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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Han, Hao Lu, Feng Deng, Yanhan Luo, Xiaofang Jin, Hai Tu, Wei Xie, Xia School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan430074 China Tongji Medical College Huazhong University of Science and Technology Tongji Hospital Wuhan430022 China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9798350358780

In the medical realm, the pivotal role of pathological Whole Slide Images (WSIs) in detecting cancer, tracking disease progression, and evaluating treatment efficacy is indisputable. Nevertheless, the identification and quantification of lesion areas in these gigapixel WSIs present a significant challenge due to their substantial size and the intricate details of lesions. To address these issues, we developed a novel multi-resolution and multi-scale cross fusion network (M2CF-Net), adept at managing large-scale pathological WSIs and capturing both fine details and context. Our model particularly focuses on segmenting local lymphocyte infiltration lesions in pathological WSIs of patients diagnosed with primary Sjogren's syndrome. By employing a patch-based training approach and combining interconnected elements via a multi-scale fusion technique, we enhance our model's capacity to detect and analyze structures and features in minor salivary gland section WSIs. Extensive experiments and ablation studies conducted on real-world clinical datasets affirm our model's superior accuracy in identifying lymphocyte-infiltrated regions over state-of-the-art models, with a performance improvement of up to 4.32% in terms of the Dice Similarity Coefficient. © 2023 IEEE.

关键词： Pathology

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Ripple: Toward a substrate middleware for peer-to-peer applications

Ripple: Toward a substrate middleware for peer-to-peer appli...

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3rd International Conference on Innovative computing Information and Control, ICICIC'08

作者： Yao, Hong Yu, Linchen Jin, Hai Yang, Sirui Huang, Qi Jiang, Wenjian School of Computer Science and Technology China University of Geosciences Wuhan 430074 China Cluster and Grid Computing Lab. Services Computing Technology and System Lab. Huazhong University of Science and Technology Wuhan 430074 China France Telecom R and D China Beijing 100190 China

ISBN: (纸本)9780769531618

Peers in multiple P2P applications execute overlapped operations with different implementation, like neighbour management, resource publish/lookup, delay investigation, which leads to poor interoperability and unnecessary network load. Peers contribute their resources only when they stay in same application. We propose a P2P middleware, called Ripple, to accomplish common functions in a layered architecture. Ripple manages all peers running various applications in a uniform substrate overlay network. The substrate overlay is designed with a hybrid topology. This hybrid topology does not aim at only a particular type of applications, but also enables the flexibility for various applications. Our work is an attempt to address common needs of P2P applications, which may simplify the development and improve the performance of P2P networks, as well as mitigate the sampling stress to physical networks. © 2008 IEEE.

关键词： Middleware

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Optimizing data shuffling in data-parallel computation by understanding user-defined functions 9

Optimizing data shuffling in data-parallel computation by un...

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9th USENIX Symposium on Networked systems Design and Implementation, NSDI 2012

作者： Zhang, Jiaxing Zhou, Hucheng Chen, Rishan Fan, Xuepeng Guo, Zhenyu Lin, Haoxiang Li, Jack Y. Lin, Wei Zhou, Jingren Zhou, Lidong Microsoft Research Asia China Microsoft Bing United States Peking University China Services Computing Technologyand System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Georgia Institute of Technology United States

Map/Reduce style data-parallel computation is characterized by the extensive use of user-defined functions for data processing and relies on data-shuffling stages to prepare data partitions for parallel computation. Instead of treating user-defined functions as "black boxes", we propose to analyze those functions to turn them into "gray boxes" that expose opportunities to optimize data shuffling. We identify useful functional properties for userdefined functions, and propose SUDO, an optimization framework that reasons about data-partition properties, functional properties, and data shuffling. We have assessed this optimization opportunity on over 10,000 dataparallel programs used in production SCOPE clusters, and designed a framework that is incorporated it into the production system. Experiments with real SCOPE programs on real production data have shown that this optimization can save up to 47% in terms of disk and network I/O for shuffling, and up to 48% in terms of cross-pod network traffic. © 2012 by The USENIX Association. All Rights Reserved.

