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IEEE Symposium on High-Performance Computer Architecture

作者： Yu Zhang Xiaofei Liao Hai Jin Ligang He Bingsheng He Haikun Liu Lin Gu 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 China University of Warwick United Kingdom National University of Singapore Singapore

Many graph processing systems have been recently developed for many-core processors. However, for iterative graph processing, due to the dependencies between vertices' states, the propagations of new states of vertices are inherently conducted along graph paths sequentially and are also dependent on each other. Despite the years' research effort, existing solutions still severely underutilize many-core processors to quickly propagate the new states of vertices, suffering from slow convergence speed. In this paper, we propose a dependency-driven programmable accelerator, DepGraph, which couples with the core architecture of the many-core processor and can fundamentally alleviate the challenge of dependencies for faster state propagation. Specifically, we propose an effective dependency-driven asynchronous execution approach into novel microarchitecture designs for faster state propagations. DepGraph prefetches the vertices for the core on-the-fly along the dependency chains between their states and the active vertices' new states, aiming to effectively accelerate the propagations of the active vertices' new states and also ensure better data locality. Through transforming the dependency chains along the frequently-used paths into direct ones at runtime and maintaining these calculated direct dependencies as a set of fast shortcuts, called hub index, DepGraph further accelerates most state propagations. Also, many propagations do not need to wait for the completion of other propagations, which enables more propagations to be effectively conducted along the paths with higher degree of parallelism. The experimental results show that for iterative graph processing on a simulated 64-core processor, a cutting-edge software graph processing system can achieve 5.0-22.7 times speedup after integrating with our DepGraph while incurring only 0.6% area cost. In comparison with three state-of-the-art hardware solutions, i.e., HATS, Minnow, and PHI, DepGraph improves the performan

关键词： Program processors Prefetching Computer architecture Parallel processing Software Acceleration Indexes

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No Free Lunch Theorem for Privacy-Preserving LLM Inference

arXiv

引用

arXiv 2024年

作者： Zhang, Xiaojin Fei, Yulin Kang, Yan Chen, Wei Fan, Lixin Jin, Hai Yang, Qiang Huazhong University of Science and Technology China WeBank China Hong Kong University of Science and Technology China 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 Wuhan430074 China

Individuals and businesses have been significantly benefited by Large Language Models (LLMs) including PaLM, Gemini and ChatGPT in various ways. For example, LLMs enhance productivity, reduce costs, and enable us to focus on more valuable tasks. Furthermore, LLMs possess the capacity to sift through extensive datasets, uncover underlying patterns, and furnish critical insights that propel the frontiers of technology and science. However, LLMs also pose privacy concerns. Users’ interactions with LLMs may expose their sensitive personal or company information. A lack of robust privacy safeguards and legal frameworks could permit the unwarranted intrusion or improper handling of individual data, thereby risking infringements of privacy and the theft of personal identities. To ensure privacy, it is essential to minimize the dependency between shared prompts and private information. Various randomization approaches have been proposed to protect prompts’ privacy, but they may incur utility loss compared to unprotected LLMs prompting. Therefore, it is essential to evaluate the balance between the risk of privacy leakage and loss of utility when conducting effective protection mechanisms. The current study develops a framework for inferring privacy-protected Large Language Models (LLMs) and lays down a solid theoretical basis for examining the interplay between privacy preservation and utility. The core insight is encapsulated within a theorem that is called as the NFL (abbreviation of the word No-Free-Lunch) Theorem. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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A Locality-Aware Energy-Efficient Accelerator for Graph Mining Applications

A Locality-Aware Energy-Efficient Accelerator for Graph Mini...

引用

IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Pengcheng Yao Long Zheng Zhen Zeng Yu Huang Chuangyi Gui Xiaofei Liao Hai Jin Jingling Xue 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 School of Computer Science and Engineering UNSW Sydney Australia

ISBN: (数字)9781728173832

ISBN: (纸本)9781728173849

Graph mining is becoming increasingly important due to the ever-increasing demands on analyzing complex structures in graphs. Existing graph accelerators typically hold most of the randomly-accessed data in an on-chip memory to avoid off-chip communications. However, graph mining exhibits substantial random accesses from not only vertex dimension but also edge dimension (with the latter being excessively more complex than the former), leading to significant degradations in terms of both performance and energy *** observe that the most random memory requests arising in graph mining come from accessing a small fraction of valuable (vertex and edge) data when handling real-world graphs. To exploit this extension locality with maximum parallelism, we architect GRAMER, the first graph mining accelerator. GRAMER contains a specialized memory hierarchy, where the valuable data (precisely identified through a cost-efficient heuristic) is permanently resident in a high-priority memory while others are maintained in a cache-like memory under a lightweight replacement policy. The specific pipelined processing units are carefully designed to maximize computational parallelism. GRAMER is also equipped with a work-stealing mechanism to reduce load imbalance. We have implemented GRAMER on a Xilinx Alveo U250 accelerator card. Compared with two state-of-the-art CPU-based graph mining systems, Fractal and RStream, running on a 14-core Intel E5-2680 v4 processor, GRAMER achieves not only considerable speedups (1.11 × ~ 129.95 ) but also significant energy savings (5.79 × ~ 678.34×)

