In parallelcomputing, where efficiency and speed are crucial, the Message Passing Interface (MPI) is a fundamental paradigm for managing large-scale distributed memory systems. MPI is critical to complex computationa...
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The proceedings contain 114 papers. The topics discussed include: accounting and monitoring infrastructure for distributedcomputing in the atlas experiment;the atlas EVENTINDEX using the HBase/phoenix storage solutio...
The proceedings contain 114 papers. The topics discussed include: accounting and monitoring infrastructure for distributedcomputing in the atlas experiment;the atlas EVENTINDEX using the HBase/phoenix storage solution;offline software and computing for the SPD experiment;the grid-characteristic method for applied dynamic problems of fractured and anisotropic media;fractal thermodynamics, big data and its 3D visualization;the technology and tools for the building of information exchange package based on semantic domain model;participation of Russian institutes in the processing and storage of ALICE data;a virtual testbed for optimizing the performance of a new type of accelerators;multithreaded event simulation in the BMNROOT package;and resource management in private multi-service cloud environments.
Efficient energy supply and consumption play a substantial role in the energy grid, especially with renewable energy sources. Renewable power sources are unreliable which made the grid difficult to handle. A smart ene...
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We develop a clearance and settlement model for Peer-to-Peer (P2P) energy trading in low-voltage networks. The model enables direct transactions between parties within an open and distributed system and integrates unu...
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ISBN:
(纸本)9798350318562;9798350318555
We develop a clearance and settlement model for Peer-to-Peer (P2P) energy trading in low-voltage networks. The model enables direct transactions between parties within an open and distributed system and integrates unused capacity while respecting network constraints. We evaluate the model through simulations of different scenarios (normal operating conditions and extreme conditions) for 24-hour time blocks. Our simulations highlight the benefits of our model in a decentralized energy system, notably its ability to deal with high-trade volumes.
The proceedings contain 222 papers. The topics discussed include: DRL-deploy: adaptive service function chains deployment with deep reinforcement learning;accuracy vs. efficiency: achieving both through hardware-aware...
ISBN:
(纸本)9781665435741
The proceedings contain 222 papers. The topics discussed include: DRL-deploy: adaptive service function chains deployment with deep reinforcement learning;accuracy vs. efficiency: achieving both through hardware-aware quantization and reconfigurable architecture with mixed precision;cmss: collaborative modeling of safety and security requirements for network protocols;FGPA: fine-grained pipelined acceleration for depthwise separable CNN in resource constraint scenarios;Dyacon: JointCloud dynamic access control model of data security based on verifiable credentials;understanding the runtime overheads of deep learning inference on edge devices;and alleviating imbalance in synchronous distributed training of deep neural networks.
This article discusses creating distributed AI algorithms for edge computing and suggests a model optimization framework using federated learning to boost computing efficiency and real-time system performance. In comm...
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The continuous growth in the worldwide demand for medical services pushes the traditional model of hospital care towards a more sustainable one which makes continuous monitoring of health status as well as remote trea...
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ISBN:
(纸本)9798350369458;9798350369441
The continuous growth in the worldwide demand for medical services pushes the traditional model of hospital care towards a more sustainable one which makes continuous monitoring of health status as well as remote treatment of diseases (where possible) feasible. In this context, the synergic exploitation of well-established paradigms such as Internet of Things (IoT), Wearable computing and Edge Intelligence (EI) within a single platform can represent a game-changer with respect to the current smart healthcare scenario. In this paper we introduce ENTRUST (usEr ceNtric plaTform foR continoUS healThcare), an innovative solution pivoted around a seamless edge-cloud architecture and a simulation-based approach for the development of next-generation, daily healthcare services. The system desiderata and challenges towards its implementation are hence discussed and a use case reported to exemplify the envisioned ENTRUST approach.
Federated Learning (FL) diverges from traditional Machine Learning (ML) models decentralizing data utilization, addressing privacy concerns. This approach involves iterative model updates, where individual devices com...
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ISBN:
(纸本)9798350363074;9798350363081
Federated Learning (FL) diverges from traditional Machine Learning (ML) models decentralizing data utilization, addressing privacy concerns. This approach involves iterative model updates, where individual devices compute gradients based on local data, share updates with a central server, and receive an improved global model. High-Performance computing (HPC) systems enhance FL efficiency by leveraging parallel processing. In this study, we aim to explore FL efficiency using four aggregation methods on three datasets across six clients, assess metrics like global model accuracy and communication efficiency, and evaluate FL on HPC. We employ Flower, a versatile FL framework, in our experiments. Our chosen datasets include MNIST, Digits, and Semeion Handwritten Digit, distributed among two clients each. We utilize NVIDIA GPUs for computation, with aggregation methods such as FedAvg, FedProx, FedOpt, and FedYogi. Metrics include Convergence Time, Global Model Accuracy, Communication Efficiency, and HPC Throughput. The results will provide insights into FL performance, especially in HPC environments, impacting convergence, communication, and resource utilization.
In order to improve the effect of risk grading control and hidden danger investigation and treatment in the construction of pumped storage power plants, as well as to prevent construction safety accidents, this study ...
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In recent years, the research of distributed generation, especially renewable energy sources, have received unprecedented attention all over the world. The proportion of distributed new energy grid absorbed is increas...
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ISBN:
(纸本)9789819743896;9789819743902
In recent years, the research of distributed generation, especially renewable energy sources, have received unprecedented attention all over the world. The proportion of distributed new energy grid absorbed is increasing year by year, and the demand of smart grid for computing resources is diverse and dynamic. The mismatch between the existing computing communication architecture and the dynamic computing resource requirements of distributed business has become an important technical problem to be solved for the further intelligence of distribution network in the dual-carbon background. In this article, we propose a triple fitness genetic algorithm (TFGA) for make fast and accurate computing scheduling. Specifically, we first introduce a compute first networking scheduling framework in the edge cloud computing environment. Second, we improve task scheduling strategy in cloud computing by add two fitness in genetic algorithm, and maximize the efficiency of cloud computing environment by optimizing task scheduling, so as to make fast and accurate arithmetic scheduling. Subsequently, we conducted a comparison of simulation experiments between the Adaptive Genetic Algorithm (AGA) and TFGA, under identical environmental conditions. Extensive simulation results show that the system can effectively assist smart grid in rational arithmetic dispatch and improving resource utilization efficiency.
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