Serving generative inference of the large language model is a crucial component of contemporary AI applications. This paper focuses on deploying such services in a heterogeneous and cross-datacenter setting to mitigat...
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Serving generative inference of the large language model is a crucial component of contemporary AI applications. This paper focuses on deploying such services in a heterogeneous and cross-datacenter setting to mitigate the substantial inference costs typically associated with a single centralized datacenter. Towards this end, we propose HEXGEN, a flexible distributed inference engine that uniquely supports the asymmetric partition of generative inference computations over both tensor model parallelism and pipeline parallelism and allows for effective deployment across diverse GPUs interconnected by a fully heterogeneous network. We further propose a sophisticated scheduling algorithm grounded in constrained optimization that can adaptively assign asymmetric inference computation across the GPUs to fulfill inference requests while maintaining acceptable latency levels. We conduct an extensive evaluation to verify the efficiency of HEXGEN by serving the state-of-the-art LLAMA-2 (70B) model. The results suggest that HEXGEN can choose to achieve up to 2.3× lower latency deadlines or tolerate up to 4× more request rates compared with the homogeneous baseline given the same budget. Our implementation is available at https://***/Relaxed-System-Lab/HexGen. Copyright 2024 by the author(s)
For a parameter Ε ∈ (0, 1), a set of Ε-locality-sensitive orderings (LSOs) has the property that for any two points, p, q ∈ [0, 1)d, there exist an order in the set such that all the points between p and q (in the...
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Cloud-based SDN(Software Defined Network)integration offers new kinds of agility,flexibility,automation,and speed in the *** and Cloud providers both leverage the benefits as networks can be configured and optimized b...
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Cloud-based SDN(Software Defined Network)integration offers new kinds of agility,flexibility,automation,and speed in the *** and Cloud providers both leverage the benefits as networks can be configured and optimized based on the application *** integration of cloud and SDN paradigms has played an indispensable role in improving ubiquitous health care *** has improved the real-time monitoring of patients by medical ***’data get stored at the central server on the cloud from where it is available to medical practitioners in no *** centralisation of data on the server makes it more vulnerable to malicious attacks and causes a major threat to patients’*** recent days,several schemes have been proposed to ensure the safety of patients’*** most of the techniques still lack the practical implementation and safety of *** this paper,a secure multi-factor authentication protocol using a hash function has been ***(Body Area Network)logic has been used to formally analyse the proposed scheme and ensure that no unauthenticated user can steal sensitivepatient *** Protocol Animator(SPAN)–Automated Validation of Internet Security Protocols and Applications(AVISPA)tool has been used for *** results prove that the proposed scheme ensures secure access to the database in terms of spoofing and *** comparisons of the proposed scheme with other related historical schemes regarding time complexity,computation cost which accounts to only 423 ms in proposed,and security parameters such as identification and spoofing prove its efficiency.
This study presents the Normal Discriminant Feature Selection based Regressive Deep Neural MapReduce (NDFS-RDNMR) framework designed for efficient prediction of diabetic chronic diseases using input datasets. The prim...
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One of the most effective methods of training a model for intrusion detection requires a very good selection of features from the data and efficient and robust training algorithms to facilitate a better prediction mod...
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Diabetes, influenced by factors like high blood pressure, aging, obesity, and poor lifestyle choices, has become a significant health issue, increasing the risk of heart disease, kidney disease, stroke, and other seri...
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Digital Twins replicate the real situations and the outcomes, helping organizations to make better decisions. Digital Twins find valuable in different domains such as manufacturing, automotive, healthcare, and etc. In...
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Underwater data transmission forms a feasible method for underwater communication. However, laboratory experiments significantly differ from natural aquatic settings due to physical size constraints. During the last f...
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Pharmacological datasets like Yeast and Escherichia coli (E. coli) have a massive impact on the healthcare industry for the production of human drugs. Yeast is well recognized as a significant constituent in the produ...
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With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image *** has also driven research on image forgery detection ***...
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With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image *** has also driven research on image forgery detection *** this paper,a U-Net with multiple sensory field feature extraction(MSCU-Net)for image forgery detection is *** proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or ***-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the features of different perceptual fields use residual propagation and residual feedback to recall the input feature information and consolidate the input feature information to make the difference in image attributes between the untampered and tampered regions more obvious,and introduce the channel coordinate confusion attention mechanism(CCCA)in skip-connection to further improve the segmentation accuracy of the *** this paper,extensive experiments are conducted on various mainstream datasets,and the results verify the effectiveness of the proposed method,which outperforms the state-of-the-art image forgery detection methods.
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