关键词： Memory management Parallel processing Feature extraction Energy efficiency Fractals system-on-chip Data mining

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ScalabFS: A scalable BFS accelerator on HBM-enhanced FPGAs

arXiv

引用

arXiv 2021年

作者： Li, Kexin Liu, Chenhao Shao, Zhiyuan Wang, Zeke Wu, Minkang Chen, Jiajie Liao, Xiaofei 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 Wuhan30074 China Collaborative Innovation Center of Artificial Intelligence Zhejiang University China

High Bandwidth Memory (HBM) provides massive aggregated memory bandwidth by exposing multiple memory channels to the processing units. To achieve high performance, an accelerator built on top of an FPGA configured with HBM (i.e., FPGA-HBM platform) needs to scale its performance according to the available memory channels. In this paper, we propose an accelerator for BFS (Breadth-First Search) algorithm, named as ScalabFS, that builds multiple processing elements to sufficiently exploit the high bandwidth of HBM to improve efficiency. We implement the prototype system of ScalabFS and conduct BFS in both real-world and synthetic scale-free graphs on Xilinx Alveo U280 FPGA card real hardware. The experimental results show that ScalabFS scales its performance almost linearly according to the available memory pseudo channels (PCs) from the HBM2 subsystem of U280. By fully using the 32 PCs and building 64 processing elements (PEs) on U280, ScalabFS achieves a performance up to 19.7 GTEPS (Giga Traversed Edges Per Second). When conducting BFS in sparse real-world graphs, ScalabFS achieves equivalent GTEPS to Gunrock running on the state-of-art Nvidia V100 GPU that features 64-PC HBM2 (twice memory bandwidth than U280). Copyright © 2021, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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mVulPreter: A Multi-Granularity Vulnerability Detection system With Interpretations

引用

IEEE Transactions on Dependable and Secure computing 2022年 1-12页

作者： Zou, Deqing Hu, Yutao Li, Wenke Wu, Yueming Zhao, Haojun Jin, Hai 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 Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore 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

Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities at a single granularity (i.e., slice-level or function-level). In practice, slice-level vulnerability detection is fine-grained but may contain incomplete vulnerability details. Function-level vulnerability detection includes full vulnerability semantics but may contain vulnerability-unrelated statements. Meanwhile, they pay more attention to predicting whether the source code is vulnerable and cannot pinpoint which statements are more likely to be vulnerable. In this paper, we design mVulPreter, a multi-granularity vulnerability detector that can provide interpretations of detection results. Specifically, we propose a novel technique to effectively blend the advantages of function-level and slice-level vulnerability detection models and output the detection results' interpretation only by the model itself. We evaluate mVulPreter on a dataset containing 5,310 vulnerable functions and 7,601 non-vulnerable functions. The experimental results indicate that mVulPreter outperforms existing state-of-the-art vulnerability detection approaches (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, StatementLSTM, SySeVR, and Devign). IEEE

关键词： Semantics

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CKG: Dynamic Representation Based on Context and Knowledge Graph

CKG: Dynamic Representation Based on Context and Knowledge G...

引用

International Conference on Pattern Recognition

作者： Xunzhu Tang Tiezhu Sun Rujie Zhu Shi Wang 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 Momenta Suzhou China University of Central Florida Orlando FL USA Institute of Computing Technology Chinese Academy beijing China

Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performance. As a result, they have achieved state-of-the-art results in a large range of language tasks. However, there exists other valuable semantic information such as similar, opposite, or other possible meanings in external knowledge graphs (KGs). We argue that entities in KGs could be used to enhance the correct semantic meaning of language sentences. In this paper, we propose a new method CKG: Dynamic Representation Based on Context and Knowledge Graph. On the one side, CKG can extract rich semantic information of large corpus. On the other side, it can make full use of inside information such as co-occurrence in large corpus and outside information such as similar entities in KGs. We conduct extensive experiments on a wide range of tasks, including QQP, MRPC, SST-5, SQuAD, CoNLL 2003, and SNLI. The experiment results show that CKG achieves SOTA 89.2 on SQuAD compared with SAN (84.4), ELMo (85.8), and BERTBase (88.5).

