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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

Context aware 3d unet for brain tumor segmentation

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arXiv 2020年

作者： Ahmad, Parvez Qamar, Saqib Shen, Linlin Saeed, Adnan 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 Vision Institute School of Computer Science and Software Engineering Shenzhen University China School of Hydropower and Information Technology Huazhong University of Science and Technology Wuhan430074 China

Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contexual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose a modified UNet architecture for brain tumor segmentation. In the proposed architecture, we used densely connected blocks in both encoder and decoder paths to extract multi-contexual information from the concept of feature reusability. The proposed residual inception blocks (RIB) are used to extract local and global information by merging features of different kernel sizes. We validate the proposed architecture on the multimodal brain tumor segmentation challenges (BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancement tumor (ET) are 89.12%, 84.74%, and 79.12%, respectively. Our proposed work is in the top ten methods based on the dice scores of the testing dataset. © 2020, CC-BY.

关键词： Tumors

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

RAHP: A Redundancy-aware Accelerator for High-performance Hypergraph Neural Network

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RAHP: A Redundancy-aware Accelerator for High-performance Hy...

IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Hui Yu Yu Zhang Ligang He Yingqi Zhao Xintao Li Ruida Xin Jin Zhao Xiaofei Liao Haikun Liu Bingsheng He Hai Jin National Engineering Research Center for Big Data Technology and System Service 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 University of Warwick United Kingdom National University of Singapore Singapore

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Hypergraph Neural Network (HyperGNN) has emerged as a potent methodology for dissecting intricate multilateral connections among various entities. Current software/hardware solutions leverage a sequential execution model that relies on hyperedge and vertex indices for conducting standard matrix operations for HyperGNN inference. Yet, they are impeded by the dual challenges of redundant computation and irregular memory access overheads. This is primarily due to the frequent and repetitive access and updating of a number of feature vectors corresponding to the same hyperedges and vertices. To address these challenges, we propose the first redundancy-aware accelerator, RAHP, which enables high performance execution of HyperGNN inference. Specifically, we present a redundancy-aware asynchronous execution approach into the accelerator design for HyperGNN to reduce redundant computations and off-chip memory accesses. To unveil opportunities for data reuse and unlock the parallelism that existing HyperGNN solutions fail to capture, it prioritizes vertices with the highest degree as roots, prefetching other vertices along the hypergraph structure to capture the common vertices among multiple hyperedges, and synchronizing the computations of hyperedges and vertices in real-time. By such means, this facilitates the concurrent processing of relevant hyperedge and vertex computations of the common vertices along the hypergraph topology, resulting in smaller redundant computations overhead. Furthermore, by efficiently caching intermediate results of the common vertices, it curtails memory traffic and off-chip communications. To fully harness the performance potential of our proposed approach in the accelerator, RAHP incorporates a topology-driven data loading mechanism to minimize off-chip memory accesses on the fly. It is also endowed with an adaptive data synchronization scheme to mitigate the effects of conflicting updates of both hyperedges and vertices. Moreover, RAHP emplo

关键词： Computational modeling Neural networks Memory management Loading Energy conservation Vectors Software Topology Synchronization Standards

Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach

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arXiv 2024年

作者： Wan, Yao Wan, Guanghua Zhang, Shijie Zhang, Hongyu Zhou, Pan Jin, Hai Sun, Lichao 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 Huazhong University of Science and Technology China University of Leigh United States

Recent years have witnessed significant progress in developing deep learning-based models for automated code completion. Examples of such models include CodeGPT and StarCoder. These models are typically trained from a large amount of source code collected from open-source communities such as GitHub. Although using source code in GitHub has been a common practice for training deep-learning-based models for code completion, it may induce some legal and ethical issues such as copyright infringement. In this paper, we investigate the legal and ethical issues of current neural code completion models by answering the following question: Is my code used to train your neural code completion model? To this end, we tailor a membership inference approach (termed CodeMI) that was originally crafted for classification tasks to a more challenging task of code completion. In particular, since the target code completion models perform as opaque black boxes, preventing access to their training data and parameters, we opt to train multiple shadow models to mimic their behavior. The acquired posteriors from these shadow models are subsequently employed to train a membership classifier. Subsequently, the membership classifier can be effectively employed to deduce the membership status of a given code sample based on the output of a target code completion model. We comprehensively evaluate the effectiveness of this adapted approach across a diverse array of neural code completion models, (i.e., LSTM-based, CodeGPT, CodeGen, and StarCoder). Experimental results reveal that the LSTM-based and CodeGPT models suffer the membership leakage issue, which can be easily detected by our proposed membership inference approach with an accuracy of 0.842, and 0.730, respectively. Interestingly, our experiments also show that the data membership of current large language models of code, e.g., CodeGen and StarCoder, is difficult to detect, leaving amper space for further improvement. Finally, we also t

