘To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution scheme.However,the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly,which highly demand on computing and memory resources.To overcome the challenges,we propose a distributed computing framework for L-BFGS optimization algorithm based on variance reduction method,which is a lightweight,few additional cost and parallelized scheme for the model training process.To validate the claims,we have conducted several experiments on multiple classical datasets.Results show that our proposed computing framework can steadily accelerate the training process of solver in either local mode or distributed mode.
基金This work was supported in part by the National Natural Science Foundation of China(61772493)the CAAI-Huawei MindSpore Open Fund(CAAIXSJLJJ-2020-004B)+4 种基金the Natural Science Foundation of Chongqing(China)(cstc2019jcyjjqX0013)Chongqing Research Program of Technology Innovation and Application(cstc2019jscx-fxydX0024,cstc2019jscx-fxydX0027,cstc2018jszx-cyzdX0041)Guangdong Province Universities and College Pearl River Scholar Funded Scheme(2019)the Pioneer Hundred Talents Program of Chinese Academy of Sciencesthe Deanship of Scientific Research(DSR)at King Abdulaziz University(G-21-135-38).
文摘Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.
文摘Mobile agents provide a new method for the distributed computation. This paper presents the advantages of using mobile agents in a distributed virtual environment (DVE) system, and describes the architecture of heterogeneous computer's distributed virtual environment system (HCWES) designed to populate some mobile agents as well as stationary agents. Finally, the paper introduces how heterogeneous computer network communication is to be realized.
基金supported by the National Natural Science Foundation of China(No.21476119)
文摘The title complex is widely used as an efficient key component of Ziegler-Natta catalyst for stereospecific polymerization of dienes to produce synthetic rubbers. However, the quantitative structure-activity relationship(QSAR) of this kind of complexes is still not clear mainly due to the difficulties to obtain their geometric molecular structures through laboratory experiments. An alternative solution is the quantum chemistry calculation in which the comformational population shall be determined. In this study, ten conformers of the title complex were obtained with the function of molecular dynamics conformational search in Gabedit 2.4.8, and their geometry optimization and thermodynamics calculation were made with a Sparkle/PM7 approach in MOPAC 2012. Their Gibbs free energies at 1 atm. and 298.15 K were calculated. Population of the conformers was further calculated out according to the theory of Boltzmann distribution, indicating that one of the ten conformers has a dominant population of 77.13%.
基金This research work is supported by Hunan Provincial Natural Science Foundation of China(Grant No.2017JJ2016)Hunan Provincial Education Science 13th Five-Year Plan(Grant No.XJK016BXX001)+3 种基金Social Science Foundation of Hunan Province(Grant No.17YBA049)2017 Hunan Provincial Higher Education Teaching Re-form Research Project(Grant No.564)Scientific Research Fund of Hunan Provin-cial Education Department(Grant No.16C0269 and No.17B046)The work is also sup-ported by Open foundation for University Innovation Platform from Hunan Province,China(Grand No.16K013)and the 2011 Collaborative Innovation Center of Big Data for Finan-cial and Economical Asset Development and Utility in Universities of Hunan Province.We also thank the anonymous reviewers for their valuable comments and insightful sug-gestions.
文摘In the large-scale Distributed Virtual Environment(DVE)multimedia systems,one of key challenges is to distributedly preserve causal order delivery of messages in real time.Most of the existing causal order control approaches with real-time constraints use vector time as causal control information which is closely coupled with system scales.As the scale expands,each message is attached a large amount of control information that introduces too much network transmission overhead to maintain the real-time causal order delivery.In this article,a novel Lightweight Real-Time Causal Order(LRTCO)algorithm is proposed for large-scale DVE multimedia systems.LRTCO predicts and compares the network transmission times of messages so as to select the proper causal control information of which the amount is dynamically adapted to the network latency variations and unconcerned with system scales.The control information in LRTCO is effective to preserve causal order delivery of messages and lightweight to maintain the real-time property of DVE systems.Experimental results demonstrate that LRTCO costs low transmission overhead and communication bandwidth,reduces causal order violations efficiently,and improves the scalability of DVE systems.
基金This project was supported by the National Natural Science Foundation of China (69931010).
文摘To meet the challenge of implementing rapidly advanced, time-consuming medical image processing algorithms, it is necessary to develop a medical image processing technology to process a 2D or 3D medical image dynamically on the web. But in a premier system, only static image processing can be provided with the limitation of web technology. The development of Java and CORBA (common object request broker architecture) overcomes the shortcoming of the web static application and makes the dynamic processing of medical images on the web available. To develop an open solution of distributed computing, we integrate the Java, and web with the CORBA and present a web-based medical image dynamic processing methed, which adopts Java technology as the language to program application and components of the web and utilies the CORBA architecture to cope with heterogeneous property of a complex distributed system. The method also provides a platform-independent, transparent processing architecture to implement the advanced image routines and enable users to access large dataset and resources according to the requirements of medical applications. The experiment in this paper shows that the medical image dynamic processing method implemented on the web by using Java and the CORBA is feasible.
文摘This paper discusses the model of how the Agent is applied to implement distributed computing of Ada95 and presents a dynamic allocation strategy for distributed computing that based on pre-allocationand Agent. The aim of this strategy is realizing dynamic equilibrium allocation.
基金Supported by the National Basic Research Program of China (973 Program 2004CB318004), the National Natural Science Foundation of China (NSFC90204016) and the National High Technology Research and Development Program of China (2003AA144030)
文摘Distributed cryptographic computing system plays an important role since cryptographic computing is extremely computation sensitive. However, no general cryptographic computing system is available. Grid technology can give an efficient computational support for cryptographic applications. Therefore, a general-purpose grid-based distributed computing system called DCCS is put forward in this paper. The architecture of DCCS is simply described at first. The policy of task division adapted in DCCS is then presented. The method to manage subtask is further discussed in detail. Furthermore, the building and execution process of a computing job is revealed. Finally, the details of DCCS implementation under Globus Toolkit 4 are illustrated.
