Tendering and bidding is one of the most important resource allocation methods in the market economy system with Chinese characteristics. Evaluation experts hold the leading position in the evaluation work, and their ...
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In this paper, adaptive control strategies for distributed new energy systems are discussed in detail. The capacity of new energy sources is often affected by climate and weather conditions, so traditional control met...
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Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as inform...
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ISBN:
(纸本)9798350364613;9798350364606
Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (distributedparallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.
R programming language is commonly used for statistical computing, data science and stochastic simulation. Existing packages for R allow to run parallel code on various parallel architectures, however, the support for...
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This paper proposes a time series prediction based industrial control honey network simulation method to address the issue that many current industrial control honey network solutions only consider the response intera...
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Modernizing grid connectivity laws is necessary for the electric power systems’ rapid proliferation of distributed generators (DGs), especially as microgrids (MGs) proliferate and speed up the decentralization of net...
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The serverless computing model has been on the rise in recent years due to a lower barrier to entry and elastic scalability. However, our experimental evidence suggests that multiple serverless computing platforms suf...
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ISBN:
(纸本)9798400701559
The serverless computing model has been on the rise in recent years due to a lower barrier to entry and elastic scalability. However, our experimental evidence suggests that multiple serverless computing platforms suffer from serious performance inefficiencies when a high number of concurrent function instances are invoked, which is a desirable capability for parallel applications. To mitigate this challenge, this paper introduces ProPack, a novel solution that provides higher performance and yields cost savings for end users running applications with high concurrency. ProPack leverages insights obtained from experimental study to build a simple and effective analytical model that mitigates the scalability bottleneck. Our evaluation on multiple serverless platforms including AWS Lambda and Google confirms that ProPack can improve average performance by 85% and save cost by 66%. ProPack provides significant improvement (over 50%) over the state-of-the-art serverless workload manager such as Pywren, and is also, effective at mitigating the concurrency bottleneck for FuncX, a recent on-premise serverless execution platform for parallel applications.
As distributed Energy Resources become more integrated into the power grid, the complexity and scale of challenges faced by grid operators have increased. The integration of micro-synchrophasors (micro-PMUs) into the ...
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Big data technology is increasingly penetrating various industries, bringing unprecedented opportunities to enterprises and society with its powerful data processing and analysis capabilities. At the same time, the ra...
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Predicting load changes in power grid operations and planning holds immense significance in guaranteeing the dependability and efficiency of power supply. This research endeavors to investigate deep learning technolog...
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