HPC is a widely used term, often referred to the applications, architectures and programming models and tools targeting highly parallel machines such as those of the *** lists. Recent advances in computing hardware re...
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An innovative approach to password cracking by leveraging a distributedcomputing model is developed. The sys- tem comprises a client-server architecture where clients receive segmented password ranges for parallelize...
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An innovative approach to password cracking by leveraging a distributedcomputing model is developed. The sys- tem comprises a client-server architecture where clients receive segmented password ranges for parallelized hashing attempts. The Java-based implementation employs MD5 hashing and divides the password into smaller units for distribution among multiple clients. Each client independently processes its assigned password range, attempting to match the hash against a pre- determined target. The server orchestrates the distribution of password segments and collects results from clients, facilitating the cracking process. Security measures include secure communication protocols, and ethical considerations center around legality, user consent, and emphasizing the educational value of responsible hacking practices. The work explores the tech- nical challenges of distributed password cracking, addressing efficiency, scalability, and security implications, while fostering a deeper understanding of cybersecurity and distributed systems.
distributed large memory offers the use of large virtual memory by using remote memory distributed over nodes in a cluster. The message passing interface (MPI) plays important role in DLM. MPI-based DLM manages the la...
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With the launch of the National Energy Administration's rooftop photovoltaic pilot construction in all counties and cities, power companies in various provinces and cities of the State grid are facing severe safet...
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ISBN:
(数字)9783031518263
ISBN:
(纸本)9783031518256;9783031518263
With the launch of the National Energy Administration's rooftop photovoltaic pilot construction in all counties and cities, power companies in various provinces and cities of the State grid are facing severe safety management challenges. For example, distributed power sources such as photovoltaic and wind power are affected by natural weather, and their output is intermittent and difficult to control. Large-scale integration into the power system will have an impact on the power system, making it unable to operate normally, and even leading to power system collapse and damage to power ***, it is necessary to form a low-cost solution that meets the basic requirements of stable and safe operation of the power grid. In this paper, we propose a regulation scheme of low-voltage distributed generation, which uses the edge computing capability deployed in the distribution transformer fusion terminal, and the communication capability of power line carrier and the intelligent circuit breaker integrated with function fusion to realize the observation and control of distributed energy and electrical equipment. The proposed scheme has been piloted and verified in two typical power distribution areas in Shenyang, and the pilot application has achieved the expected goals.
Electric power computing resources are distributed in the respective regions of provincial data centers and transmission and transformation equipment, and the scheduling efficiency of computing resources is limited by...
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In distributed training, deep neural networks (DNNs) are launched over multiple workers concurrently and aggregate their local updates on each step in bulk-synchronous parallel (BSP) training. However, BSP does not li...
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ISBN:
(纸本)9798350307924
In distributed training, deep neural networks (DNNs) are launched over multiple workers concurrently and aggregate their local updates on each step in bulk-synchronous parallel (BSP) training. However, BSP does not linearly scale-out due to high communication cost of aggregation. To mitigate this overhead, alternatives like Federated Averaging (FedAvg) and Stale-Synchronous parallel (SSP) either reduce synchronization frequency or eliminate it altogether, usually at the cost of lower final accuracy. In this paper, we present SelSync, a practical, low-overhead method for DNN training that dynamically chooses to incur or avoid communication at each step either by calling the aggregation op or applying local updates based on their significance. We propose various optimizations as part of SelSync to improve convergence in the context of semi-synchronous training. Our system converges to the same or better accuracy than BSP while reducing training time by up to 14x.
The widespread utilization of Internet of Things (IoT) devices has resulted in an exponential increase in data at the Internet's edges. This trend, combined with the rapid growth of machine learning (ML) applicati...
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ISBN:
(纸本)9798350369458;9798350369441
The widespread utilization of Internet of Things (IoT) devices has resulted in an exponential increase in data at the Internet's edges. This trend, combined with the rapid growth of machine learning (ML) applications, necessitates the execution of learning tasks across the entire spectrum of computing resources - from the device, to the edge, to the cloud. This paper investigates the execution of machine learning algorithms within the edge-cloud continuum, focusing on their implications from a distributedcomputing perspective. We explore the integration of traditional ML algorithms, leveraging edge computing benefits such as low-latency processing and privacy preservation, along with cloud computing capabilities offering virtually limitless computational and storage resources. Our analysis offers insights into optimizing the execution of machine learning applications by decomposing them into smaller components and distributing these across processing nodes in edge-cloud architectures. By utilizing the Apache Spark framework, we define an efficient task allocation solution for distributing ML tasks across edge and cloud layers. Experiments on a clustering application in an edgecloud setup confirm the effectiveness of our solution compared to highly centralized alternatives, in which cloud resources are extensively used for handling large volumes of data from IoT devices.
To enhance the high-reliability operation capability of reconfigurable battery energy storage systems, a distributed cooperative control method of reconfigurable battery energy storage based on consensus algorithms is...
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ISBN:
(纸本)9798350377477;9798350377460
To enhance the high-reliability operation capability of reconfigurable battery energy storage systems, a distributed cooperative control method of reconfigurable battery energy storage based on consensus algorithms is proposed in this paper, which can improve the frequency and power characteristics of the system. Firstly, the mathematical model of virtual synchronous generator(VSG) control, the structure of reconfigurable battery energy storage system, and the parallel VSG distributed architecture are introduced;Secondly, combined with the mathematical model of VSG control, the overall scheme of frequency recovery and power output reasonable distribution of VSG parallel system is designed;Then, according to the structural characteristics of reconfigurable battery energy storage, a battery pack reconfiguration strategy is proposed, which can meet the power output demand through the free combination of battery packs;Finally, a simulation model is built in Matlab/Simulink environment, and the simulation results verify the correctness and effectiveness of the proposed method.
Many parallel and distributedcomputing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for par...
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ISBN:
(纸本)9798350364613;9798350364606
Many parallel and distributedcomputing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for parameters that define the behavior of the underlying simulation models it implements. The main concern for a simulator is accuracy: simulated behaviors should be as close as possible to those observed in the real-world target system. This requires that values for each of the simulator's parameters be carefully picked, or "calibrated," based on ground truth real-world executions. Examining the current state of the art shows that simulator calibration, at least in the field of parallel and distributedcomputing, is often undocumented (and thus perhaps often not performed) and, when documented, is described as a labor-intensive, manual process. In this work we evaluate the benefit of automating simulation calibration using simple algorithms. Specifically, we use a real-world case study from the field of High Energy Physics and compare automated calibration to calibration performed by a domain scientist. Our main finding is that automated calibration is on par with or significantly outperforms the calibration performed by the domain scientist. Furthermore, automated calibration makes it straightforward to operate desirable trade-offs between simulation accuracy and simulation speed.
Large power transformers typically use oil-immersed transformers, and the insulation oil can play a role in heat dissipation and insulation. By analyzing the gas composition in the oil, transformer failures can be pre...
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