基金Supported by the National 863 High TechnologyDevelopment of China (2001AA111081)
文摘Using Java, Java enabled Web and object-oriented programming technologies, a framework is designed to or ganize multicornputer system on lntranet quickly to complete Monte Carlo simulation parallelizing, The high-performance computing enviromnent is embedded in Web server so it can be accessed more easily. Adaptive parallelism and eager scheduling algorithm are used to realize load balancing, parallel processing and system fault-tolerance. Independent sequence pseudo-randorn number generator schemes to keep the parallel simulation availability. Three kinds of stock option pricing models as instances, ideal speedup and pricing results obtained on test bed. Now, as a Web service, a high-performance financial derivative security-pricing platform is set up for training and studying. The framework can also be used to develop other SPMD (single procedure multiple data) application. Robustness is still a major problem for further research.
基金Sandia Nat.Lab.Sandia is a multiprogram laboratory operated by Sandia Corporation,a Lockheed Martin Company,for the United States Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000
文摘The goal of this paper is to present a versatile framework for solution verification of PDE's. We first generalize the Richardson Extrapolation technique to an optimized extrapolation solution procedure that constructs the best consistent solution from a set of two or three coarse grid solution in the discrete norm of choice. This technique generalizes the Least Square Extrapolation method introduced by one of the author and W. Shyy. We second establish the conditioning number of the problem in a reduced space that approximates the main feature of the numerical solution thanks to a sensitivity analysis. Overall our method produces an a posteriori error estimation in this reduced space of approximation. The key feature of our method is that our construction does not require an internal knowledge of the software neither the source code that produces the solution to be verified. It can be applied in principle as a postprocessing procedure to off the shelf commercial code. We demonstrate the robustness of our method with two steady problems that are separately an incompressible back step flow test case and a heat transfer problem for a battery. Our error estimate might be ultimately verified with a near by manufactured solution. While our pro- cedure is systematic and requires numerous computation of residuals, one can take advantage of distributed computing to get quickly the error estimate.
文摘In many applications such as computational fluid dynamics and weather prediction, as well as image processing and state of Markov chain etc., the grade of matrix n is often very large, and any serial algorithm cannot solve the problems. A distributed cluster-based solution for very large linear equations is discussed, it includes the definitions of notations, partition of matrix, communication mechanism, and a master-slaver algorithm etc., the computing cost is O(n^3/N), the memory cost is O(n^2/N), the I/O cost is O(n^2/N), and the com- munication cost is O(Nn ), here, N is the number of computing nodes or processes. Some tests show that the solution could solve the double type of matrix under 10^6 × 10^6 effectively.
文摘In this paper, we adopt Java platform to achieve a multi-tier distributed object enterprise computing model which provides an open, flexible, robust and cross-platform standard for enterprise applications of new generation. In addition to this model, we define remote server objects as session or entity objects according to their roles in a distributed application server, which separate information details from business operations for software reuse. A web store system is implement by using this multi-tier distributed object enterprise computing model.
基金partially supported by US NSF under Grant No.NSF-CNS-1066391and No.NSF-CNS-0914371,NSF-CPS-1135814 and NSF-CDI-1125165
文摘Many science and engineering applications involve solvinga linear least-squares system formed from some field measurements. In the distributed cyber-physical systems(CPS),each sensor node used for measurement often only knowspartial independent rows of the least-squares system. To solve the least-squares all the measurements must be gathered at a centralized location and then perform the computa-tion. Such data collection and computation are inefficient because of bandwidth and time constraints and sometimes areinfeasible because of data privacy concerns. Iterative methods are natural candidates for solving the aforementionedproblem and there are many studies regarding this. However,most of the proposed solutions are related to centralized/parallel computations while only a few have the potential to beapplied in distributed networks. Thus distributed computations are strongly preferred or demanded in many of the realworld applications, e.g. smart-grid, target tracking, etc. Thispaper surveys the representative iterative methods for distributed least-squares in networks.
文摘The paper describes the use of invented,developed,and tested in different countries of the high-level spatial grasp model and technology capable of solving important problems in large social systems,which may be represented as dynamic,self-evolving and distributed social networks.The approach allows us to find important solutions on a holistic level by spatial navigation and parallel pattern matching of social networks with active self-propagating scenarios represented in a special recursive language.This approach effectively hides inside the distributed and networked language implementation traditional system management routines,often providing hundreds of times shorter and simpler high-level solution code.The paper highlights the demands to efficient simulation of social systems,briefs the technology used,and provides some programming examples for solutions of practical problems.
基金in part,supported by the European Commission through the EU FP7 SEE GRID SCI and SCI BUS projectsby the Grant 098-0982562-2567 awarded by the Ministry of Science,Education and Sports of the Republic of Croatia.
文摘Today we witness the exponential growth of scientific research. This fast growth is possible thanks to the rapid development of computing systems since its first days in 1947 and the invention of transistor till the present days with high performance and scalable distributed computing systems. This fast growth of computing systems was first observed by Gordon E. Moore in 1965 and postulated as Moore’s Law. For the development of the scalable distributed computing systems, the year 2000 was a very special year. The first GHz speed processor, GB size memory and GB/s data transmission through network were achieved. Interestingly, in the same year the usable Grid computing systems emerged, which gave a strong impulse to a rapid development of distributed computing systems. This paper recognizes these facts that occurred in the year 2000, as the G-phenomena, a millennium cornerstone for the rapid development of scalable distributed systems evolved around the Grid and Cloud computing paradigms.