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arXiv 2023年

作者： Zhang, Yu Wang, Zixiao Zhao, Jin Guo, Yuluo Yu, Hui Huang, Zhiying Shi, Xuanhua Liao, Xiaofei 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

Modern scientific applications predominantly run on large-scale computing platforms, necessitating collaboration between scientific domain experts and high-performance computing (HPC) experts. While domain experts are often skilled in customizing domain-specific scientific computing routines, which often involves various matrix computations, HPC experts are essential for achieving efficient execution of these computations on large-scale platforms. This process often involves utilizing complex parallel computing libraries tailored to specific matrix computation scenarios. However, the intricate programming procedure and the need for deep understanding in both application domains and HPC poses significant challenges to the widespread adoption of scientific computing. In this research, we observe that matrix computations can be transformed into equivalent graph representations, and that by utilizing graph processing engines, HPC experts can be freed from the burden of implementing efficient scientific computations. Based on this observation, we introduce a graph engine-based scientific computing (Graph for Science) paradigm, which provides a unified graph programming interface, enabling domain experts to promptly implement various types of matrix computations. The proposed paradigm leverages the underlying graph processing engine to achieve efficient execution, eliminating the needs for HPC expertise in programming large-scale scientific applications. We evaluate the performance of the developed graph compute engine for three typical scientific computing routines. Our results demonstrate that the graph engine-based scientific computing paradigm achieves performance comparable to the best-performing implementations based on existing parallel computing libraries and bespoke implementations. Importantly, the paradigm greatly simplifies the development of scientific computations on large-scale platforms, reducing the programming difficulty for scientists and facilitating b

关键词： Application programming interfaces (API)

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StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream Learning

arXiv

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arXiv 2024年

作者： Shi, Tongjun Zhang, Shuhao Chen, Binbin He, Bingsheng National Engineering Research Center for Big DataTechnology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Singapore University of Technology and Design Singapore National University of Singapore Singapore

Stream Learning (SL) requires models that can quickly adapt to continuously evolving data, posing significant challenges in both computational efficiency and learning accuracy. Effective data selection is critical in SL to ensure a balance between information retention and training efficiency. Traditional rule-based data selection methods struggle to accommodate the dynamic nature of streaming data, highlighting the necessity for innovative solutions that effectively address these challenges. Recent approaches to handling changing data distributions face challenges that limit their effectiveness in fast-paced environments. In response, we propose StreamFP, a novel approach that uniquely employs dynamic, learnable parameters called fingerprints to enhance data selection efficiency and adaptability in stream learning. StreamFP optimizes coreset selection through its unique fingerprint-guided mechanism for efficient training while ensuring robust buffer updates that adaptively respond to data dynamics, setting it apart from existing methods in stream learning. Experimental results demonstrate that StreamFP outperforms state-of-the-art methods by achieving accuracy improvements of 15.99%, 29.65%, and 51.24% compared to baseline models across varying data arrival rates, alongside a training throughput increase of 4.6x. © 2024, CC BY.

关键词： Data accuracy

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The Entry-Extensible Cuckoo Filter 17th

The Entry-Extensible Cuckoo Filter

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17th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2020

作者： Yu, Shuiying Wu, Sijie Chen, Hanhua Jin, Hai 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

ISBN: (纸本)9783030794774

The emergence of large-scale dynamic sets in real applications brings severe challenges in approximate set representation structures. A dynamic set with changing cardinality requires an elastic capacity of the approximate set representation structure, while traditional static structures, e.g., bloom filter, cuckoo filter, and their variants cannot satisfy the requirements. Existing dynamic approximate set representation structures only provide filter-level extensions, which require a single membership query to probe all discrete filters one by one. The large number of small discrete memory accesses takes up the vast majority of query time and results in unsatisfied query performance. To address the problem, in this work we propose the entry-extensible cuckoo filter (E2CF) to reduce memory access overhead for dynamic set representation and accelerate the membership query. E2CF utilizes adjacent buckets with continuous physical addresses in a cuckoo filter to extend bucket entries, which avoids many discrete memory accesses in a query. To further make E2CF space and time efficient, we adopt asynchronous extension and fine-grained splitting methods. Experiment results show that compared to state-of-the-art designs, E2CF reduces the query and insertion time by 82% and 28%, respectively. © 2021, IFIP International Federation for Information Processing.

关键词： Data structures

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A multi-dimensional index for privacy-preserving queries in cloud computing

A multi-dimensional index for privacy-preserving queries in ...