关键词： Semantics Data mining Task analysis Context modeling

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Temporal Knowledge Graph Reasoning via Time-Distributed Representation Learning

Temporal Knowledge Graph Reasoning via Time-Distributed Repr...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Kangzheng Liu Feng Zhao Guandong Xu Xianzhi Wang Hai Jin 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 Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Temporal knowledge graph (TKG) reasoning has attracted significant attention. Recent approaches for modeling historical information have led to great advances. However, the problems of time variability and unseen entities have become two major obstacles preventing further development. The time variability problem means that different historical timestamps play different roles in the inference process. Furthermore, in the context of time variability, the unseen entity problem means that a query cannot obtain a predicted entity that is unseen in the scale-varying history rather than in a fixed set, thus turning from static to dynamic. In this paper, we propose a novel method named DHU-NET for addressing the time variability challenge and the dynamic unseen entity challenge derived from it. With regard to the former concern, we propose a time-distributed representation learning method based on a graph convolutional network(GCN) and a self-attention mechanism, which learns the distributed representations of facts at different historical timestamps and comprehensively pays different levels of attention to the different timestamps. With regard to the latter issue, we extract the unseen entities from a global static KG based on a copy mechanism and bring them into consideration during the final prediction step. Experiments on six benchmark datasets demonstrate the substantial improvements achieved by DHUNET in terms of multiple evaluation metrics. Our released codes are available at https://***/CGCL-codes/DHUNET.

关键词： Representation learning Measurement Knowledge engineering Predictive models Turning Cognition Data models

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Worm Containment in Peer-to-Peer Networks

Worm Containment in Peer-to-Peer Networks

引用

International Conference on Scalable computing and Communications;International Conference on Embedded computing, SCALCOM-EMBEDDEDCOM

作者： Sirui Yang Hai Jin Bo Li Xiaofei Liao Hong Yao Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Hong Kong University of Science and Technology Hong Kong China

Recently there have been increasing attentions on the security aspects in Peer-to-Peer (P2P) networks, especially with respect to worm epidemics. However, existing worm containment mechanisms can be largely ineffective due to the long delay exhibited in worm identification and patch generation. In this paper, we propose a new worm containment algorithm based on the concept of overlay partition, which can effectively quarantine worms into small and more manageable sub-networks. This algorithm relies on a simple anomaly detection which takes effect much faster than existing schemes. With ldquobridge linksrdquo, the alert of an anomaly can be disseminated faster than random scanning and dedicated worms. Further, in order to deal with false positives, we propose a granularity based clustering algorithm, called CAGA, which, through extensive simulation, is shown to realize better service sustenance during unnecessary partition caused by false positives and can effectively contain fast worms. Finally, we examine the effects of topologies and network dynamics.

关键词： Peer to peer computing Computer worms Partitioning algorithms clustering algorithms Embedded computing grid computing Computer networks Computer science Computer security Delay

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A Web-Based Phylogenetic Analysis for a Query Gene

A Web-Based Phylogenetic Analysis for a Query Gene

引用

International Conference on Bioinformatics and Biomedical Engineering (ICBBE)

作者： Shenglong Tan Song Wu School of Computer Hubei University of Economics Wuhan China Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

We present a robust web server called Phytree of a query gene for phylogenetic analysis from the latest protein database with a user-friendly, interactive graphical user interface. Phytree of a query gene combines a BLAST search with a suite of tools that allows interactive, phylogenetic-oriented exploration of the BLAST results and flexible selection of homologous sequences among the BLAST hits. Once the selection of the BLAST hits is done, the corresponding sequences can be passed to the phylogenetic tree reconstruction pipelines (multiple alignment, data process, phylogenetic reconstruction, tree visualization). As the major technological innovation, selection of a meaningful subset of BLAST hits is implemented using pipeline programming and tree visualization used Java-based recursive method that allows trees to be integrated and viewed seamlessly in standard web browsers with no extra software required. Phytree of a query gene only needs users to copy their amino acid sequence of their query gene to the input box, and the phylogenetic tree will be built once and for all. This function is very practical for non-specialist in phylogeny. Moreover, Phytree of a query gene introduces several new methods in treatment of intermediate results, which makes it much faster, easier and more accurate, robust in phylogenetic tree reconstruction. It is specially designed for users who have no experience in phylogeny. It is freely available on our web page: http://211.69.198.144/***.

关键词： Phylogeny Databases Pipelines Proteins Bioinformatics Visualization Software

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A Novel Application-layer Relay Strategy for Low Delay P2P SIP VoIP system

A Novel Application-layer Relay Strategy for Low Delay P2P S...

引用

International Conference on Ubiquitous Information Technologies and Applications (ICUT)

作者： Hong Yao Hai Jin Siwei Zhou Linchen Yu Xiaofei Liao Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology China University of Geoscience Wuhan China

Development of Internet brings the great increasing communication requirement. This demand impels the developing progress of Voice over Internet Protocol (VoIP). It is difficult to supply the high quality user experience for VoIP services, which has brought great challenges to VoIP developer because of the dynamic property of Internet. We obtain the following results in lots of existing VoIP systems: (1) many relay peer selections are suboptimal; (2) the waiting time to select a relay node can be quite long; (3) there are a large number of unnecessary probes, resulting in heavy network traffic. This paper presents a way called novel application-layer relay strategy (NARS) to solve relay problem and improve scalability. We introduce the mechanism in detail, and test our mechanism in NS2 network simulation environment and get a high relay node hit ratio result, which confirms the feasibility of NARS. Also we make a comparison with the other existing models.

关键词： Relays Delay Peer to peer computing Internet telephony Web and internet services Probes Telecommunication traffic Traffic control Scalability Testing

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