关键词： Deep learning

QoS-Aware and Cost-Efficient Dynamic Resource Allocation for Serverless ML Workflows

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QoS-Aware and Cost-Efficient Dynamic Resource Allocation for...

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Hao Wu Junxiao Deng Hao Fan Shadi Ibrahim Song Wu 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 Inria Univ. Rennes CNRS IRISA France

Machine Learning (ML) workflows are increasingly deployed on serverless computing platforms to benefit from their elasticity and fine-grain pricing. Proper resource allocation is crucial to achieve fast and cost-efficient execution of serverless ML workflows (specially for hyperparameter tuning and model training). Unfortunately, existing resource allocation methods are static, treat functions equally, and rely on offline prediction, which limit their efficiency. In this paper, we introduce CE-scaling – a Cost-Efficient autoscaling framework for serverless ML work-flows. During the hyperparameter tuning, CE-scaling partitions resources across stages according to their exact usage to minimize resource waste. Moreover, it incorporates an online prediction method to dynamically adjust resources during model training. We implement and evaluate CE-scaling on AWS Lambda using various ML models. Evaluation results show that compared to state-of-the-art static resource allocation methods, CE-scaling can reduce the job completion time and the monetary cost by up to 63% and 41% for hyperparameter tuning, respectively; and by up to 58% and 38% for model training.

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Economy-Based Data Replication Broker

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Economy-Based Data Replication Broker

IEEE International Conference on e-science and grid computing

作者： Henry Lin J.H. Abawajy Rajkumar Buyya Grid Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Melbourne VIC Australia Grid and Pervasive Computing Laboratory School of Engineering and Information Technology Deakin University Geelong VIC Australia

ISBN: (纸本)9781424430116

Data replication is one of the key components in data grid architecture as it enhances data access and reliability and minimises the cost of data transmission. In this paper, we address the problem of reducing the overheads of the replication mechanisms that drive the data management components of a data grid. We propose an approach that extends the resource broker with policies that factor in user quality of service as well as service costs when replicating and transferring data. A realistic model of the data grid was created to simulate and explore the performance of the proposed policy. The policy displayed an effective means of improving the performance of the grid network traffic and is indicated by the improvement of speed and cost of transfers by brokers.

关键词： Resource management Australia Telecommunication traffic Power generation economics Cost accounting Protocols grid computing Pervasive computing computer science Software

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

SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

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SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

International Conference on Data Engineering

作者： Feng Cheng Jiang Xiao Cunyang Liu Shijie Zhang Yifan Zhou Bo Li Baochun Li 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 Hong Kong University of Science and Technology Hong Kong University of Toronto Canada

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Directed Acyclic Graph (DAG)-based blockchain (a.k.a distributed ledger) has become prevalent for supporting highly concurrent applications. Its inherent parallel data structure accelerates block generation significantly, shifting the bottleneck from performance to storage scalability. An intuitive solution is to apply state sharding that divides the entire ledger (i.e., transactions and states) into multiple shards. While each node only stores proportional transactions, it suffers from the challenges of storing and ensuring the processing consistency of cross-shard transactions. In this paper, we propose SharDAG, a new mechanism that leverages adaptive sharding for DAG-based blockchains to achieve high performance and strong consistency. The key idea of SharDAG is to exploit unique characteristics - silent assets - and design a lightweight processing mechanism based on avatar account caching. Furthermore, we design a Byzantine resilient cross-shard verification mechanism with a theoretically optimal number of participating nodes, which guarantees the consistency and security of avatar account aggregation. Our comprehensive evaluations on real-world workloads demonstrate that SharDAG presents up to 3.8 x throughput improvement compared to the state-of-the-art and reduces the storage overhead of cross-shard transactions.

关键词： Sharding Directed acyclic graph Distributed ledger Avatars Scalability Receivers Throughput