A multi-scale modeling approach is employed for the study of the effect of oleic-acid (OA) coverage on the magnetic behaviour of Co ferrite nanoparticles (CFNs), using high performance computing (HPC). Our study is pe...
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With the advent of the era of big data, the traditional stand-alone computing model can no longer meet the needs of complex network analysis, and the distributed computing model provides a new solution. This paper fir...
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ISBN:
(数字)9798331536169
ISBN:
(纸本)9798331536176
With the advent of the era of big data, the traditional stand-alone computing model can no longer meet the needs of complex network analysis, and the distributed computing model provides a new solution. This paper first introduces the importance of complex network analysis and the challenges brought by big data, then discusses the application of distributed computing model in complex network analysis in detail, and focuses on the distributed computing algorithm based on MapReduce. Then, this paper puts forward an optimization strategy of distributed community detection algorithm based on modularity optimization. This strategy performs community detection tasks in parallel through MapReduce framework, and optimizes community division through iterative updating to maximize modularity. The experimental results show that the optimized algorithm has a significant improvement in computing time, accuracy and resource consumption. The distributed computing model based on MapReduce framework and its optimization strategy have obvious advantages in dealing with large-scale complex networks. It can not only improve the calculation efficiency and accuracy, but also reduce the resource consumption, which provides an efficient and feasible solution for complex network analysis.
When all the qubits needed for solving a problem are not located in a single quantum computer, qubits from different quantum computers can be collectively utilized. In this case, quantum communication is needed for th...
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ISBN:
(数字)9798331531591
ISBN:
(纸本)9798331531607
When all the qubits needed for solving a problem are not located in a single quantum computer, qubits from different quantum computers can be collectively utilized. In this case, quantum communication is needed for the multiple quantum computers to communicate with each other. Several studies address the problem of minimizing the number of quantum communications when evaluating a general quantum circuit. The solutions proposed typically involve solving some intractable problems. In this paper, we show that we can obtain much better solutions when we focus on solving specific problems (instead of seeking solutions for generic circuits). Specifically, we consider several fundamental quantum circuits and identify communication protocols that need a much smaller number of communication steps than those offered by generic solutions. Our work is in line with traditional parallel and distributed computing research where typically scientists focus on solving specific problems (such as sorting, matrix multiplication, network flow, etc.) in a parallel or distributed setting.
In contemporary social networks, dynamic privacy protection remains a pivotal yet challenging endeavor due to the intricate and evolving nature of information exchange. Traditional privacy models, predominantly static...
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Transitive closure computation is a fundamental operation in graph theory with applications in various domains. However, the increasing size and complexity of real-world graphs make traditional algorithms inefficient,...
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ISBN:
(数字)9798331524937
ISBN:
(纸本)9798331524944
Transitive closure computation is a fundamental operation in graph theory with applications in various domains. However, the increasing size and complexity of real-world graphs make traditional algorithms inefficient, especially when dealing with large datasets. This paper investigates the optimisation of transitive closure algorithms for high performance computing (HPC) applications. We implement and compare three different methods for computing the adjacency matrix of the transitive closure, based on three different Python libraries (networkX, PyTorch and NumPy). Our approach is benchmarked on seven real-world datasets of varying size and density to evaluate performance and scalability. The results show that NumPy achieves the best performance for large and dense graphs. The paper concludes with a discussion of the potential benefits of algorithmic optimization in HPC and security.
This paper introduces an application of Grover’s algorithm to optimize neural network training by eliminating the computationally demanding backward propagation. It clarifies previous assertions regarding training ne...
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ISBN:
(数字)9798331531591
ISBN:
(纸本)9798331531607
This paper introduces an application of Grover’s algorithm to optimize neural network training by eliminating the computationally demanding backward propagation. It clarifies previous assertions regarding training neural networks with Grover’s algorithm. The paper introduces parallel data training and training on exponentially seeded datasets. A quantum binary neural network is created to more efficiently implement neural networks on quantum computers. This trained discriminatory quantum binary neural network is shown to behave as a generative quantum binary neural network. We present methods that both simplify and enhance the process of neural network development and training through Grover’s algorithm. The quantum binary neural network has the potential to far outpace classical neural network training in terms of training time and energy.
This paper presents SPARE, a novel serverless platform that supports self-adaptive resource allocation and reconfiguration, thereby increasing the availability of computing resources for time-critical tasks in urgent ...
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ISBN:
(数字)9798331524937
ISBN:
(纸本)9798331524944
This paper presents SPARE, a novel serverless platform that supports self-adaptive resource allocation and reconfiguration, thereby increasing the availability of computing resources for time-critical tasks in urgent events. In emergency scenarios, SPARE reallocates resources by forwarding serverless function invocations to the nearest edge nodes having sufficient capacity. Additionally, the platform employs the use of unikernels and lightweight virtualization through Firecracker, which helps to reduce cold start times and improve function responsiveness. The experimental results demonstrate that SPARE is capable of releasing up to one-third of edge nodes within a serverless edge platform, while only experiencing a mild increase in latency, thus maintaining service continuity.
Transitive closure computation is a fundamental operation in graph theory with applications in various domains. However, the increasing size and complexity of real-world graphs make traditional algorithms inefficient,...
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Synthetic Aperture Radar (SAR) tomography is an advanced technique for monitoring deformations of the Earth's surface. However, the computational complexity of SAR tomography algorithms often restricts their appli...
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FBFC (Flit Bubble Flow Control) and Dateline flow control are two commonly used flow control mechanisms in Torus networks. However, each of these two flow control mechanisms has its own performance limitations. The bu...
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