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作者： Xu, Hui Ding, Xiaofeng Jin, Hai Yu, Qing 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

Although cloud computing saves the cost of enterprises and individuals to manage data in various applications on public cloud servers, it also causes the problem of the leakage of critical personal information and sensitive data. Therefore, the issues about data privacy of the published personal information have become a challenging problem. Most of the existing methods focus on cryptography-based techniques by encrypting the sensitive data before publishing. However, these techniques are either too computation expensive or only some authorized users can get access to the encrypted cloud data. The consequence is that the search response time is not satisfactory and the sharing extent of the cloud data is becoming limited. Motivated by this, we propose a perturbation-based method to preserve the privacy of data in cloud. To accelerate the query processing without privacy breaches, a high-efficiency multi-dimensional index is built for answering range counting queries under differential privacy, which embeds privacy-preserving R-tree index in Content Addressable Network. Experimental results demonstrate that our methods not only protect data privacy in different degree, but also accelerate the query speed efficiently. Compared with the existing method Quad-opt, our method provides about 20% better data utility under the same differential privacy principles. © 2019 John Wiley & Sons, Ltd.

关键词： Sensitive data

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OUTLIER SYNTHESIS VIA HAMILTONIAN MONTE CARLO FOR OUT-OF-DISTRIBUTION DETECTION

arXiv

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arXiv 2025年

作者： Li, Hengzhuang Zhang, Teng National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Out-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer from either poor quality or high cost due to the monotonous sampling strategy and the heavy-parameterized generative models. In this paper, we overcome all these problems by proposing the Hamiltonian Monte Carlo Outlier Synthesis (HamOS) framework, which views the synthesis process as sampling from Markov chains. Based solely on the in-distribution data, the Markov chains can extensively traverse the feature space and generate diverse and representative outliers, hence exposing the model to miscellaneous potential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance rate almost close to 1 also makes our framework enjoy great efficiency. By empirically competing with SOTA baselines on both standard and large-scale benchmarks, we verify the efficacy and efficiency of our proposed HamOS. Our code is available at: https://***/Fir-lat/HamOS_OOD. © 2025, CC BY.

关键词： Markov chains

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MalScan: Android Malware Detection Based on Social-Network Centrality Analysis

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Wu, Yueming Suo, Wenqi Feng, Siyue Zou, Deqing Yang, Wei Liu, Yang Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Jinyinhu Laboratory Wuhan430074 China University of Texas at Dallas United States Nanyang Technological University Singapore 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 School of Computer Science and Technology Wuhan430074 China

Malware scanning of an app market is expected to be scalable and effective. However, existing approaches use syntax-based features that can be evaded by transformation attacks or semantic-based features which are usually extracted by expensive program analysis. Therefore, to address the scalability challenges of traditional heavyweight static analysis, we propose a graph-based lightweight approach MalScan for Android malware detection. MalScan considers the function call graph as a complex social network and employs centrality analysis on sensitive application program interfaces (APIs) to express the semantic characteristics of the graph. On this basis, machine learning algorithms and ensemble learning algorithms are applied to classify the extracted features. We evaluate MalScan on datasets of 104,892 benign apps and 108,640 malwares, and the results of experiments indicate that MalScan outperforms six state-of-the-art detectors and can quickly detect Android malware with an f-value as high as 99%. In addition, there are also significant improvements in the robustness of Android app evolution and robustness to obfuscation. Finally, we conduct an exhaustive statistical study of over one million applications in the Google-Play app market and successfully identify 498 zero-day malware, which further validates the feasibility of MalScan on market-wide malware scanning. © 2004-2012 IEEE.

关键词： Android malware

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FDGLib: A Communication Library for Efficient Large-Scale Graph Processing in FPGA-Accelerated Data Centers

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Journal of Computer Science & technology 2021年第5期36卷 1051-1070页

作者： Yu-Wei Wu Qing-Gang Wang Long Zheng Xiao-Fei Liao Hai Jin Wen-Bin Jiang Ran Zheng Kan Hu National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

With the rapid growth of real-world graphs,the size of which can easily exceed the on-chip(board)storage capacity of an accelerator,processing large-scale graphs on a single Field Programmable Gate Array(FPGA)becomes *** multi-FPGA acceleration is of great necessity and *** cloud providers(e.g.,Amazon,Microsoft,and Baidu)now expose FPGAs to users in their data centers,providing opportunities to accelerate large-scale graph *** this paper,we present a communication library,called FDGLib,which can easily scale out any existing single FPGA-based graph accelerator to a distributed version in a data center,with minimal hardware engineering *** provides six APIs that can be easily used and integrated into any FPGA-based graph accelerator with only a few lines of code *** the torus-based FPGA interconnection in data centers,FDGLib also improves communication efficiency using simple yet effective torus-friendly graph partition and placement *** interface FDGLib into AccuGraph,a state-of-the-art graph *** results on a 32-node Microsoft Catapult-like data center show that the distributed AccuGraph can be 2.32x and 4.77x faster than a state-of-the-art distributed FPGA-based graph accelerator ForeGraph and a distributed CPU-based graph system Gemini,with better scalability.

