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FlashWalker: An In-Storage Accelerator for Graph Random Walks

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FlashWalker: An In-Storage Accelerator for Graph Random Walk...

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Fuping Niu Jianhui Yue Jiangqiu Shen Xiaofei Liao Haikun Liu 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 Department of Computer Science Michigan Technological University Houghton Michigan USA

Graph random walk is widely used in the graph processing as it is a fundamental component in graph analysis, ranging from vertices ranking to the graph embedding. Different from traditional graph processing workload, random walk features massive processing parallelisms and poor graph data reuse, being limited by low I/O efficiency. Prior designs for random walk mitigate slow I/O operations. However, the state-of-the-art random walk processing systems are bounded by slow disk I/O bandwidth, which is confirmed by our experiments with real-world graphs. To address this issue, we propose FlashWalker, an in-storage accelerator for random walk that moves walk updating close to graph data stored in flash memory, by exploiting significant parallelisms inside SSD. Featuring a heterogeneous and parallel processing system, FlashWalker includes a board-level accelerator, channel-level accelerators, and chip-level accelerators. To address challenges posed by the tight resource constraints for processing large-scale graphs, we propose novel designs: storing a few popular subgraphs in accelerators, the pre-walking for dense walks, two optimizations to search the subgraph mapping table, and a subgraph scheduling algorithm. We implement FlashWalker in RTL, showing small circuit area overhead. Our evaluation shows FlashWalker reduces the execution time of random walk algorithms by up to 660.50×, compared with GraphWalker, which is the state-of-the-art system for random walk algorithms.

关键词： Distributed processing Scheduling algorithms Bandwidth Parallel processing Distance measurement Flash memories Optimization

An Efficient Block Validation Mechanism for UTXO-based Blockchains

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An Efficient Block Validation Mechanism for UTXO-based Block...

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiaohai Dai Bin Xiao Jiang Xiao 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 China Department of Computing The Hong Kong Polytechnic University Hong Kong

It has been recognized that one of the bottlenecks in the UTXO-based blockchain systems is the slow block validation - the process of validating a newly-received block by a node before locally storing it and further broadcasting it. As a block contains multiple inputs, the block validation mainly involves checking the inputs against the status data, which is also known as the Unspent Transaction Outputs (UTXO) set. As time goes by, the UTXO set becomes more and more expansive, most of which can only be stored on disks. This considerably slows down the input checking and thus block validation, which can potentially compromise system security. To deal with the above problem, we disassemble the function of input checking into three parts: existence validation (EV), unspent validation (UV), and script validation (SV). Based on the disassembly, we propose EBV, an efficient block validation mechanism to speed up EV, UV, and SV individually. First, EBV changes the representation of status data, from UTXO set to a bit-vector set, which drastically reduces its size. The smaller status data can be entirely maintained in memory, thereby accelerating UV and also block validation. Second, EBV requires each transaction to carry the proof data, which enables EV and SV without accessing the disks. Furthermore, we also cope with two challenges in the design of EBV, namely transaction inflation and fake positions. To evaluate the EBV mechanism, we implement a prototype on top of Bitcoin, the most widely known UTXO-based blockchain, and conduct extensive experiments to compare EBV and Bitcoin. The experimental results demonstrate that EBV successfully reduces the memory requirement by 93.1 % and the block validation time by up to 93.5%.

关键词： Distributed processing Memory management Prototypes Bitcoin Broadcasting Blockchains Delays

A Hybrid Memory Architecture Supporting Fine-Grained Data Migration

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SSRN

SSRN 2022年

作者： Chi, Ye Yue, Jianhui Liao, Xiaofei Liu, Haikun 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 Wuhan430074 China Computer Science Michigan Technological University HoughtonMI United States

We propose Mocha, a non-hierarchical caching architecture that organizes DRAM and NVM in a flat address space physically, but manages DRAM/NVM in a cache/memory hierarchy in this paper. Since the commercial NVM device-Intel Optane DC Persist Memory Modules (DCPMM) actually access the physical media at a granularity of 256 bytes (an Optane block), we manage the DRAM cache at the 256-byte size to adapt to this device feature of Optane. This design not only enables fine-grained data migration and management for the DRAM cache, but also avoids write amplification for Optane DCPMM. We also create a the \textit{ Indirect Address Cache} (IAC) in \textit{Hybrid Memory Controller}(HMC) and propose a reverse address mapping table in the DRAM to speed up address translation and cache replacement. Besides, we exploit an utility-based caching mechanism to filter cold blocks in the NVM, and further improve DRAM cache effectiveness. We implement Mocha in an architecture simulator. Experimental results indicate that MOCHA can improve performance of applications by 8.2\% on average (up to 24.6\%), provide 6.9\% reduction in average energy consumption and 25.9\% reduction in average data migration traffic, compared to the most advanced hybrid memory architecture-HSCC. © 2022, The Authors. All rights reserved.