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Dispatcher: Resource-aware Nakamoto Blockchain via Hierarchical Topology and Adaptive Incentives

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Distributed Ledger Technologies: Research and Practice 2024年第2期3卷 1-20页

作者： Hai Jin Shuohua Dong Xiaohai Dai Yuandi Cai Jiang Xiao National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Mainstream blockchain systems such as Bitcoin and Ethereum are revolutionizing the financial industry by adopting the Nakamoto consensus protocol, i.e., Proof-of-Work (PoW). Only nodes with sufficient computing resources can work out the PoW difficulties, thereby increasing the mining cost of malicious attackers and ensuring the security of blockchain systems. Such an assumption of having abundant resources leads to drawbacks of low throughput and risk of centralization. In this article, we present Dispatcher, a novel distributed consensus protocol that takes resource heterogeneity into account to ensure resource-aware PoW with high efficiency. Dispatcher introduces a hierarchical topology to offer flexible PoW difficulties tailored for different nodes’ resources. In particular, it utilizes the limited resource of each node to jointly maximize the performance by concurrent mining. Moreover, we design an adaptive incentive mechanism to fit the available resource of blockchain nodes to rewards. Our experiments show that Dispatcher enjoys a substantial performance margin over the state-of-the-art. We can achieve a 50% throughput improvement compared with OHIE.

关键词： Blockchain resource heterogeneity incentive hierarchical architecture

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λGrapher: A Resource-Efficient Serverless system for GNN Serving through Graph Sharing 24

λGrapher: A Resource-Efficient Serverless System for GNN Se...

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33rd ACM Web Conference, WWW 2024

作者： Hu, Haichuan Liu, Fangming Pei, Qiangyu Yuan, Yongjie Xu, Zichen Wang, Lin National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab in the School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan China Peng Cheng Laboratory Huazhong University of Science and Technology China School of Mathematics and Computer Science Nanchang University China Paderborn University Paderborn Germany

ISBN: (纸本)9798400701719

Graph Neural Networks (GNNs) have been increasingly adopted for graph analysis in web applications such as social networks. Yet, efficient GNN serving remains a critical challenge due to high workload fluctuations and intricate GNN operations. Serverless computing, thanks to its flexibility and agility, offers on-demand serving of GNN inference requests. Alas, the request-centric serverless model is still too coarse-grained to avoid resource waste. Observing the significant data locality in computation graphs of requests, we propose λGrapher, a serverless system for GNN serving that achieves resource efficiency through graph sharing and fine-grained resource allocation. "Grapher features the following designs: (1) adaptive timeout for request buffering to balance resource efficiency and inference latency, (2) graph-centric scheduling to minimize computation and memory redundancy, and (3) resource-centric function management with fine-grained resource allocation catered to the resource sensitivities of GNN operations and function orchestration optimized to hide communication latency. We implement a prototype of λGrapher based on the representative open-source serverless platform Knative and evaluate it with real-world traces from various web applications. Our results show that λGrapher can achieve an average savings of 61.5% in memory resource and 47.2% in computing resource compared with the state of the arts while ensuring GNN inference latency. © 2024 ACM.

关键词： Efficiency

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Optimizing non-coalesced memory access for irregular applications with GPU computing

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Frontiers of Information technology & Electronic Engineering 2020年第9期21卷 1285-1301页

作者： Ran ZHENG Yuan-dong LIU Hai JIN National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

General purpose graphics processing units(GPGPUs)can be used to improve computing performance considerably for regular ***,irregular memory access exists in many applications,and the benefits of graphics processing units(GPUs)are less substantial for irregular *** recent years,several studies have presented some solutions to remove static irregular memory ***,eliminating dynamic irregular memory access with software remains a serious challenge.A pure software solution without hardware extensions or offline profiling is proposed to eliminate dynamic irregular memory access,especially for indirect memory *** reordering and index redirection are suggested to reduce the number of memory transactions,thereby improving the performance of GPU *** improve the efficiency of data reordering,an operation to reorder data is offloaded to a GPU to reduce overhead and thus transfer *** concurrently executing the compute unified device architecture(CUDA)streams of data reordering and the data processing kernel,the overhead of data reordering can be *** these optimizations,the volume of memory transactions can be reduced by 16.7%-50%compared with CUSPARSE-based benchmarks,and the performance of irregular kernels can be improved by 9.64%-34.9%using an NVIDIA Tesla P4 GPU.

关键词： General purpose graphics processing units Memory coalescing Non-coalesced memory access Data reordering

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RAPN: Network attack prediction using ranking access petri net

Proceedings - 2011 6th Annual ChinaGrid Conference, ChinaGri...