关键词： data center accelerator graph processing distributed architecture communication optimization

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Intersecting-boundary-sensitive fingerprinting for tampering detection of DNN models 24

Intersecting-boundary-sensitive fingerprinting for tampering...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Xiaofan Bai Chaoxiang He Xiaojing Ma Bin Benjamin Zhu Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security Microsoft School of Computer Science and Technology Huazhong University of Science and Technology and National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab.

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF.

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GraSU: A fast graph update library for fpga-based dynamic graph processing 21

GraSU: A fast graph update library for fpga-based dynamic gr...

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2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA 2021

作者： Wang, Qinggang Zheng, Long Huang, Yu Yao, Pengcheng Gui, Chuangyi Liao, Xiaofei Jin, Hai Jiang, Wenbin Mao, Fubing National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology China

ISBN: (纸本)9781450382182

Existing FPGA-based graph accelerators, typically designed for static graphs, rarely handle dynamic graphs that often involve substantial graph updates (e.g., edge/node insertion and deletion) over time. In this paper, we aim to fill this gap. The key innovation of this work is to build an FPGA-based dynamic graph accelerator easily from any off-the-shelf static graph accelerator with minimal hardware engineering efforts (rather than from scratch). We observe\em spatial similarity of dynamic graph updates in the sense that most of graph updates get involved with only a small fraction of vertices. We therefore propose an FPGA library, called GraSU, to exploit spatial similarity for fast graph updates. GraSU uses a differential data management, which retains the high-value data (that will be frequently accessed) in the specialized on-chip UltraRAM while the overwhelming majority of low-value ones reside in the off-chip memory. Thus, GraSU can transform most of off-chip communications arising in dynamic graph updates into fast on-chip memory accesses. Our experiences show that GraSU can be easily integrated into existing state-of-the-art static graph accelerators with only 11 lines of code modifications. Our implementation atop AccuGraph using a Xilinx Alveo#8482;\U250 board outperforms two state-of-the-art CPU-based dynamic graph systems, Stinger and Aspen, by an average of 34.24× and 4.42× in terms of update throughput, improving further overall efficiency by 9.80× and 3.07× on average. © 2021 ACM.

关键词： Field programmable gate arrays (FPGA)

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Maverick: Personalized Edge-Assisted Federated Learning with Contrastive Training 25

Maverick: Personalized Edge-Assisted Federated Learning with...

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34th ACM Web Conference, WWW 2025

作者： Wang, Kaibin He, Qiang Dong, Zeqian Chen, Rui He, Chuan Chua, Caslon Chen, Feifei Yang, Yun Swinburne University of Technology Melbourne Australia Huazhong University of Science and Technology Wuhan China Deakin University Melbourne Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400712746

In an edge-assisted federated learning (FL) system, edge servers aggregate the local models from the clients within their coverage areas to produce intermediate models for the production of the global model. This significantly reduces the communication overhead incurred during the FL process. To accelerate model convergence, FedEdge, the state-of-the-art edge-assisted FL system, trains clients’ models in local federations when they wait for the global model in each training round. However, our investigation reveals that it drives the global model towards clients with excessive local training, causing model drifts that undermine model performance for other clients. To tackle this problem, this paper presents Maverick, a new edge-assisted FL system that mitigates model drifts by training personalized local models for clients through contrastive local training. It introduces a model-contrastive loss to facilitate personalized local federated training by driving clients’ local models away from the global model and close to their corresponding intermediate models. In addition, Maverick includes anomalous models in contrastive local training as negative samples to accelerate the convergence of clients’ local models. Extensive experiments are conducted on three widely-used models trained on three datasets to comprehensively evaluate the performance of Maverick. Compared to state-of-the-art edge-assisted FL systems, Maverick accelerates model convergence by up to 16.2x and improves model accuracy by up to 12.7%. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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