关键词： Dynamic random access storage

Significant Engagement Community Search on Temporal Networks: Concepts and Algorithms

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arXiv 2022年

作者： Zhang, Yifei Lin, Longlong Yuan, Pingpeng 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 & Technology HuaZhong University of Science and Technology Luoyu Road 1037 Hubei Wuhan430074 China

Community search, retrieving the cohesive subgraph which contains the query vertex, has been widely touched over the past decades. The existing studies on community search mainly focus on static networks. However, real-world networks usually are temporal networks where each edge is associated with timestamps. The previous methods do not work when handling temporal networks. We study the problem of identifying the significant engagement community to which the user-specified query belongs. Specifically, given an integer k and a query vertex u, then we search for the subgraph H which satisfies (i) u ∈ H;(ii) the de-temporal graph of H is a connected k-core;(iii) In H that u has the maximum engagement level. To address our problem, we first develop a top-down greedy peeling algorithm named TDGP, which iteratively removes the vertices with the maximum temporal degree. To boost the efficiency, we then design a bottom-up local search algorithm named BULS and its enhanced versions BULS+ and BULS*. Lastly, we empirically show the superiority of our proposed solutions on six real-world temporal graphs. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

Cross-Language Binary-Source Code Matching with Intermediate Representations

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arXiv 2022年

作者： Gui, Yi Wan, Yao Zhang, Hongyu Huang, Huifang Sui, Yulei Xu, Guandong Shao, Zhiyuan 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 The University of Newcastle Australia School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China School of Computer Science University of Technology Sydney Australia

Binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment. Currently, several approaches have been proposed for binary-source code matching by jointly learning the embeddings of binary code and source code in a common vector space. Despite much effort, existing approaches target on matching the binary code and source code written in a single programming language. However, in practice, software applications are often written in different programming languages to cater for different requirements and computing platforms. Matching binary and source code across programming languages introduces additional challenges when maintaining multi-language and multi-platform applications. To this end, this paper formulates the problem of cross-language binary-source code matching, and develops a new dataset for this new problem. We present a novel approach XLIR, which is a Transformer-based neural network by learning the intermediate representations for both binary and source code. To validate the effectiveness of XLIR, comprehensive experiments are conducted on two tasks of cross-language binary-source code matching, and cross-language source-source code matching, on top of our curated dataset. Experimental results and analysis show that our proposed XLIR with intermediate representations significantly outperforms other state-of-the-art models in both of the two tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Binary codes

Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

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arXiv 2023年

作者： Liu, Xiaogeng Li, Minghui Wang, Haoyu Hu, Shengshan Ye, Dengpan Jin, Hai Wu, Libing Xiao, Chaowei School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China School of Cyber Science and Engineering Wuhan University China Arizona State University United States

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo)1, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Crime

An effective framework for asynchronous incremental graph processing

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Frontiers of computer science 2019年第3期13卷 539-551页

作者： Xinqiao LV Wei XIAO Yu ZHANG Xiaofei LIAO Hai JIN Qiangsheng HUA Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

Although many graph processing systems have been proposed, graphs in the real-world are often dynamic. It is important to keep the results of graph computation up-todate. Incremental computation is demonstrated to be an efficient solution to update calculated results. Recently, many incremental graph processing systems have been proposed to handle dynamic graphs in an asynchronous way and are able to achieve better performance than those processed in a synchronous way. However, these solutions still suffer from sub-optimal convergence speed due to their slow propagation of important vertex state (important to convergence speed) and poor locality. In order to solve these problems, we propose a novel graph processing framework. It introduces a dynamic partition method to gather the important vertices for high locality, and then uses a priority-based scheduling algorithm to assign them with a higher priority for an effective processing order. By such means, it is able to reduce the number of updates and increase the locality, thereby reducing the convergence time. Experimental results show that our method reduces the number of updates by 30%, and reduces the total execution time by 35%, compared with state-of-the-art systems.

关键词： incremental computation graph processing iterative computation asynchronous convergence

mVulPreter: A Multi-Granularity Vulnerability Detection System With Interpretations

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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

Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs

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arXiv 2022年

作者： Xu, Shaoxian Shao, Zhiyuan Yang, Ci Liao, Xiaofei Jin, Hai The 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 Zhejiang Lab Hangzhou311121 China

The emerging Graph Convolutional Network (GCN) has been widely used in many domains, where it is important to improve the efficiencies of applications by accelerating GCN trainings. Due to the sparsity nature and exploding scales of input real-world graphs, state-of-the-art GCN training systems (e.g., GNNAdvisor) employ graph processing techniques to accelerate the message exchanging (i.e. aggregations) among the graph vertices. Nevertheless, these systems treat both the aggregation stages of forward and backward propagation phases as all-active graph processing procedures that indiscriminately conduct computations on all vertices of an input graph. In this paper, we first point out that in a GCN training problem with a given training set on an input graph, its aggregation stages of backward propagation phases (called as backward aggregations in this paper) can be equivalently converted to partially-active graph processing procedures, which conduct computations on only partial vertices of the input graph. By leveraging such a finding, we propose an execution path preparing method that collects and coalesces the graph data used during different training layers of backward aggregations, and constructs their corresponding sub-graphs (called as execution paths in this paper) as inputs to conduct the backward training on GPUs. Further, we propose a structural-aware strategy for the execution paths to compute their optimal group sizes, so as to gain as high as possible performances on GPUs during the backward aggregations. The experiment results by conducting GCN training in typical real-world graphs show that compared with GNNAdvisor, our approach improves the performance of backward aggregations by up to 5.68x on NVIDIA P100 GPU, and up to 6.57x on NVIDIA V100S GPU. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphics processing unit