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Proceedings - 2011 6th Annual ChinaGrid Conference, ChinaGrid 2011 2011年 108-115页

作者： Traore, Moussa Djiriba Jin, Hai Zou, Deqing Qiang, Weizhong Xiang, Guofu Services Computing Technology and System Lab. Cluster and Grid Computing Lab. Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769544724

Exploits sequencing is a typical way by which an attacker breaks into a network. In such a scenario, each exploit lays as an atomic proposition for subsequent exploits. An attack path is seen as a succession of exploits which take an attacker right to his/her final goal. The set of all possible attack paths form an attack graph. Researchers have proposed a multitude of techniques to generate attack graph which grows exponentially in the size of the network. Hence it is preferable to optimize the choice of solutions which avoid the cost of scalability and cumbersome. In this paper, we propose a comprehensive approach to network vulnerability analysis by ranking access Petri net graph and utilizing a penetration tester's perspective of maximal level of access possible on a host. Our approach has the following benefits: it provides a simple model in which an analyst can work, its algorithmic complexity is polynomial in the size of the network, and has the ability of scaling well to large size networks. Nevertheless, it has some drawback as in place of all possible attack paths, we seek only good attack paths. An analyst may make suboptimal choices when repairing the network. © 2011 IEEE.

关键词： Petri nets

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Piggyback game: Efficient event stream dissemination in Online Social Network systems

Piggyback game: Efficient event stream dissemination in Onli...

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International Conference on Network Protocols

作者： Fan Zhang Hanhua Chen Hai Jin Services Computing Technology and System Lab Big Data Technology and System Lab and Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781509032822

Event stream dissemination dominates the workloads in large-scale Online Social Network (OSN) systems. Based on the de facto per-user view data storage, event stream dissemination raises a large amount of inter-server traffics due to the complex interconnection among OSN users. The state-of-the-art schemes mainly explore the structure features of social graphs to reduce the inter-server messages for event stream dissemination. Different sub-graph structures are exploited for achieving the approximated optimal assignment. However, such schemes incur high costs of computation or communication. In this work, we follow a different design philosophy by using a game theoretic approach, which decomposes the high complex graph computation problem into individuals' rational strategy selection of each node. Specifically, we propose a novel social piggyback game to achieve a more efficient solution. We mathematically prove the existing of the Nash Equilibrium of the social piggyback game. Moreover, we propose an efficient best response dynamic algorithm to achieve the Nash Equilibrium, which quickly converges in a small number of iterations for large-scale OSNs. We further show that the communication cost of this design achieves a 1.5-approximation of the theoretical social optimal. We conduct comprehensive experiments to evaluate the performance of this design using large-scale real-world traces from popular OSN systems. Results show that the social piggyback game achieves a significant 302× improvement in system efficiency compared to existing schemes.

关键词： Games Schedules Heuristic algorithms Algorithm design and analysis Nash equilibrium Social network services Servers

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A multi-domain and multi-overlay framework of P2P IMS core network based on cloud infrastructure

A multi-domain and multi-overlay framework of P2P IMS core n...

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International Conference on Networking, Architecture, and Storage (NAS)

作者： Feng Lu Jiao Song Xiao Lei Hai Jin Zaiyang Tang Xiaofei Liao Fei Qiu Services Computing Technology and System Lab Huazhong University of Science and Technology China

ISBN: (纸本)9781467378925

The key technology of next generation network, IP Multimedia Subsystem (IMS), lacks of scalability, reliability and load balancing ability because of its traditional centralized control architecture. Using P2P (peer-to-peer) technology to transform architecture of the traditional IMS core network is a feasible solution, while the function separation and independent operation requirements of IMS network elements in telecommunication career level can not be met. In this paper, we present a multi-domain and multi-overlay framework of P2P IMS core network based on cloud infrastructure. In this framework, various network elements (NEs) are divided into different domains and overlay of the core network. Experimental results indicate that this framework can be deployed in the actual operating core network and provides core functions. Besides, the service capacity grows linearly as the number of serving nodes increases. The load can be automatically distributed on each element in a balanced way and the maximum load difference between elements is no more than 16%. Moreover, the response time of redesigned procedures is much smaller than the original procedures.

关键词： Peer-to-peer computing Overlay networks Registers Protocols Computer architecture Centralized control Servers

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POSTER: Layrub: Layer-centric GPU memory reuse and data migration in extreme-scale deep learning systems

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ACM SIGPLAN Notices 2018年第1期53卷 405-406页

作者： Liu, Bo Jiang, Wenbin Jin, Hai Shi, Xuanhua Ma, Yang Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450349116

Growing accuracy and robustness of Deep Neural Networks (DNN) models are accompanied by growing model capacity (going deeper or wider). However, high memory requirements of those models make it difficult to execute the training process in one GPU. To address it, we first identify the memory usage characteristics for deep and wide convolutional networks, and demonstrate the opportunities of memory reuse on both intra-layer and inter-layer levels. We then present Layrub, a runtime data placement strategy that orchestrates the execution of training process. It achieves layer-centric reuse to reduce memory consumption for extreme-scale deep learning that cannot be run on one single GPU. © 2018 Owner/Author.

关键词： Deep neural networks

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